Alternative splicing in cardiovascular disease: a visualized bibliometric analysis of global research trends (1996-2024)

Alternative splicing in cardiovascular disease: a visualized bibliometric analysis of global research trends (1996-2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Alternative splicing in cardiovascular disease: a visualized bibliometric analysis of global research trends (1996-2024) Jianbin Qin, Quanwen Li, Bin Cai, Weijian Wang, Shengyuan Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8654383/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Alternative splicing (AS) is a critical regulatory mechanism in cardiovascular diseases (CVDs). However, a comprehensive bibliometric overview of this rapidly evolving field is lacking. This study aimed to address this gap by providing the first systematic map of the research landscape. Methods Publications on AS and CVDs (1996–2024) were retrieved from the Web of Science Core Collection. Analyses were performed using VOSviewer, CiteSpace, and the R package "bibliometrix" for computational mapping and network visualization, supplemented by Microsoft Excel and Charticulator. Results From 1996 to 2024, 1,712 pertinent publications were identified, involving 9,665 authors from 200 institutions across 66 countries/regions. The United States, China, and Germany led in productivity, with a strong US–China collaborative link. The University of California system was the most productive institution, and Thomas A. Cooper was the leading author. Core journals included the Journal of Biological Chemistry , Gene , and Biochemical and Biophysical Research Communications . Research hotspots centered on RNA-binding proteins, heart failure, dilated cardiomyopathy, cardiac hypertrophy, and oxidative stress. Conclusion This study delivers the first systematic bibliometric assessment of the AS in CVDs field. Future efforts should prioritize elucidating the mechanistic basis of splicing dysregulation and translating these insights into targeted therapies to meet unmet needs in cardiovascular clinical practice. bibliometric analysis alternative splicing cardiovascular diseases VOSviewer CiteSpace Highlights • We construct the first comprehensive map of the alternative splicing (AS) landscape in cardiovascular diseases (CVDs) research from 1996 to 2024. • The intellectual and collaborative architecture is defined by leading contributions from the United States, key institutions, and influential investigators. • Journal co-citation and keyword analyses reveal the foundational knowledge base and evolving focus on splicing regulators in specific pathologies. • Splicing dysregulation in heart failure, cardiomyopathy, and oxidative stress is highlighted as a central pathogenic mechanism. Background Cardiovascular diseases (CVDs) are the leading cause of global mortality and morbidity, imposing a substantial burden on healthcare systems. 1 Their pathogenesis is complex, involving a dynamic interplay between genetic susceptibility and environmental factors. In this context, alternative splicing (AS) has recently emerged as a critical post-transcriptional regulatory mechanism, one that expands proteomic diversity and plays a key role in cardiovascular pathophysiology. 2–4 Over 95% of human multiexon genes undergo alternative splicing (AS). 5 This fundamental mechanism enables a single gene to generate multiple, functionally distinct mRNA isoforms. 6,7 This precise regulation is orchestrated by the interplay between trans -acting factors, such as RNA-binding proteins (RBPs), and specific cis -regulatory elements within the pre-mRNA. 8,9 Within the cardiovascular system, precise spatiotemporal control of AS is indispensable for normal cardiac development, stable electrophysiology, and proper contractile function. 10,11 Consequently, the loss of this splicing fidelity directly contributes to the pathogenesis of diverse CVDs, including heart failure (HF), cardiomyopathies, and atherosclerosis. 12–14 Furthermore, the expression of specific splice variants in genes that control cytoskeletal integrity, calcium handling and energy metabolism serves as a key mechanism linking altered post-transcriptional regulation to disease-related cardiac remodeling and dysfunction. 15,16 Increasing studies on AS in CVDs highlight its important role in disease development. However, this knowledge remains fragmented without a clear, systematic synthesis of the intellectual landscape and core research themes. To bridge this gap, we have performed a bibliometric analysis of publications on AS in CVDs. This quantitative approach enables the objective mapping of research output, collaboration networks, and conceptual trends over time. 17,18 Its specific aims are to delineate the knowledge structure, trace evolutionary shifts in focus, and identify emerging frontiers. This work provides a data-driven, empirical overview of the scientific landscape. It serves as a strategic guide to foster collaboration, prioritize research avenues, and catalyze the translation of fundamental discoveries into new therapies for CVDs. Results Trends in Publication Distribution The initial search identified 1,873 records meeting the inclusion criteria. After the removal of 149 non-original publications (e.g., meeting abstracts) and 12 non-English studies, 1,724 publications (1,453 articles and 271 reviews) were retained for analysis. As shown in Fig. 2 , the annual publication output was low from 1996 to 2004, indicating a nascent stage in the field. A period of steady growth ensued from 2005 to 2020, followed by a sharp surge beginning in 2021, which peaked at 97 publications in 2022, highlighting the rapidly growing research interest in the role of AS in CVDs. Contribution of Countries and Institutions Our analysis encompassed 1,712 publications from 66 countries/regions. The United States (US) was the predominant contributor (815 publications, 47.61%), followed by China (220, 12.85%), Germany (179, 10.46%), the United Kingdom (UK) (148, 8.65%), and Japan (131, 7.65%) (Table 1 ). The US also led to research impact, with a cumulative total of 41,496 citations, more than six times that of the next highest country. Betweenness centrality analysis identified the US (0.58) and the UK (0.36) as the principal hubs within the global collaboration network. This network structure, visualized in the chord diagram (Fig. 3 B), confirms that the US-China partnership is the most frequent international collaboration. Table 1 Top 10 Most Productive Countries in AS in CVDs Research Rank Country Article counts Percentage (%) Total citations ACI Centrality 1 USA 815 47.605 41,496 62.70 0.58 2 CHINA 220 12.850 2,905 14.90 0.01 3 GERMANY 179 10.456 6,770 58.40 0.17 4 ENGLAND 148 8.645 5,435 58.40 0.36 5 JAPAN 131 7.652 2,813 29.30 0.07 6 ITALY 108 6.308 2,701 37.00 0.11 7 CANADA 98 5.724 1,918 28.20 0.05 8 FRANCE 82 4.790 2,843 56.90 0.09 9 NETHERLANDS 65 3.797 2,058 49.00 0.02 10 SPAIN 55 3.213 1079 27.00 0.02 Note: ACI, average number of citations per publication. Institutional productivity and collaboration were analyzed across 200 contributing institutions. The most productive institutions were the University of California System (US, n = 94), Institut National de la Santé et de la Recherche Médicale (France, n = 57), and Harvard University (US, n = 56) (Table 2 ). As measured by the H-index, 21,22 the University of California System (H = 41), Harvard University (H = 34), and Harvard Medical School Affiliates (H = 31) were the most influential. The collaboration network, generated using VOSviewer with a threshold of at least 11 publications per institution, comprised 66 institutions organized into eight major clusters (Fig. 3 C, 3 D), revealing the field's collaborative architecture. Table 2 Top 10 Most Productive Institutions in AS in CVDs Research Rank Institution Country/Region Publication counts Total citations Average citations H-Index 1 University of California System United States 94 5,691 60.54 41 2 Institut national de la santé et de la recherche médicale France 57 3,301 57.91 28 3 Harvard University United States 56 3,999 71.41 34 4 Ohio University United States 53 2,650 50.00 28 5 Harvard Medical School Affiliates United States 48 3,459 72.06 31 6 Baylor College of Medicine United States 45 3,920 87.11 29 7 University of Wisconsin–Madison United States 43 2,243 52.16 26 8 University of Texas System United States 41 3,304 80.59 21 9 Centre national de la recherche scientifique France 40 1,639 40.98 22 10 University of London England 40 1,541 38.53 21 Contribution of journals to publications The 1,712 publications on AS in CVDs were disseminated across 563 journals. Bradford's Law delineated the core journal sources and distributed them into three zones (Fig. 4 A). Zone 1 constituted the nucleus, with 25 core journals (4.4%) contributing to one-third of all the publications. Zones 2 and 3 contained 115 (20.4%) and 423 (75.1%) journals, respectively. As listed in Table 3 , the most prolific journals were Journal of Biological Chemistry (n = 80, IF:3.9), Gene (n = 48, IF:2.4), and Biochemical and Biophysical Research Communications (n = 44, IF:2.2). Notably, four of the top ten most productive journals, including Circulation Research (IF:16.2), hold a Q1 ranking in the Journal Citation Reports (JCR), attesting to their considerable academic influence. Table 3 Top 10 Most Productive Journals in AS in CVDs Research Rank Journal Publications (%) IF (2024) JCR quartile 1 Journal of Biological Chemistry 80 4.673 3.9 Q2 2 Gene 48 2.804 2.4 Q2 3 Biochemical and Biophysical Research Communications 44 2.570 2.2 Q3 4 PLoS One 40 2.336 2.6 Q2 5 Circulation Research 32 1.869 16.2 Q1 6 International Journal of Molecular Sciences 30 1.752 4.9 Q1 7 Journal of Molecular and Cellular Cardiology 29 1.694 4.7 Q1 8 Human Molecular Genetics 21 1.227 3.2 Q2 8 Proceedings of the Indian National Science Academy 21 1.227 2.1 Q3 8 Scientific Reports 21 1.227 3.9 Q1 Co-citation analysis, which assessed the relationships between frequently cited works, revealed a ranking of journal influence that differed considerably from the ranking based on publication volume. The Journal of Biological Chemistry (6,324 co-citations) and Physiological Reviews (5,590) emerged as the most influential journals, surpassing Circulation Research (2,723) and the Journal of Clinical Investigation (2,268) (Table 4 ). This contrast highlights the difference between journals that constitute the field's foundational knowledge base and those that publish the most articles. Table 4 Top 10 Most Productive Co-Cited Journals in AS in CVDs Research Rank Co-cited Journal Total citation Average citation H-Index IF (2024) JCR quartile 1 Journal of Biological Chemistry 6,324 79.05 45 3.9 Q2 2 Physiological Reviews 5,590 1,118 4 28.7 Q1 3 Circulation Research 2,723 85.09 28 16.2 Q1 4 Journal of Clinical Investigation 2,268 226.8 10 13.6 Q1 5 Proceedings of the Indian National Science Academy 2,159 102.81 20 2.1 Q3 6 Nature Genetics 1,872 468 4 29.0 Q1 7 Gene 1,853 38.6 18 2.4 Q2 8 Journal of Molecular and Cellular Cardiology 1,669 57.55 19 4.7 Q1 9 Circulation 1,403 73.84 17 38.6 Q1 10 Molecular and Cellular Biology 1,331 1,331 14 2.7 Q3 The dual-map overlay (Fig. 4 B) illustrates the thematic evolution and intellectual base of the field from 1996 to 2024. In this asymmetric layout, citing journals representing current research fronts are on the left, whereas cited journals representing foundational knowledge are on the right. The color-coded curves depict the flow of intellectual influence from foundational research areas to current research areas. Contribution of authors and co-cited authors From 1996 to 2024, 9,665 authors published research on AS in CVDs. As shown in Table 5 , Thomas A. Cooper (Baylor College of Medicine) led to both publication output (n = 25) and total citations (n = 3,096). He was followed by Jian-Ping Jin (University of Illinois Chicago; n = 21, citations = 1,207) and Andrea N. Ladd (Case Western Reserve