Postmortem analyses of myocardial microRNA expression in sepsis 

Postmortem analyses of myocardial microRNA expression in sepsis | 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 Article Postmortem analyses of myocardial microRNA expression in sepsis Pasi Lehto, Taru Saukko, Hanna Säkkinen, Hannu Syrjälä, Risto Kerkelä, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4455151/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Sepsis can lead to myocardial depression, playing a significant role in sepsis pathophysiology, clinical care, and outcome. To gain more insight into the pathophysiology of the myocardial response in sepsis, we investigated the expression of microRNA in myocardial autopsy specimens in critically ill deceased with sepsis and non-septic controls. Materials and methods In this retrospective observational study, we obtained myocardial tissue samples collected during autopsy from adult patients deceased with sepsis (n = 15) for routine histological examination. We obtained control myocardial tissue specimens (n = 15) from medicolegal autopsies of cadavers whose cause of death was injury or who were found dead at home and the cause of death was coronary artery disease with sudden cardiac arrest. RNA was isolated from formalin-fixed paraffin- embedded (FFPE) cardiac samples using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Invitrogen). Differentially expressed miRNAs were identified using edgeR v3.32. MicroRNA was considered up- or down-regulated if the false discovery rate was < 0.05 and logarithmic fold change (log2FC) ≥ 1 for up-regulated or log2FC ≤ -1 for down-regulated miRNAs. The mean difference and 95% confidence interval (CI) was calculated for normalized read counts. Predicted miRNA targets were retrieved using Ingenuity Pathway Analysis (IPA) software, and pathway enrichment and classification were performed using PantherDB. Results Differential expression analysis identified a total of 32 miRNAs in the myocardial specimens. Eight miRNAs had a significant change in the mean difference based on the 95% CI, with the largest increase in mean counts in septic samples with hsa-miR-12136 and the highest fold change with hsa-miR-146b-5p. The threshold for down-regulated miRNAs in sepsis compared to controls was obtained with hsa-miR-144-5p and hsa-miR-451a, with the latter having the largest decrease in mean counts and fold decrease. Conclusions Several regulatory miRNAs were up- or down-regulated in the myocardial tissue of patients deceased with sepsis compared to non-septic subjects. The predicted target genes of miRNAs are associated with biological functions related to cardiovascular functions, cell viability, cell adhesion, and regulation of inflammatory and immune response. microRNA septic cardiomyopathy sepsis INTRODUCTION Sepsis is a life-threatening dysregulated host immune response to infection that leads to multisystem organ damage [ 1 ]. Pathophysiological features include a complex network of pro-inflammatory responses, immune suppression, and endothelial dysfunction and cell damage from uncontrolled inflammation, which includes myocardial inflammatory responses [ 2 , 3 ]. This systemic immune response leads to septic shock with circulatory maldistribution associated with peripheral vasodilation, arterial and capillary shunting, and sepsis-induced myocardial dysfunction leading to organ damage and high risk of death. Septic cardiomyopathy has been defined as reversible acute uni- or bi-ventricular systolic or diastolic dysfunction with reduced contractility not caused by ischemic coronary heart disease [ 4 ]. MicroRNAs (miRNAs) are a class of non-coding RNA genes that regulate mRNA expression to affect various physiological processes through the adjustment of protein levels [ 5 ]. MiRNAs regulate multiple pathways that form a complex network. Single miRNAs can target hundreds of genes, inhibiting or promoting inflammatory and innate immune responses, a key role in sepsis-induced endothelial and organ dysfunction, including sepsis-related myocardial dysfunction [ 6 – 8 ]. In addition, circulating miRNAs can act as signaling molecules and induce hormone-like responses in target cells and tissues [ 9 , 10 ]. To date, human studies on organ-specific cardiac miRNA tissue levels in sepsis are lacking. We were interested in the expression of miRNA in postmortem myocardial specimens of critically ill septic patients and non-septic controls. METHODS Subjects This retrospective observational study was conducted at Oulu University Hospital, Oulu, Finland, an academic tertiary referral hospital. The study protocol was approved by the hospital administration and the ethics committee of Oulu University Hospital and the Northern Ostrobothnia Hospital District (72/2021; 33/2021). All research was performed in accordance with relevant guidelines and regulations in accordance with the Declaration of