University; n = 13, citations = 800). Tuck Wah Soong (National University of Singapore) and Ying Ge (University of Wisconsin-Madison) also ranked high, with 13 publications each. The high citation counts of these researchers reflect their profound influence on the development of the AS in CVDs field. Table 5 Top 12 Most Productive Authors in AS in CVDs Research Rank Author Publications Institutions Total citation Average citation H-index 1 Cooper, Thomas A. 25 Baylor College of Medicine 3,096 123.84 21 2 Jian-Ping Jin 21 University of Illinois Chicago 1,207 57.48 18 3 Ladd, Andrea N. 13 Case Western Reserve University 800 61.54 11 3 Soong, Tuck Wah 13 National University of Singapore 584 44.92 12 3 Ge, Ying 13 University of Wisconsin-Madison 558 42.92 10 6 Esther E. Creemers 12 University of Amsterdam 795 66.25 11 7 Pinto, Yigal M 11 University of Amsterdam 696 63.27 11 8 Liao, Ping 10 National Neuroscience Institute (Singapore) 495 49.5 10 8 Meder, Benjamin 10 University Hospital Heidelberg 281 28.1 9 10 van den Hoogenhof, Maarten M. G. 9 Academic Medical Center (University of Amsterdam) 675 75 8 10 Beqqali, Abdelaziz 9 Academic Medical Center (University of Amsterdam) 640 71.11 8 10 Medina, Marisa Wong 9 Children's Hospital Oakland Research Institute 377 41.89 9 A co-authorship network analysis revealed collaboration among 258 authors, each of whom had at least three publications. The network was divided into six major clusters forming distinct research communities (Fig. 5 A). Temporal network analysis further elucidated the dynamics of these collaborations (Fig. 5 B), revealing both the stability and concurrent activity of the primary research teams. High-cited publications and co-cited references Table 6 lists the 10 most-cited publications within AS in CVDs research, all of which exceed 570 citations. Among these, the review " Dopamine receptors: From structure to function " 23 (2,763 citations) stands as a seminal work. Co-citation analysis of 74,622 references revealed " RBM20, a gene for hereditary cardiomyopathy, regulates titin splicing " 24 (100 co-citations) as the most central publication (Table 7 ). It plays a pivotal role by demonstrating that RBM20 deficiency drives pathological splicing, leading to sarcomere impairment, aberrant calcium handling, and dilated cardiomyopathy. Table 6 Top 10 Most-Cited Articles in AS in CVDs Research Rank Title Citation Author Document type Journal Year IF (2024) 1 Dopamine receptors: From structure to function 2,736 C Missale Review Physiological Reviews 1998 28.7 2 The calpain system 2,309 Darrell E Goll Review Physiological Reviews 2003 28.7 3 Mutations in the gene encoding lamin A/C cause autosomal dominant Emery-Dreifuss muscular dystrophy 1015 G Bonne Article Nature Genetics 1999 29.0 4 Structural and functional diversity of connexin genes in the mouse and human genome 956 Klaus Willecke Review Biological Chemistry 2002 2.4 5 Prostanoid receptors: Subtypes and signaling 808 R M Breyer Article Annual Review of Pharmacology and Toxicology 2001 13.1 6 Isoform 1c of sterol regulatory element binding protein is less active than isoform 1a in livers of transgenic mice and in cultured cells 693 H Shimano Article Journal of Clinical Investigation 1997 13.6 7 Structure of von Willebrand factor-cleaving protease (ADAMTS13), a metalloprotease involved in thrombotic thrombocytopenic purpura 664 Xinglong Zheng Article Journal of Biological Chemistry 2001 3.9 8 Disruption of splicing regulated by a CUG-binding protein in myotonic dystrophy 663 A V Philips Article Science 1998 45.8 9 Differential expression of exons 1a and 1c in mRNAs for sterol regulatory element binding protein-1 in human and mouse organs and cultured cells 632 I Shimomura Article Journal of Clinical Investigation 1997 13.6 10 A novel X-linked gene, G4.5. is responsible for Barth syndrome 575 S Bione Article Nature Genetics 1996 29.0 Table 7 Top 10 Most Co-Cited References in AS in CVDs Research Rank Title Citation TLS Author Document type Journal Year 1 RBM20, a gene for hereditary cardiomyopathy, regulates titin splicing 100 2503 Wei Guo Article Nature Medicine 2012 2 Alternative isoform regulation in human tissue transcriptomes 99 1785 Eric T Wang Article Nature 2008 3 A postnatal switch of CELF and MBNL proteins reprograms alternative splicing in the developing heart 82 2115 Auinash Kalsotra Article PNAS 2008 4 Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing 71 1294 Qun Pan Article Nature Genetics 2008 5 Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction 64 182 P Chomczynski Article Analytical Biochemistry 1987 6 ASF/SF2-regulated CaMKIIdelta alternative splicing temporally reprograms excitation-contraction coupling in cardiac muscle 64 1285 Xiangdong Xu Article Cell 2005 7 Mutations in ribonucleic acid binding protein gene cause familial dilated cardiomyopathy 53 1460 Katharine M Brauch Article Journal of the American College of Cardiology 2009 8 RNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing 53 1422 Henrike Maatz Article The Journal of clinical investigation 2014 9 Mechanisms of alternative pre-messenger RNA splicing 48 576 Douglas L Black Review Annual review of biochemistry 2003 10 Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 48 583 Michael I Love Article Genome biology 2014 Citation burst analysis, which identifies publications with sharp increases in citation rates, tracks the evolving research trends. Figure 6 B shows the top 25 references with the strongest citation bursts from 1996 to 2024, including their strength and duration. This highlights publications pivotal to advancing new research frontiers. Analysis of Keywords and Keywords Burst The research landscape and its thematic structure were mapped through keyword co-occurrence and cluster analysis. High-frequency keywords such as "RNA-binding proteins" (n = 38), "heart failure" (n = 35), and "gene expression" (n = 34) represent core research interests (Table 8 ). Thematic network visualization achieved through VOSviewer and CiteSpace revealed distinct research clusters, each color-coded for clarity. Specifically, the resulting analyses included a keyword cluster density map (Fig. 7 A), co-occurrence network (Fig. 7 B), and clustered keyword maps (Fig. 7 C), which collectively delineated the conceptual architecture of the field. Table 8 Top 23 Keywords in AS in CVDs Research Rank Keywords Count Centrality Year 1 alternative splicing 462 0.7 1996 2 RNA-binding proteins 38 0.04 2013 3 Heart failure 35 0.05 1996 4 Gene expression 34 0.05 1996 5 Cardiovascular diseases 27 0.02 2001 6 Dilated cardiomyopathy 19 0.02 2003 7 Cardiac hypertrophy 14 0.03 2002 8 Cardiac muscle 13 0.03 1996 9 Oxidative stress 11 0.01 2007 10 Tissue factor 9 0 2006 10 Skeletal muscle 9 0 2002 12 Calcium channel 8 0 1996 12 Splice variant 8 0 2005 14 Signal transduction 7 0.01 1999 14 Alternative RNA splicing 7 0.02 1998 14 Calcium channels 7 0.01 1997 17 Cerebral ischemia 6 0.01 2001 17 Cardiac function 6 0 2010 19 Alternative polyadenylation 5 0.01 2012 19 Alzheimers disease 5 0 2005 19 Calcium signaling 5 0.01 2000 19 Blood pressure 5 0 2006 19 Mass spectrometry 5 0.01 2020 Additional files The original documents downloaded from the Web of Science Core Collection contain all 1,712 publications for the analysis. The evolution of research priorities was traced using timeline visualization and burst detection methods. A timeline cluster view (Fig. 7 D) was generated using the log-likelihood ratio (LLR) method. This illustrates the developmental trajectory of major research themes. Concurrently, keyword burst analysis identified terms that attracted strong but brief scholarly attention (Fig. 7 E). In Fig. 7 E, the blue line represents the entire timeline (1996–2024), while the red segments denote periods of citation bursts, capturing both sustained and transient research trends. Collectively, the findings outline a comprehensive and evolving landscape of the field and identify key emerging frontiers that shape its future trajectory. Discussion General information We used WoSCC data to construct a systematic map of AS research in CVDs from 1996 to 2024. The intellectual landscape is geographically concentrated. The US leads in productivity, scientific impact, and collaborative networks. This leadership is supported by foundational contributions from key institutions such as the University of California System. Core knowledge is spread through influential journals, including Circulation Research , the Journal of Biological Chemistry , and PLoS One . Among individual researchers, Thomas A. Cooper stands out as a pivotal figure, showing exceptional leadership in productivity, citations, and H-index metrics. Collectively, our findings outline the intellectual and social structure of the AS in CVDs field, providing a basic roadmap to guide future research directions. Hotspots and future perspectives We synthesize recent key literature on these topics by analyzing keywords and timelines, which identify several research hotspots. RNA-binding proteins RNA-binding proteins (RBPs) are the master regulators of RNA metabolism. They control key steps in pre-mRNA processing, including AS and translational control, which are essential for maintaining cellular homeostasis. 25 The foundational era of AS research (1970s–1990s) has systematically elucidated the mechanistic principles of splicing variants. 26 It also established its role within a multi-tiered gene regulatory network that integrates chromatin accessibility, transcription, and proteostasis. 27 In the cardiovascular system, RBPs directly govern the cell fate and phenotype. For instance, Quaking and HuR induce disease-related phenotypic switching in vascular smooth muscle and endothelial cells, thereby promoting inflammatory activation and barrier dysfunction in atherosclerosis. 28 Under hemodynamic stress, RBPs further promote leukocyte recruitment by modulating the splicing and expression of adhesion molecules, including ICAM-1 and VCAM-1. 29 Dysfunctional RBPs play a critical role in the development of dilated cardiomyopathy (DCM) and arrhythmia. Notably, RBM20 deficiency induces pathological titin splicing, leading to sarcomere impairment and DCM. 30 Similarly, the loss of RBPMS, a regulator of structural gene splicing, results in severe cardiomyopathy. 31 Collectively, these findings demonstrate that RBPs are key drivers of cardiovascular pathogenesis and serve as promising targets for molecular therapy. Heart Failure Splicing dysregulation is a central pathogenic mechanism in HF. This process is directed by RBPs, including RBM20, MBNL1, and CELF1, which orchestrate tissue-specific AS of critical cardiac genes such as TTN , MYH7 , and SCN5A . 32,33 For example, RBM20 mutations induce aberrant TTN splicing, which leads to familial DCM. 34 Beyond these well-characterized RBPs, alterations in other splicing factors, such as RBM5, ZRANB2, and HNRNPF, also contribute to maladaptive AS events in HF. 14 Recent advances have revealed the translational potential of splicing error correction. These include restoring RBFox1 expression to ameliorate pathological cardiac remodeling via protective MEF2 isoforms, 35 inhibiting Dyrk1A to normalize pathological CaMKIIδ splicing, improving post-infarction cardiac function, 36 and identifying DDX5 as a key RNA helicase that maintains calcium homeostasis by repressing the aberrant CaMKIIδA isoform. 37 Additionally, Trdn-as regulates triadin splicing to preserve calcium handling. 38 Together, these findings delineate multiple splicing-dependent pathways in HF and validate their therapeutic relevance. They also pave the way for splicing-corrective strategies, such as small molecules, antisense oligonucleotides (ASOs), and RNA-targeted gene therapies. Dilated Cardiomyopathy Driven by defects in AS, DCM manifests as ventricular dilation and systolic dysfunction, and typically progresses to HF. In particular, RBM20 mutations disrupt the expression of TTN and CAMK2D isoforms, leading to sarcomere dysfunction and electromechanical remodeling. 24,39 Although RBM20 variants increase atrial fibrillation risk, they show no significant association with overall survival or transplant outcomes. 40 This finding suggests that mutation carriers may experience distinct pathophysiological trajectories. Splicing dysregulation in DCM involves other key regulators. For instance, RBM24 ablation disrupts Z-disc and M-band integrity because it causes aberrant splicing of structural proteins. 41 In contrast, upregulated SLM2 expression fine-tunes the splicing of sarcomeric transcripts, such as MYL2 and TTN , thereby preserving cardiomyocyte architecture. 42 Additionally, a lack of lncRNA DCRT causes mitochondrial impairment by triggering PTBP1-mediated missplicing of NDUFS2 . 43 Taken together, these findings delineate a complex, multi-factorial regulatory network in DCM and pinpoint promising therapeutic targets. Cardiac Hypertrophy Pathological splicing reprogramming drives extensive remodeling underlying cardiac hypertrophy. 44 In the initial phase, pressure overload induces upregulation of PTB/ESRP1, which stabilizes pro-hypertrophic mRNAs through alternative polyadenylation at the 3'-UTR. 45 Conversely, reduced RBM10-Star-PAP activity diminishes anti-hypertrophic gene expression, disrupting the balance of growth signals. 46 During disease progression, RIPK3 deficiency modulates the splicing regulators ASF/SF2 and SC-35 to promote pathological CaMKIIδ splicing. 47 Furthermore, PP1γ directly promotes production of the pro-hypertrophic CaMKIIδC isoform. 48 These events converge to impair calcium handling, which is further worsened by Ca V 1.2 channel splicing defects 49 and SRSF9-mediated progression of the hypertrophic phenotype. 50 Collectively, studies have established that the regulatory mechanisms of cardiac hypertrophy exhibit significant spatial heterogeneity, a feature that drives a spectrum of region-specific adaptive and dysfunctional outcomes. Oxidative Stress Oxidative stress and AS are key pathogenic mechanisms in CVDs. Mitochondria-derived oxidative stress causes direct cellular damage and induces widespread splicing alterations through multiple mechanisms. 51 For instance, oxidative stress triggers the pathological splicing of VEGF-A and QKI in diabetic vasculopathy, which disrupts endothelial homeostasis. 52 Oxidative stress further promotes arrhythmogenesis through oxidative modification of CaMKIIδ, which generates a persistent pathological signal. 53 In addition, transcriptional cross-talk between Foxp1 and NLRP3 regulates oxidative stress responses, thereby increasing the complexity of pathogenic mechanisms. 54 The discovery that GATA4 binds spliceosomal components reveals a novel link between oxidative stress and splicing. 55 By targeting this integrated response, oligonucleotide therapies thus represent a promising intervention for ischemia-reperfusion injury, which is now under clinical evaluation in CVDs. 56 Future Trends Moving forward, bridging AS research and cardiovascular clinical practice will require synergistic advances in high-resolution molecular diagnostics and mechanism-based targeted therapies. 57 The emerging atlas of human cardiac isoforms, mapped via long-read single-cell RNA sequencing (scRNA-seq), revealed that isoform switching in genes such as TTN and MYH7 is a hallmark of HF. 58,59 Increasing evidence highlights the need to develop isoform-specific diagnostic tools. Concurrently, AI models trained on genomic and epigenetic data predict splicing regulators and decipher the cis-regulatory codes of pathological AS, thereby unveiling new druggable targets. 60,61 To target these nodes, ASO therapeutics have been advanced through chemical redesign and guanidine-based lipid nanoparticles (LNPs). 62,63 These LNPs enhance extrahepatic mRNA delivery and target immune cells, providing a critical advantage for modulating splicing in cardiac immune-stromal cells. 64 By targeting splice variants like CD73, the sonogenetic ASO nanoplatform strategy modulates cellular metabolism and immunity, showing promise for application against cardiovascular inflammation and fibrosis. 65 The convergence of single-cell multiomics, rational oligonucleotide design, and cell-selective delivery is ushering in a transformative era for personalized splicing modulation, which is poised to redefine therapeutic paradigms across a range of CVDs. Limitations This study provides the first systematic bibliometric mapping of AS research in CVDs and establishes an objective framework to evaluate this rapidly evolving field. Through a quantitative analysis of large-scale research literature, we identified foundational publications, collaborative networks, and emerging research frontiers. This study had several limitations. First, our analysis relied exclusively on English-language publications from WoSCC, which may introduce selection bias, while the database's coverage of high-impact journals supports the robustness of the identified trends. Second, the study period ends in December 2024, establishing a defined historical baseline; however, it excludes subsequent developments. Relatedly, citation metrics are subject to temporal bias, inherently favoring older established works. Finally, lags in database indexing can cause variability in the number of publications in recent years. These limitations are inherent to bibliometric research and do not undermine the core conclusions about the field's development and organization. Consequently, this analysis establishes a robust foundation for the future tracking of AS research in CVDs. Conclusion In conclusion, AS dysregulation is now recognized as a central mechanism in CVDs. It holds dual importance for both diagnostic discovery and therapeutic intervention. Advanced technologies enable high-resolution mapping of splicing landscapes, and novel agents (e.g., octaguanidine-conjugated ASOs, inducible CRISPR/dCas13 systems) offer enhanced precision. However, full clinical translation of this knowledge requires a deeper understanding of cell type-specific mechanisms. To realize this potential, the field must focus on identifying key splicing regulators and driver events that support targeted diagnostics and therapies. Materials and Methods Data source and literature search strategy We selected the Web of Science Core Collection (WoSCC) for this bibliometric analysis based on its comprehensive coverage of high-impact journals and established role in scholarly mapping. 19 To ensure data consistency and avoid potential biases from daily database updates, all data were retrieved on April 11 2025. The search strategy was refined in consultation with a specialized medical librarian and through a review of pivotal literature in the field. 20 The final Boolean search query employed was: TS= (alternative splicing) AND TS = (“high blood pressure” or hypertensi* or “peripheral arter*” disease* or “atrial fibrillat*” or tachycardi* or endocardi* or pericard* or ischem* or arrhythmi* or thrombo* or cardio* or cardiac* or “heart failure” or “heart beat” or “heart rate*” or “heart val*” or coronary* or angina* or ventric* or myocard* or “hyperlipid∗” or “hypercholesterol∗” or “hypercholester∗” or “hypertriglycerides∗” or “cholesterol*” or “congenital heart” or “heart defect*” or “congenital heart defect” or “heart attack”). Inclusion and exclusion criteria The literature search encompassed publications from January 1, 1996, to December 31, 2024. To minimize potential analytical biases from language barriers, we restricted the inclusion to English-language publications. This study included only original articles and reviews as the primary sources of established, peer-reviewed knowledge in this field. To minimize subjective bias, two investigators (Jianbin Qin and Quanwen Li) independently performed screening: first, based on titles and abstracts, and then via full-text assessment of potentially eligible records. Any discrepancies in eligibility were resolved through a consensus discussion or, when necessary, adjudicated by a senior researcher (Quanzhong Li). Figure 1 illustrates the screening workflow and the inclusion criteria. Bibliometric analysis We performed a bibliometric analysis and scientific mapping using a suite of established tools. We employed the following software for the specific tasks: VOSviewer (version 1.6.20) was used to construct and visualize the co-authorship, co-institution, and co-occurrence networks. CiteSpace (version 6.4.R1) to detect emerging trends and conduct citation burst analysis. The R package "bibliometrix" (version 4.3.5) was used for comprehensive data ingestion, preprocessing, and descriptive statistical analysis. Additionally, we used Microsoft Excel (2021) for the initial data curation and Microsoft Charticulator to generate customized, publication-ready figures beyond the output of the primary bibliometric tools. Abbreviations AS alternative splicing ASOs antisense oligonucleotides CVDs cardiovascular diseases DCM dilated cardiomyopathy HF heart failure LNPs lipid nanoparticles RBPs RNA- binding proteins scRNA-seq single-cell RNA sequencing UK United Kingdom US United States WoSCC Web of Science Core Collection. Declarations Acknowledgements Not applicable. Authors' contributions Jianbin Qin, Quanwen Li, and Bin Cai designed the study, collected the data, and drafted the initial manuscript. Weijian Wang conducted the statistical analysis and assisted in data interpretation. Shengyuan Lin critically revised the manuscript and participated in data interpretation. Shengjun Xiao supervised the project administration and contributed to the final review and approval of the manuscript. Quanzhong Li provided scientific supervision and oversaw the entire study. All authors reviewed and approved the final manuscript prior to submission. Funding None. Data Availability All data generated during this study are included in this published article. The analysis during the study can be obtained from the corresponding author Quanzhong Li on reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: Update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982-3021. doi: 10.1016/j.jacc.2020.11.010. 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RNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing. J Clin Investig. 2014;124(8):3419-3430. doi: 10.1172/JCI74523. Refaat MM, Lubitz SA, Makino S, et al. Genetic variation in the alternative splicing regulator RBM20 is associated with dilated cardiomyopathy. Heart Rhythm. 2012;9(3):390-396. doi: 10.1016/j.hrthm.2011.10.016. Liu J, Kong X, Zhang M, Yang X, Xu X. RNA binding protein 24 deletion disrupts global alternative splicing and causes dilated cardiomyopathy. Protein Cell. 2019;10(6):405-416. doi: 10.1007/s13238-018-0578-8. Boeckel JN, Möbius-Winkler M, Müller M, et al. SLM2 is A novel cardiac splicing factor involved in heart failure due to dilated cardiomyopathy. Genomics Proteomics Bioinformatics. 2022;20(1):129-146. doi: 10.1016/j.gpb.2021.01.006. Du H, Zhao Y, Wen J, et al. LncRNA DCRT protects against dilated cardiomyopathy by preventing NDUFS2 alternative splicing by binding to PTBP1. Circulation. 2024;150(13):1030-1049. doi: 10.1161/CIRCULATIONAHA.123.067861. Yamazaki T, Wälchli S, Fujita T, et al. Splice variants of enigma homolog, differentially expressed during heart development, promote or prevent hypertrophy. Cardiovasc Res. 2010;86(3):374-382. doi: 10.1093/cvr/cvq023. Kim T, Kim JO, Oh JG, Hong SE, Kim DH. Pressure-overload cardiac hypertrophy is associated with distinct alternative splicing due to altered expression of splicing factors. Mol Cells. 2014;37(1):81-87. doi: 10.14348/molcells.2014.2337. Mohan N, Kumar V, Kandala DT, Kartha CC, Laishram RS. A splicing-independent function of RBM10 controls specific 3′ UTR processing to regulate cardiac hypertrophy. Cell Rep. 2018;24(13):3539-3553. doi: 10.1016/j.celrep.2018.08.077. Qian J, Zhang J, Cao J, Wang X, Zhang W, Chen X. The regulatory effect of receptor-interacting protein kinase 3 on CaMKIIδ in TAC-induced myocardial hypertrophy. Int J Mol Sci. 2023;24(19):14529. doi: 10.3390/ijms241914529. Liao RJ, Tong LJ, Huang C, et al. Rescue of cardiac failing and remodelling by inhibition of protein phosphatase 1γ is associated with suppression of the alternative splicing factor-mediated splicing of Ca2+/calmodulin-dependent protein kinase δ. Clin Exp Pharmacol Physiol. 