Helsinki. The use of myocardial tissue obtained from the medicolegal autopsies as case control samples was approved by the Finnish Institute for Health and Welfare (THL/873/5.05.00/2023; THL/697/5.05.00/2017). All adult intensive care unit (ICU) patients who died with sepsis during the years 2015–2020 and underwent clinical autopsy with available myocardial specimens were included in this study. The specimens were collected during autopsy for routine histological examination and preserved in the Biobank Borealis of Northern Finland. For the present study, the specimens were obtained with sample donation permission from the scientific board committee of the Biobank of Northern Finland. Myocardial tissue samples for non-septic controls were collected during medicolegal autopsies at the Finnish Institute for Health and Welfare. Finnish law requires a medicolegal autopsy to be carried out when the death is caused by non-natural conditions, such as accident, suicide, or homicide, or when the death is unexpected. The selected control subjects had either trauma or coronary artery disease (sudden cardiac death) as the underlying cause of death, and they were matched for age, sex, and presence of coronary artery disease. Sepsis was defined according to the American College of Chest Physicians/Society of Critical Care Medicine Criteria [ 1 ]. All ICU sepsis patients were treated by a multidisciplinary team of intensivists and infectious disease specialists in our 26-bed, closed adult ICU. Intensive care treatment was performed according to normal ICU protocols and sepsis guidelines. The decision to perform a postmortem examination was made at the discretion of the treating physician or following a request from the next of kin. Clinical data collection Clinical parameters were collected from the ICU clinical data management system database (Centricity Critical Care, Clinisoft, GE Healthcare). Illness severity was determined by the Acute Physiology and Chronic Health (APACHE II) score, which was recorded on admission. Organ failure was defined by the Sequential Organ Failure Assessment (SOFA) score, which is commonly used in ICU settings [ 11 , 12 ]. Data were obtained regarding age, sex, focus of infection, chronic illnesses, vasoactive use, and other ICU treatments, as well as the length of stay in the ICU and hospital. Clinical laboratory and microbiological samples were analyzed by commercially available laboratory methods in the hospital’s accredited central laboratory (NordLab, Oulu University Hospital, Oulu, Finland). The control tissue samples were linked to the subjects’ limited health data, which included age, sex, and major chronic diseases. Autopsy material collection and tissue sample preparation The autopsies of the sepsis cases were performed by the Department of Pathology in Oulu University Hospital. Following the cessation of vital functions, post-mortem preparations were conducted within the confines of the intensive care ward prior to the cadaver's transfer to the morgue. The time at ambient room temperature typically lasted a couple of hours. At the morgue, cadavers were stored at a temperature of 4°C. The median time from death to autopsy was 5 days (range: 3–6 days). During the autopsy, cardiac specimens were routinely collected from the septum and left ventricular free wall. Standard procedures were used for preparation. Specimens were fixed in 10% formalin for approximately 24 hours. After fixation, the specimens were processed in a tissue processor, dehydrated in 100% alcohol, and cleared with xylene before being embedded in paraffin and stored at room temperature. The medicolegal autopsies of the control cases were performed in the Forensic Medicine Unit, Finnish Institute for Health and Welfare, Oulu, Finland, and the tissue preparations at the Department of Forensic Medicine, University of Oulu, Oulu, Finland. The specimen preparation was done according to the standard procedures mentioned above with the main difference of an uncertain time in temperatures above 4°C before forensic investigation of the cadaver. Tissue samples were preserved as formalin-fixed paraffin-embedded (FFPE) blocks due to their excellent tissue morphology preservation over time. This method prevents tissue autolysis, enabling histological analysis. Isolating nucleic acids from these samples is important for understanding disease mechanisms at the genomic level. MiRNAs are very short (18–23 nucleotides) and less affected by fixation/embedding, allowing their recovery from FFPE samples even after many years, possibly decades. The expression levels of isolated miRNAs are directly comparable in both frozen and FFPE tissue samples [ 13 – 17 ]. FFPE cardiac tissue specimens and the quality of cardiac tissue specimens were verified by hematoxylin-eosin staining and microscopic analysis before being sectioned for miRNA analysis, using a fresh microtome blade for