2014;41(12):976-985. doi: 10.1111/1440-1681.12308. Hu Z, Liang MC, Soong TW. Alternative Splicing of L-type Ca V 1.2 Calcium Channels: Implications in Cardiovascular Diseases. Genes (Basel). 2017 Nov 24;8(12):344. doi: 10.3390/genes8120344. Yu S, Sun Z, Ju T, et al. The m7G methyltransferase Mettl1 drives cardiac hypertrophy by regulating SRSF9-mediated splicing of NFATc4. Adv Sci (Weinh). 2024;11(29):e2308769. doi: 10.1002/advs.202308769. Dubois-Deruy E, Peugnet V, Turkieh A, Pinet F. Oxidative stress in cardiovascular diseases. Antioxidants (Basel, Switzerland). 2020;9(9):864. doi: 10.3390/antiox9090864. Cornelius VA, Fulton JR, Margariti A. Alternative splicing: A key mediator of diabetic vasculopathy. Genes. 2021;12(9):1332. doi: 10.3390/genes12091332. Duran J, Nickel L, Estrada M, Backs J, van den Hoogenhof MMG. CaMKIIδ splice variants in the healthy and diseased heart. Front Cell Dev Biol. 2021;9:644630. doi: 10.3389/fcell.2021.644630. Liu XM, Du SL, Miao R, Wang LF, Zhong JC. Targeting the forkhead box protein P1 pathway as a novel therapeutic approach for cardiovascular diseases. Heart Fail Rev. 2022;27(1):345-355. doi: 10.1007/s10741-020-09992-2. Zhu L, Choudhary K, Gonzalez-Teran B, et al. Transcription factor GATA4 regulates cell type-specific splicing through direct interaction with RNA in human induced pluripotent stem cell-derived cardiac progenitors. Circulation. 2022;146(10):770-787. doi: 10.1161/CIRCULATIONAHA.121.057620. Dery KJ, Wong Z, Wei M, Kupiec-Weglinski JW. Mechanistic insights into alternative gene splicing in oxidative stress and tissue injury. Antioxid Redox Signal. 2024;41(13-15):890-909. doi: 10.1089/ars.2023.0437. Jiang J, Wu H, Ji Y, et al. Development and disease-specific regulation of RNA splicing in cardiovascular system. Front Cell Dev Biol. 2024;12:1423553. doi: 10.3389/fcell.2024.1423553. Pan T, Lu L, Youker K, et al. Single-cell splicing isoform atlas of the adult human heart and heart failure. Circulation. 2025;152(21):1501-1514. doi: 10.1161/CIRCULATIONAHA.125.074959. Wu H, Lu Y, Duan Z, et al. Nanopore long-read RNA sequencing reveals functional alternative splicing variants in human vascular smooth muscle cells. Commun Biol. 2023;6(1):1104. doi: 10.1038/s42003-023-05481-y. Kimata K, Satou K. Improved CRISPR/Cas9 off-target prediction with DNABERT and epigenetic features. PLOS One. 2025;20(11):e0335863. doi: 10.1371/journal.pone.0335863. Cao J, Wei Z, Nie Y, Chen HZ. Therapeutic potential of alternative splicing in cardiovascular diseases. EBiomedicine. 2024;101:104995. doi: 10.1016/j.ebiom.2024.104995. Zhao C, Li X, He Z, Ye C, Chen F, Cheng J. PEG-ASO conjugates for efficient targeted delivery and migration inhibition in Cancer cell. Bioorg Med Chem Lett. 2025;122:130208. doi: 10.1016/j.bmcl.2025.130208. Zhang H, Liu D, Yang K, Liang Z, Li M. Ionizable guanidine-based lipid nanoparticle for targeted mRNA delivery and cancer immunotherapy. Sci Adv. 2025;11(43):eadx5970. doi: 10.1126/sciadv.adx5970. Yu H, Dyett BP, Drummond CJ, Zhai J. Ionizable lipid nanoparticles for mRNA delivery: Internal self-assembled inverse mesophase structure and endosomal escape. Acc Chem Res. 2025;58(20):3210-3222. doi: 10.1021/acs.accounts.5c00522. Xiong B, Yu J, Wen C, et al. Antisense oligonucleotide-loaded nanozyme reverses tumor immune suppression through sonogenetic metabolic therapy. J Control Release. 2025;387:114236. doi: 10.1016/j.jconrel.2025.114236. Figures Figures are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Additionalfiles.zip Additional files The original documents downloaded from the Web of Science Core Collection contain all 1,712 publications for the analysis. 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02:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8654383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8654383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397725,"identity":"b34eaf1d-92f2-42d7-9cbe-b821fef3547f","added_by":"auto","created_at":"2026-03-11 11:55:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414930,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8654383/v1/60f60e45-07e4-491f-97a6-14a2030ff2e0.pdf"},{"id":103528836,"identity":"17e720b0-f8d7-4c6f-8d94-2fd8b88c71af","added_by":"auto","created_at":"2026-02-26 16:37:30","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4955614,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional files\u003c/p\u003e\n\u003cp\u003eThe original documents downloaded from the Web of Science Core Collection contain all 1,712 publications for the analysis.\u003c/p\u003e","description":"","filename":"Additionalfiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-8654383/v1/179ce98a1803a778323e6c3d.zip"},{"id":103528847,"identity":"70221ba5-35f8-4743-b8b9-e08bb2e08bf4","added_by":"auto","created_at":"2026-02-26 16:37:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14515,"visible":true,"origin":"","legend":"","description":"","filename":"Figureslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-8654383/v1/510804bfdca2c18cba2187c2.docx"},{"id":103528846,"identity":"e978e60d-fc70-4214-b3a6-0b00e69fd925","added_by":"auto","created_at":"2026-02-26 16:37:31","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":43949777,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8654383/v1/391d44f316e8b221ce35e2fc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Alternative splicing in cardiovascular disease: a visualized bibliometric analysis of global research trends (1996-2024)","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; We construct the first comprehensive map of the alternative splicing (AS) landscape in cardiovascular diseases (CVDs) research from 1996 to 2024.\u003c/p\u003e\u003cp\u003e\u0026bull; The intellectual and collaborative architecture is defined by leading contributions from the United States, key institutions, and influential investigators.\u003c/p\u003e\u003cp\u003e\u0026bull; Journal co-citation and keyword analyses reveal the foundational knowledge base and evolving focus on splicing regulators in specific pathologies.\u003c/p\u003e\u003cp\u003e\u0026bull; Splicing dysregulation in heart failure, cardiomyopathy, and oxidative stress is highlighted as a central pathogenic mechanism.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eCardiovascular diseases (CVDs) are the leading cause of global mortality and morbidity, imposing a substantial burden on healthcare systems.\u003csup\u003e1\u003c/sup\u003e Their pathogenesis is complex, involving a dynamic interplay between genetic susceptibility and environmental factors. In this context, alternative splicing (AS) has recently emerged as a critical post-transcriptional regulatory mechanism, one that expands proteomic diversity and plays a key role in cardiovascular pathophysiology.\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOver 95% of human multiexon genes undergo alternative splicing (AS).\u003csup\u003e5\u003c/sup\u003e This fundamental mechanism enables a single gene to generate multiple, functionally distinct mRNA isoforms.\u003csup\u003e6,7\u003c/sup\u003e This precise regulation is orchestrated by the interplay between \u003cem\u003etrans\u003c/em\u003e-acting factors, such as RNA-binding proteins (RBPs), and specific \u003cem\u003ecis\u003c/em\u003e-regulatory elements within the pre-mRNA.\u003csup\u003e8,9\u003c/sup\u003e Within the cardiovascular system, precise spatiotemporal control of AS is indispensable for normal cardiac development, stable electrophysiology, and proper contractile function.\u003csup\u003e10,11\u003c/sup\u003e Consequently, the loss of this splicing fidelity directly contributes to the pathogenesis of diverse CVDs, including heart failure (HF), cardiomyopathies, and atherosclerosis.\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e Furthermore, the expression of specific splice variants in genes that control cytoskeletal integrity, calcium handling and energy metabolism serves as a key mechanism linking altered post-transcriptional regulation to disease-related cardiac remodeling and dysfunction.\u003csup\u003e15,16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIncreasing studies on AS in CVDs highlight its important role in disease development. However, this knowledge remains fragmented without a clear, systematic synthesis of the intellectual landscape and core research themes. To bridge this gap, we have performed a bibliometric analysis of publications on AS in CVDs. This quantitative approach enables the objective mapping of research output, collaboration networks, and conceptual trends over time.\u003csup\u003e17,18\u003c/sup\u003e Its specific aims are to delineate the knowledge structure, trace evolutionary shifts in focus, and identify emerging frontiers.\u003c/p\u003e \u003cp\u003eThis work provides a data-driven, empirical overview of the scientific landscape. It serves as a strategic guide to foster collaboration, prioritize research avenues, and catalyze the translation of fundamental discoveries into new therapies for CVDs.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTrends in Publication Distribution\u003c/h2\u003e \u003cp\u003eThe initial search identified 1,873 records meeting the inclusion criteria. After the removal of 149 non-original publications (e.g., meeting abstracts) and 12 non-English studies, 1,724 publications (1,453 articles and 271 reviews) were retained for analysis.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the annual publication output was low from 1996 to 2004, indicating a nascent stage in the field. A period of steady growth ensued from 2005 to 2020, followed by a sharp surge beginning in 2021, which peaked at 97 publications in 2022, highlighting the rapidly growing research interest in the role of AS in CVDs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContribution of Countries and Institutions\u003c/h3\u003e\n\u003cp\u003eOur analysis encompassed 1,712 publications from 66 countries/regions. The United States (US) was the predominant contributor (815 publications, 47.61%), followed by China (220, 12.85%), Germany (179, 10.46%), the United Kingdom (UK) (148, 8.65%), and Japan (131, 7.65%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The US also led to research impact, with a cumulative total of 41,496 citations, more than six times that of the next highest country. Betweenness centrality analysis identified the US (0.58) and the UK (0.36) as the principal hubs within the global collaboration network. This network structure, visualized in the chord diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), confirms that the US-China partnership is the most frequent international collaboration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Most Productive Countries in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArticle counts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCentrality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41,496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGERMANY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENGLAND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAPAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITALY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCANADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNETHERLANDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPAIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ACI, average number of citations per publication.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInstitutional productivity and collaboration were analyzed across 200 contributing institutions. The most productive institutions were the University of California System (US, n\u0026thinsp;=\u0026thinsp;94), Institut National de la Sant\u0026eacute; et de la Recherche M\u0026eacute;dicale (France, n\u0026thinsp;=\u0026thinsp;57), and Harvard University (US, n\u0026thinsp;=\u0026thinsp;56) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As measured by the H-index,\u003csup\u003e21,22\u003c/sup\u003e the University of California System (H\u0026thinsp;=\u0026thinsp;41), Harvard University (H\u0026thinsp;=\u0026thinsp;34), and Harvard Medical School Affiliates (H\u0026thinsp;=\u0026thinsp;31) were the most influential. The collaboration network, generated using VOSviewer with a threshold of at least 11 publications per institution, comprised 66 institutions organized into eight major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), revealing the field's collaborative architecture.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Most Productive Institutions in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry/Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublication counts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH-Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of California System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitut national de la sant\u0026eacute; et de la recherche m\u0026eacute;dicale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarvard University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOhio University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarvard Medical School Affiliates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaylor College of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Wisconsin\u0026ndash;Madison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Texas System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentre national de la recherche scientifique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of London\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEngland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eContribution of journals to publications\u003c/h3\u003e\n\u003cp\u003eThe 1,712 publications on AS in CVDs were disseminated across 563 journals. Bradford's Law delineated the core journal sources and distributed them into three zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Zone 1 constituted the nucleus, with 25 core journals (4.4%) contributing to one-third of all the publications. Zones 2 and 3 contained 115 (20.4%) and 423 (75.1%) journals, respectively. As listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the most prolific journals were \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;80, IF:3.9), \u003cem\u003eGene\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;48, IF:2.4), and \u003cem\u003eBiochemical and Biophysical Research Communications\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;44, IF:2.2). Notably, four of the top ten most productive journals, including \u003cem\u003eCirculation Research\u003c/em\u003e (IF:16.2), hold a Q1 ranking in the Journal Citation Reports (JCR), attesting to their considerable academic influence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Most Productive Journals in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003cp\u003e(2024)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJCR quartile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal of Biological Chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiochemical and Biophysical Research Communications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLoS One\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCirculation Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational Journal of Molecular Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal of Molecular and Cellular Cardiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Molecular Genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProceedings of the Indian National Science Academy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific Reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCo-citation analysis, which assessed the relationships between frequently cited works, revealed a ranking of journal influence that differed considerably from the ranking based on publication volume. The \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e (6,324 co-citations) and \u003cem\u003ePhysiological Reviews\u003c/em\u003e (5,590) emerged as the most influential journals, surpassing \u003cem\u003eCirculation Research\u003c/em\u003e (2,723) and the \u003cem\u003eJournal of Clinical Investigation\u003c/em\u003e (2,268) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This contrast highlights the difference between journals that constitute the field's foundational knowledge base and those that publish the most articles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Most Productive Co-Cited Journals in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo-cited Journal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal citation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage citation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003cp\u003e(2024)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJCR quartile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal of Biological Chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysiological Reviews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCirculation Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal of Clinical Investigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProceedings of the Indian National Science Academy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNature Genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal of Molecular and Cellular Cardiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCirculation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular and Cellular Biology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dual-map overlay (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) illustrates the thematic evolution and intellectual base of the field from 1996 to 2024. In this asymmetric layout, citing journals representing current research fronts are on the left, whereas cited journals representing foundational knowledge are on the right. The color-coded curves depict the flow of intellectual influence from foundational research areas to current research areas.\u003c/p\u003e\n\u003ch3\u003eContribution of authors and co-cited authors\u003c/h3\u003e\n\u003cp\u003eFrom 1996 to 2024, 9,665 authors published research on AS in CVDs. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Thomas A. Cooper (Baylor College of Medicine) led to both publication output (n\u0026thinsp;=\u0026thinsp;25) and total citations (n\u0026thinsp;=\u0026thinsp;3,096). He was followed by Jian-Ping Jin (University of Illinois Chicago; n\u0026thinsp;=\u0026thinsp;21, citations\u0026thinsp;=\u0026thinsp;1,207) and Andrea N. Ladd (Case Western Reserve University; n\u0026thinsp;=\u0026thinsp;13, citations\u0026thinsp;=\u0026thinsp;800). Tuck Wah Soong (National University of Singapore) and Ying Ge (University of Wisconsin-Madison) also ranked high, with 13 publications each. The high citation counts of these researchers reflect their profound influence on the development of the AS in CVDs field.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 12 Most Productive Authors in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInstitutions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal citation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage citation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCooper, Thomas A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaylor College of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJian-Ping Jin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversity of Illinois Chicago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLadd, Andrea N.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase Western Reserve University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoong, Tuck Wah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNational University of Singapore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGe, Ying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversity of Wisconsin-Madison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEsther E. Creemers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversity of Amsterdam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePinto, Yigal M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversity of Amsterdam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiao, Ping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNational Neuroscience Institute (Singapore)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeder, Benjamin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversity Hospital Heidelberg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evan den Hoogenhof, Maarten M. G.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcademic Medical Center (University of Amsterdam)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeqqali, Abdelaziz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcademic Medical Center (University of Amsterdam)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedina, Marisa Wong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren's Hospital Oakland Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA co-authorship network analysis revealed collaboration among 258 authors, each of whom had at least three publications. The network was divided into six major clusters forming distinct research communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Temporal network analysis further elucidated the dynamics of these collaborations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), revealing both the stability and concurrent activity of the primary research teams.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eHigh-cited publications and co-cited references\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e lists the 10 most-cited publications within AS in CVDs research, all of which exceed 570 citations. Among these, the review \"\u003cem\u003eDopamine receptors: From structure to function\u003c/em\u003e\"\u003csup\u003e23\u003c/sup\u003e (2,763 citations) stands as a seminal work. Co-citation analysis of 74,622 references revealed \"\u003cem\u003eRBM20, a gene for hereditary cardiomyopathy, regulates titin splicing\u003c/em\u003e\"\u003csup\u003e24\u003c/sup\u003e (100 co-citations) as the most central publication (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). It plays a pivotal role by demonstrating that RBM20 deficiency drives pathological splicing, leading to sarcomere impairment, aberrant calcium handling, and dilated cardiomyopathy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Most-Cited Articles in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDocument type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIF (2024)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopamine receptors: From structure to function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC Missale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePhysiological Reviews\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe calpain system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDarrell E Goll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePhysiological Reviews\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutations in the gene encoding lamin A/C cause autosomal dominant Emery-Dreifuss muscular dystrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG Bonne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNature Genetics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructural and functional diversity of connexin genes in the mouse and human genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKlaus Willecke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBiological Chemistry\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstanoid receptors: Subtypes and signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR M Breyer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eAnnual Review of Pharmacology and Toxicology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsoform 1c of sterol regulatory element binding protein is less active than isoform 1a in livers of transgenic mice and in cultured cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH Shimano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eJournal of Clinical Investigation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructure of von Willebrand factor-cleaving protease (ADAMTS13), a metalloprotease involved in thrombotic thrombocytopenic purpura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eXinglong Zheng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisruption of splicing regulated by a CUG-binding protein in myotonic dystrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA V Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eScience\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifferential expression of exons 1a and 1c in mRNAs for sterol regulatory element binding protein-1 in human and mouse organs and cultured cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI Shimomura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eJournal of Clinical Investigation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA novel X-linked gene, G4.5. is responsible for Barth syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS Bione\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNature Genetics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Most Co-Cited References in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDocument type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRBM20, a gene for hereditary cardiomyopathy, regulates titin splicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWei Guo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eNature Medicine\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative isoform regulation in human tissue transcriptomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEric T Wang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eNature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA postnatal switch of CELF and MBNL proteins reprograms alternative splicing in the developing heart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuinash Kalsotra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePNAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQun Pan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eNature Genetics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Chomczynski\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnalytical Biochemistry\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASF/SF2-regulated CaMKIIdelta alternative splicing temporally reprograms excitation-contraction coupling in cardiac muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXiangdong Xu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCell\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutations in ribonucleic acid binding protein gene cause familial dilated cardiomyopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKatharine M Brauch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHenrike Maatz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eThe Journal of clinical investigation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanisms of alternative pre-messenger RNA splicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDouglas L Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnnual review of biochemistry\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerated estimation of fold change and dispersion for RNA-seq data with DESeq2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMichael I Love\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGenome biology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCitation burst analysis, which identifies publications with sharp increases in citation rates, tracks the evolving research trends. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB shows the top 25 references with the strongest citation bursts from 1996 to 2024, including their strength and duration. This highlights publications pivotal to advancing new research frontiers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Keywords and Keywords Burst\u003c/h2\u003e \u003cp\u003eThe research landscape and its thematic structure were mapped through keyword co-occurrence and cluster analysis. High-frequency keywords such as \"RNA-binding proteins\" (n\u0026thinsp;=\u0026thinsp;38), \"heart failure\" (n\u0026thinsp;=\u0026thinsp;35), and \"gene expression\" (n\u0026thinsp;=\u0026thinsp;34) represent core research interests (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Thematic network visualization achieved through VOSviewer and CiteSpace revealed distinct research clusters, each color-coded for clarity. Specifically, the resulting analyses included a keyword cluster density map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), co-occurrence network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), and clustered keyword maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), which collectively delineated the conceptual architecture of the field.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 23 Keywords in AS in CVDs Research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKeywords\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealternative splicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA-binding proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDilated cardiomyopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiac hypertrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiac muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxidative stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkeletal muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcium channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSplice variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative RNA splicing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcium channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCerebral ischemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiac function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative polyadenylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlzheimers disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcium signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMass spectrometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAdditional files\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe original documents downloaded from the Web of Science Core Collection contain all 1,712 publications for the analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe evolution of research priorities was traced using timeline visualization and burst detection methods. A timeline cluster view (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) was generated using the log-likelihood ratio (LLR) method. This illustrates the developmental trajectory of major research themes. Concurrently, keyword burst analysis identified terms that attracted strong but brief scholarly attention (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, the blue line represents the entire timeline (1996\u0026ndash;2024), while the red segments denote periods of citation bursts, capturing both sustained and transient research trends. Collectively, the findings outline a comprehensive and evolving landscape of the field and identify key emerging frontiers that shape its future trajectory.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information\u003c/h2\u003e \u003cp\u003eWe used WoSCC data to construct a systematic map of AS research in CVDs from 1996 to 2024. The intellectual landscape is geographically concentrated. The US leads in productivity, scientific impact, and collaborative networks. This leadership is supported by foundational contributions from key institutions such as the University of California System. Core knowledge is spread through influential journals, including \u003cem\u003eCirculation Research\u003c/em\u003e, the \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e, and \u003cem\u003ePLoS One\u003c/em\u003e. Among individual researchers, Thomas A. Cooper stands out as a pivotal figure, showing exceptional leadership in productivity, citations, and H-index metrics. Collectively, our findings outline the intellectual and social structure of the AS in CVDs field, providing a basic roadmap to guide future research directions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHotspots and future perspectives\u003c/h2\u003e \u003cp\u003eWe synthesize recent key literature on these topics by analyzing keywords and timelines, which identify several research hotspots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRNA-binding proteins\u003c/h2\u003e \u003cp\u003eRNA-binding proteins (RBPs) are the master regulators of RNA metabolism. They control key steps in pre-mRNA processing, including AS and translational control, which are essential for maintaining cellular homeostasis.\u003csup\u003e25\u003c/sup\u003e The foundational era of AS research (1970s\u0026ndash;1990s) has systematically elucidated the mechanistic principles of splicing variants.\u003csup\u003e26\u003c/sup\u003e It also established its role within a multi-tiered gene regulatory network that integrates chromatin accessibility, transcription, and proteostasis.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the cardiovascular system, RBPs directly govern the cell fate and phenotype. For instance, Quaking and HuR induce disease-related phenotypic switching in vascular smooth muscle and endothelial cells, thereby promoting inflammatory activation and barrier dysfunction in atherosclerosis.\u003csup\u003e28\u003c/sup\u003e Under hemodynamic stress, RBPs further promote leukocyte recruitment by modulating the splicing and expression of adhesion molecules, including ICAM-1 and VCAM-1. \u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDysfunctional RBPs play a critical role in the development of dilated cardiomyopathy (DCM) and arrhythmia. Notably, RBM20 deficiency induces pathological titin splicing, leading to sarcomere impairment and DCM.\u003csup\u003e30\u003c/sup\u003e Similarly, the loss of RBPMS, a regulator of structural gene splicing, results in severe cardiomyopathy.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrate that RBPs are key drivers of cardiovascular pathogenesis and serve as promising targets for molecular therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHeart Failure\u003c/h2\u003e \u003cp\u003eSplicing dysregulation is a central pathogenic mechanism in HF. This process is directed by RBPs, including RBM20, MBNL1, and CELF1, which orchestrate tissue-specific AS of critical cardiac genes such as \u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eMYH7\u003c/em\u003e, and \u003cem\u003eSCN5A\u003c/em\u003e.\u003csup\u003e32,33\u003c/sup\u003e For example, RBM20 mutations induce aberrant \u003cem\u003eTTN\u003c/em\u003e splicing, which leads to familial DCM.\u003csup\u003e34\u003c/sup\u003e Beyond these well-characterized RBPs, alterations in other splicing factors, such as RBM5, ZRANB2, and HNRNPF, also contribute to maladaptive AS events in HF.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecent advances have revealed the translational potential of splicing error correction. These include restoring \u003cem\u003eRBFox1\u003c/em\u003e expression to ameliorate pathological cardiac remodeling via protective MEF2 isoforms,\u003csup\u003e35\u003c/sup\u003e inhibiting Dyrk1A to normalize pathological CaMKIIδ splicing, improving post-infarction cardiac function,\u003csup\u003e36\u003c/sup\u003e and identifying DDX5 as a key RNA helicase that maintains calcium homeostasis by repressing the aberrant CaMKIIδA isoform.\u003csup\u003e37\u003c/sup\u003e Additionally, \u003cem\u003eTrdn-as\u003c/em\u003e regulates triadin splicing to preserve calcium handling.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTogether, these findings delineate multiple splicing-dependent pathways in HF and validate their therapeutic relevance. They also pave the way for splicing-corrective strategies, such as small molecules, antisense oligonucleotides (ASOs), and RNA-targeted gene therapies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDilated Cardiomyopathy\u003c/h2\u003e \u003cp\u003eDriven by defects in AS, DCM manifests as ventricular dilation and systolic dysfunction, and typically progresses to HF. In particular, RBM20 mutations disrupt the expression of \u003cem\u003eTTN\u003c/em\u003e and \u003cem\u003eCAMK2D\u003c/em\u003e isoforms, leading to sarcomere dysfunction and electromechanical remodeling.\u003csup\u003e24,39\u003c/sup\u003e Although RBM20 variants increase atrial fibrillation risk, they show no significant association with overall survival or transplant outcomes.\u003csup\u003e40\u003c/sup\u003e This finding suggests that mutation carriers may experience distinct pathophysiological trajectories.\u003c/p\u003e \u003cp\u003eSplicing dysregulation in DCM involves other key regulators. For instance, RBM24 ablation disrupts Z-disc and M-band integrity because it causes aberrant splicing of structural proteins.\u003csup\u003e41\u003c/sup\u003e In contrast, upregulated SLM2 expression fine-tunes the splicing of sarcomeric transcripts, such as \u003cem\u003eMYL2\u003c/em\u003e and \u003cem\u003eTTN\u003c/em\u003e, thereby preserving cardiomyocyte architecture.\u003csup\u003e42\u003c/sup\u003e Additionally, a lack of lncRNA DCRT causes mitochondrial impairment by triggering PTBP1-mediated missplicing of \u003cem\u003eNDUFS2\u003c/em\u003e.\u003csup\u003e43\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTaken together, these findings delineate a complex, multi-factorial regulatory network in DCM and pinpoint promising therapeutic targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCardiac Hypertrophy\u003c/h2\u003e \u003cp\u003ePathological splicing reprogramming drives extensive remodeling underlying cardiac hypertrophy.\u003csup\u003e44\u003c/sup\u003e In the initial phase, pressure overload induces upregulation of PTB/ESRP1, which stabilizes pro-hypertrophic mRNAs through alternative polyadenylation at the 3'-UTR.\u003csup\u003e45\u003c/sup\u003e Conversely, reduced RBM10-Star-PAP activity diminishes anti-hypertrophic gene expression, disrupting the balance of growth signals.\u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDuring disease progression, RIPK3 deficiency modulates the splicing regulators ASF/SF2 and SC-35 to promote pathological CaMKIIδ splicing.\u003csup\u003e47\u003c/sup\u003e Furthermore, PP1γ directly promotes production of the pro-hypertrophic CaMKIIδC isoform.\u003csup\u003e48\u003c/sup\u003e These events converge to impair calcium handling, which is further worsened by Ca\u003csub\u003eV\u003c/sub\u003e1.2 channel splicing defects\u003csup\u003e49\u003c/sup\u003e and SRSF9-mediated progression of the hypertrophic phenotype.\u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCollectively, studies have established that the regulatory mechanisms of cardiac hypertrophy exhibit significant spatial heterogeneity, a feature that drives a spectrum of region-specific adaptive and dysfunctional outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOxidative Stress\u003c/h2\u003e \u003cp\u003eOxidative stress and AS are key pathogenic mechanisms in CVDs. Mitochondria-derived oxidative stress causes direct cellular damage and induces widespread splicing alterations through multiple mechanisms.\u003csup\u003e51\u003c/sup\u003e For instance, oxidative stress triggers the pathological splicing of VEGF-A and QKI in diabetic vasculopathy, which disrupts endothelial homeostasis.\u003csup\u003e52\u003c/sup\u003e Oxidative stress further promotes arrhythmogenesis through oxidative modification of CaMKIIδ, which generates a persistent pathological signal.\u003csup\u003e53\u003c/sup\u003e In addition, transcriptional cross-talk between Foxp1 and NLRP3 regulates oxidative stress responses, thereby increasing the complexity of pathogenic mechanisms.\u003csup\u003e54\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe discovery that GATA4 binds spliceosomal components reveals a novel link between oxidative stress and splicing.\u003csup\u003e55\u003c/sup\u003e By targeting this integrated response, oligonucleotide therapies thus represent a promising intervention for ischemia-reperfusion injury, which is now under clinical evaluation in CVDs.\u003csup\u003e56\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture Trends\u003c/h2\u003e \u003cp\u003eMoving forward, bridging AS research and cardiovascular clinical practice will require synergistic advances in high-resolution molecular diagnostics and mechanism-based targeted therapies.\u003csup\u003e57\u003c/sup\u003e The emerging atlas of human cardiac isoforms, mapped via long-read single-cell RNA sequencing (scRNA-seq), revealed that isoform switching in genes such as \u003cem\u003eTTN\u003c/em\u003e and \u003cem\u003eMYH7\u003c/em\u003e is a hallmark of HF.\u003csup\u003e58,59\u003c/sup\u003e Increasing evidence highlights the need to develop isoform-specific diagnostic tools.\u003c/p\u003e \u003cp\u003eConcurrently, AI models trained on genomic and epigenetic data predict splicing regulators and decipher the cis-regulatory codes of pathological AS, thereby unveiling new druggable targets.\u003csup\u003e60,61\u003c/sup\u003e To target these nodes, ASO therapeutics have been advanced through chemical redesign and guanidine-based lipid nanoparticles (LNPs).\u003csup\u003e62,63\u003c/sup\u003e These LNPs enhance extrahepatic mRNA delivery and target immune cells, providing a critical advantage for modulating splicing in cardiac immune-stromal cells.\u003csup\u003e64\u003c/sup\u003e By targeting splice variants like CD73, the sonogenetic ASO nanoplatform strategy modulates cellular metabolism and immunity, showing promise for application against cardiovascular inflammation and fibrosis.\u003csup\u003e65\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe convergence of single-cell multiomics, rational oligonucleotide design, and cell-selective delivery is ushering in a transformative era for personalized splicing modulation, which is poised to redefine therapeutic paradigms across a range of CVDs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study provides the first systematic bibliometric mapping of AS research in CVDs and establishes an objective framework to evaluate this rapidly evolving field. Through a quantitative analysis of large-scale research literature, we identified foundational publications, collaborative networks, and emerging research frontiers.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, our analysis relied exclusively on English-language publications from WoSCC, which may introduce selection bias, while the database's coverage of high-impact journals supports the robustness of the identified trends. Second, the study period ends in December 2024, establishing a defined historical baseline; however, it excludes subsequent developments. Relatedly, citation metrics are subject to temporal bias, inherently favoring older established works. Finally, lags in database indexing can cause variability in the number of publications in recent years.\u003c/p\u003e \u003cp\u003eThese limitations are inherent to bibliometric research and do not undermine the core conclusions about the field's development and organization. Consequently, this analysis establishes a robust foundation for the future tracking of AS research in CVDs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, AS dysregulation is now recognized as a central mechanism in CVDs. It holds dual importance for both diagnostic discovery and therapeutic intervention. Advanced technologies enable high-resolution mapping of splicing landscapes, and novel agents (e.g., octaguanidine-conjugated ASOs, inducible CRISPR/dCas13 systems) offer enhanced precision. However, full clinical translation of this knowledge requires a deeper understanding of cell type-specific mechanisms. To realize this potential, the field must focus on identifying key splicing regulators and driver events that support targeted diagnostics and therapies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eData source and literature search strategy\u003c/h2\u003e \u003cp\u003eWe selected the Web of Science Core Collection (WoSCC) for this bibliometric analysis based on its comprehensive coverage of high-impact journals and established role in scholarly mapping.\u003csup\u003e19\u003c/sup\u003e To ensure data consistency and avoid potential biases from daily database updates, all data were retrieved on April 11 2025. The search strategy was refined in consultation with a specialized medical librarian and through a review of pivotal literature in the field.\u003csup\u003e20\u003c/sup\u003e The final Boolean search query employed was: TS= (alternative splicing) AND TS = (\u0026ldquo;high blood pressure\u0026rdquo; or hypertensi* or \u0026ldquo;peripheral arter*\u0026rdquo; disease* or \u0026ldquo;atrial fibrillat*\u0026rdquo; or tachycardi* or endocardi* or pericard* or ischem* or arrhythmi* or thrombo* or cardio* or cardiac* or \u0026ldquo;heart failure\u0026rdquo; or \u0026ldquo;heart beat\u0026rdquo; or \u0026ldquo;heart rate*\u0026rdquo; or \u0026ldquo;heart val*\u0026rdquo; or coronary* or angina* or ventric* or myocard* or \u0026ldquo;hyperlipid\u0026lowast;\u0026rdquo; or \u0026ldquo;hypercholesterol\u0026lowast;\u0026rdquo; or \u0026ldquo;hypercholester\u0026lowast;\u0026rdquo; or \u0026ldquo;hypertriglycerides\u0026lowast;\u0026rdquo; or \u0026ldquo;cholesterol*\u0026rdquo; or \u0026ldquo;congenital heart\u0026rdquo; or \u0026ldquo;heart defect*\u0026rdquo; or \u0026ldquo;congenital heart defect\u0026rdquo; or \u0026ldquo;heart attack\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe literature search encompassed publications from January 1, 1996, to December 31, 2024. To minimize potential analytical biases from language barriers, we restricted the inclusion to English-language publications. This study included only original articles and reviews as the primary sources of established, peer-reviewed knowledge in this field. To minimize subjective bias, two investigators (Jianbin Qin and Quanwen Li) independently performed screening: first, based on titles and abstracts, and then via full-text assessment of potentially eligible records. Any discrepancies in eligibility were resolved through a consensus discussion or, when necessary, adjudicated by a senior researcher (Quanzhong Li). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the screening workflow and the inclusion criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eBibliometric analysis\u003c/h2\u003e \u003cp\u003eWe performed a bibliometric analysis and scientific mapping using a suite of established tools. We employed the following software for the specific tasks:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVOSviewer (version 1.6.20)\u003c/b\u003e was used to construct and visualize the co-authorship, co-institution, and co-occurrence networks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCiteSpace (version 6.4.R1)\u003c/b\u003e to detect emerging trends and conduct citation burst analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe R package \"bibliometrix\" (version 4.3.5)\u003c/b\u003e was used for comprehensive data ingestion, preprocessing, and descriptive statistical analysis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we used \u003cb\u003eMicrosoft Excel (2021)\u003c/b\u003e for the initial data curation and \u003cb\u003eMicrosoft Charticulator\u003c/b\u003e to generate customized, publication-ready figures beyond the output of the primary bibliometric tools.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealternative splicing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASOs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantisense oligonucleotides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edilated cardiomyopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eheart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLNPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipid nanoparticles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA- binding proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003escRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-cell RNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWoSCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeb of Science Core Collection.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eJianbin Qin, Quanwen Li, and Bin Cai designed the study, collected the data, and drafted the initial manuscript. Weijian Wang conducted the statistical analysis and assisted in data interpretation. Shengyuan Lin critically revised the manuscript and participated in data interpretation. Shengjun Xiao supervised the project administration and contributed to the final review and approval of the manuscript. Quanzhong Li provided scientific supervision and oversaw the entire study. All authors reviewed and approved the final manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll data generated during this study are included in this published article. The analysis during the study can be obtained from the corresponding author Quanzhong Li on reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: Update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982-3021. doi: 10.1016/j.jacc.2020.11.010.\u003c/li\u003e\n\u003cli\u003ePark E, Pan Z, Zhang Z, Lin L, Xing Y. The expanding landscape of alternative splicing variation in human populations. 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Curr Atheroscler Rep. 2024;27(1):8. doi: 10.1007/s11883-024-01250-2.\u003c/li\u003e\n\u003cli\u003eGotthardt M, Badillo-Lisakowski V, Parikh VN, et al. Cardiac splicing as a diagnostic and therapeutic target. Nat Rev Cardiol. 2023;20(8):517-530. doi: 10.1038/s41569-022-00828-0.\u003c/li\u003e\n\u003cli\u003eGan P, Wang Z, Bezprozvannaya S, et al. RBPMS regulates cardiomyocyte contraction and cardiac function through RNA alternative splicing. Cardiovasc Res. 2024;120(1):56-68. doi: 10.1093/cvr/cvad166.\u003c/li\u003e\n\u003cli\u003eKhan MS, Shahid I, Bennis A, Rakisheva A, Metra M, Butler J. Global epidemiology of heart failure. Nat Rev Cardiol. 2024;21(10):717-734. doi: 10.1038/s41569-024-01046-6.\u003c/li\u003e\n\u003cli\u003eFochi S, Lorenzi P, Galasso M, et al. The emerging role of the RBM20 and PTBP1 ribonucleoproteins in heart development and cardiovascular diseases. Genes. 2020;11(4):402. doi: 10.3390/genes11040402.\u003c/li\u003e\n\u003cli\u003eMehta Z, Touma M. Post-transcriptional modification by alternative splicing and pathogenic splicing variants in cardiovascular development and congenital heart defects. Int J Mol Sci. 2023;24(2):1555. doi: 10.3390/ijms24021555.\u003c/li\u003e\n\u003cli\u003eGao C, Ren S, Lee JH, et al. RBFox1-mediated RNA splicing regulates cardiac hypertrophy and heart failure. J Clin Investig. 2016;126(1):195-206. doi: 10.1172/JCI84015.\u003c/li\u003e\n\u003cli\u003eHe J, Yao J, Sheng H, Zhu J. Involvement of the dual-specificity tyrosine phosphorylation-regulated kinase 1A-alternative splicing factor-calcium/calmodulin-dependent protein kinase II\u0026delta; signaling pathway in myocardial infarction-induced heart failure of rats. J Card Fail. 2015;21(9):751-760. doi: 10.1016/j.cardfail.2015.05.015.\u003c/li\u003e\n\u003cli\u003eJia K, Cheng H, Ma W, et al. RNA helicase DDX5 maintains cardiac function by regulating CamkII\u0026delta; alternative splicing. Circulation. 2024;150(14):1121-1139. doi: 10.1161/CIRCULATIONAHA.123.064774.\u003c/li\u003e\n\u003cli\u003eZhao Y, Riching AS, Knight WE, et al. Cardiomyocyte-specific long noncoding RNA regulates alternative splicing of the triadin gene in the heart. Circulation. 2022;146(9):699-714. doi: 10.1161/CIRCULATIONAHA.121.058017.\u003c/li\u003e\n\u003cli\u003eMaatz H, Jens M, Liss M, et al. RNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing. J Clin Investig. 2014;124(8):3419-3430. doi: 10.1172/JCI74523.\u003c/li\u003e\n\u003cli\u003eRefaat MM, Lubitz SA, Makino S, et al. Genetic variation in the alternative splicing regulator RBM20 is associated with dilated cardiomyopathy. Heart Rhythm. 2012;9(3):390-396. doi: 10.1016/j.hrthm.2011.10.016.\u003c/li\u003e\n\u003cli\u003eLiu J, Kong X, Zhang M, Yang X, Xu X. RNA binding protein 24 deletion disrupts global alternative splicing and causes dilated cardiomyopathy. Protein Cell. 2019;10(6):405-416. doi: 10.1007/s13238-018-0578-8.\u003c/li\u003e\n\u003cli\u003eBoeckel JN, M\u0026ouml;bius-Winkler M, M\u0026uuml;ller M, et al. SLM2 is A novel cardiac splicing factor involved in heart failure due to dilated cardiomyopathy. Genomics Proteomics Bioinformatics. 2022;20(1):129-146. doi: 10.1016/j.gpb.2021.01.006.\u003c/li\u003e\n\u003cli\u003eDu H, Zhao Y, Wen J, et al. LncRNA DCRT protects against dilated cardiomyopathy by preventing NDUFS2 alternative splicing by binding to PTBP1. Circulation. 2024;150(13):1030-1049. doi: 10.1161/CIRCULATIONAHA.123.067861.\u003c/li\u003e\n\u003cli\u003eYamazaki T, W\u0026auml;lchli S, Fujita T, et al. Splice variants of enigma homolog, differentially expressed during heart development, promote or prevent hypertrophy. Cardiovasc Res. 2010;86(3):374-382. doi: 10.1093/cvr/cvq023.\u003c/li\u003e\n\u003cli\u003eKim T, Kim JO, Oh JG, Hong SE, Kim DH. 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Circulation. 2022;146(10):770-787. doi: 10.1161/CIRCULATIONAHA.121.057620.\u003c/li\u003e\n\u003cli\u003eDery KJ, Wong Z, Wei M, Kupiec-Weglinski JW. Mechanistic insights into alternative gene splicing in oxidative stress and tissue injury. Antioxid Redox Signal. 2024;41(13-15):890-909. doi: 10.1089/ars.2023.0437.\u003c/li\u003e\n\u003cli\u003eJiang J, Wu H, Ji Y, et al. Development and disease-specific regulation of RNA splicing in cardiovascular system. Front Cell Dev Biol. 2024;12:1423553. doi: 10.3389/fcell.2024.1423553.\u003c/li\u003e\n\u003cli\u003ePan T, Lu L, Youker K, et al. Single-cell splicing isoform atlas of the adult human heart and heart failure. Circulation. 2025;152(21):1501-1514. doi: 10.1161/CIRCULATIONAHA.125.074959.\u003c/li\u003e\n\u003cli\u003eWu H, Lu Y, Duan Z, et al. Nanopore long-read RNA sequencing reveals functional alternative splicing variants in human vascular smooth muscle cells. Commun Biol. 2023;6(1):1104. doi: 10.1038/s42003-023-05481-y.\u003c/li\u003e\n\u003cli\u003eKimata K, Satou K. Improved CRISPR/Cas9 off-target prediction with DNABERT and epigenetic features. PLOS One. 2025;20(11):e0335863. doi: 10.1371/journal.pone.0335863.\u003c/li\u003e\n\u003cli\u003eCao J, Wei Z, Nie Y, Chen HZ. Therapeutic potential of alternative splicing in cardiovascular diseases. EBiomedicine. 2024;101:104995. doi: 10.1016/j.ebiom.2024.104995.\u003c/li\u003e\n\u003cli\u003eZhao C, Li X, He Z, Ye C, Chen F, Cheng J. PEG-ASO conjugates for efficient targeted delivery and migration inhibition in Cancer cell. Bioorg Med Chem Lett. 2025;122:130208. doi: 10.1016/j.bmcl.2025.130208.\u003c/li\u003e\n\u003cli\u003eZhang H, Liu D, Yang K, Liang Z, Li M. Ionizable guanidine-based lipid nanoparticle for targeted mRNA delivery and cancer immunotherapy. Sci Adv. 2025;11(43):eadx5970. doi: 10.1126/sciadv.adx5970.\u003c/li\u003e\n\u003cli\u003eYu H, Dyett BP, Drummond CJ, Zhai J. Ionizable lipid nanoparticles for mRNA delivery: Internal self-assembled inverse mesophase structure and endosomal escape. Acc Chem Res. 2025;58(20):3210-3222. doi: 10.1021/acs.accounts.5c00522.\u003c/li\u003e\n\u003cli\u003eXiong B, Yu J, Wen C, et al. Antisense oligonucleotide-loaded nanozyme reverses tumor immune suppression through sonogenetic metabolic therapy. J Control Release. 2025;387:114236. doi: 10.1016/j.jconrel.2025.114236.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Figures","content":"\u003cp\u003eFigures are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bibliometric analysis, alternative splicing, cardiovascular diseases, VOSviewer, CiteSpace","lastPublishedDoi":"10.21203/rs.3.rs-8654383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8654383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlternative splicing (AS) is a critical regulatory mechanism in cardiovascular diseases (CVDs). However, a comprehensive bibliometric overview of this rapidly evolving field is lacking. This study aimed to address this gap by providing the first systematic map of the research landscape.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePublications on AS and CVDs (1996\u0026ndash;2024) were retrieved from the Web of Science Core Collection. Analyses were performed using VOSviewer, CiteSpace, and the R package \"bibliometrix\" for computational mapping and network visualization, supplemented by Microsoft Excel and Charticulator.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 1996 to 2024, 1,712 pertinent publications were identified, involving 9,665 authors from 200 institutions across 66 countries/regions. The United States, China, and Germany led in productivity, with a strong US\u0026ndash;China collaborative link. The University of California system was the most productive institution, and Thomas A. Cooper was the leading author. Core journals included the \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e, \u003cem\u003eGene\u003c/em\u003e, and \u003cem\u003eBiochemical and Biophysical Research Communications\u003c/em\u003e. Research hotspots centered on RNA-binding proteins, heart failure, dilated cardiomyopathy, cardiac hypertrophy, and oxidative stress.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study delivers the first systematic bibliometric assessment of the AS in CVDs field. Future efforts should prioritize elucidating the mechanistic basis of splicing dysregulation and translating these insights into targeted therapies to meet unmet needs in cardiovascular clinical practice.\u003c/p\u003e","manuscriptTitle":"Alternative splicing in cardiovascular disease: a visualized bibliometric analysis of global research trends (1996-2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 16:37:21","doi":"10.21203/rs.3.rs-8654383/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T16:16:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T23:02:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206967954282680739075009963779328535002","date":"2026-03-08T18:22:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T17:22:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287877222201326330687796923988247714120","date":"2026-03-04T22:14:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T13:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146805372302850905052421556130343527877","date":"2026-02-25T05:32:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306563837220461250838493033269329557489","date":"2026-02-24T18:03:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T17:49:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T14:07:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-23T07:32:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T07:31:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2026-01-21T02:48:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff686182-8cbe-4319-aedf-c56965cbc3eb","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T17:23:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 16:37:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8654383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8654383","identity":"rs-8654383","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}