each block. RNA isolation and miRNA analysis RNA was isolated from FFPE cardiac samples using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Invitrogen). The RealSeq Single Index kit (RealSeq Biosciences) was used for library preparation, and sequencing was performed on Novaseq SP100 (Illumina) using single end reads. Sequencing data were analyzed using the miND® analysis pipeline [ 18 ]. The quality of the NGS data was evaluated using fastQC v0.11.9 and multiQC v1.14. Reads were adapter trimmed using cutadapt v3.3 and mapped to GRCh38.p12 and miRBase v22.1 with bowtie v1.3.0 and miRDeep2 v2.0.1.2. Differentially expressed miRNAs were identified using edgeR v3.32. The target genes for each miRNA were identified utilizing the miRNA target filter within Ingenuity Pathway Analysis (IPA) software (Qiagen) by incorporating target predictions from TargetScan human, Ingenuity Expert Findings, miRecords, and Tarbase databases. Predicted miRNA targets were retrieved by IPA software, and pathway enrichment and classification were performed using PantherDB ( www.pantherdb.org/ ). The target filter prioritized targets that were either experimentally observed or predicted with high confidence, ensuring the exclusion of less reliable predictions from the target gene lists. Enrichment analysis for the target genes of each miRNA was performed using PantherDB’s overrepresentation test (GO biological process complete). Statistical analysis For continuous variables, we used medians with 25th -75th percentiles or means with standard deviations; and counts (%) were used for categorical variables. Comparisons of continuous variables were analyzed using the t-test for independent samples. We also report the 95% confidence intervals (CIs) of the differences between the mean miRNA counts. MiRNAs were considered up- or down-regulated if the FDR was < 0.05 and logarithmic fold change (log2FC) ≥ 1 for up-regulated or log2FC ≤ -1 for down-regulated miRNAs. The mean difference and 95% CI was calculated for normalized read counts. Analyses were performed using SPSS for Windows software (IBM SPSS Statistics for Windows, version 25.0; IBM Corp., Armonk, New York). This study was designed to be descriptive, and the sample size was feasible for the autopsy material available and judged to be adequate to provide reasonable data. Two-tailed P-values were calculated and P < 0.05 considered significant in all analyses. RESULTS We identified a total of 15 patients deceased due to sepsis and who had autopsies performed with myocardial tissue samples available. All of the myocardial specimens for the analysis in this study were obtained from the left ventricular free wall. In the sepsis group, the foci of infection was pulmonary (47%), gastrointestinal (33%), skin (13%), or urinary (7%). Ten (67%) of the sepsis cases had a laboratory confirmed positive blood culture. The median stay in the ICU was 1.3 days, whereas hospital stay was 2.6 days. Cause of death for the 15 control cases was accidental trauma (n = 3) or self-inflicted injury (n = 2) leading to death on-site, or sudden cardiac arrest (n = 10) with coronary artery disease being the underlying cause of death. None had signs of acute infection at autopsy. There were no significant differences regarding gender, age, or major chronic illnesses between the septic and control subjects (Table 1 ). Table 1 Demographics of the controls and patients with myocardial autopsy specimens. Parameter Control (n = 15) Cases (n = 15) P-value Sex, male 8 (53%) 8 (53%) 1.00 Age, years 72 [57.5–77.50] 72 [58.5–77.5] 0.934 Chronic illnesses Hypertension 6 (40%) 10 (67%) 0.143 Coronary artery disease 10 (67%) 10 (67%) 1.0 Diabetes 1 (7%) 4 (27%) 0.227 Data are expressed as median [25th -75th percentile] or n (%). In both groups, 67% of the subjects had coronary artery disease and the median age was 72 years (Table 2 ). Table 2 Demographics and clinical data for sepsis patients with myocardial autopsy specimens. Parameter All (n = 15) Age, years 72 (57–78) Male sex 8 (53%) BMI 24 (22–26) Chronic diseases Coronary artery disease 10 (67%) Hypertension 10 (67%) Diabetes 4 (27%) Chronic renal disease 3 (20%) Severity of disease scores APACHEII, admission 24 (19–34) SOFA, admission 10 (7–15) Focus of infection Pulmonary 7 (47%) Gastrointestinal 5 (33%) Urinary tract 1 (7%) Skin 2 (13%) Laboratory values Lactate, admission 3.1 (1.4–9.8) CRP, admission 168 (99–353) PCT, admission 13.1 (3.3–61.3) Lactate, highest 14.4 (5.9–16.5) ICU treatments Mechanical ventilation 9 (60%) Inotropes 8 (53%) Renal replacement 8 (53%) Length of stay Hospital days 2.6 (1.1–12.8) ICU days 1.3(0.5–6.8) Data are expressed as median (25th − 75th PCT) or n (%). A total of 1753 miRNAs were detected in the miRNA sequencing, 267 of which were abundantly expressed in the samples and subjected to differential expression analysis. We identified 32 differentially expressed miRNAs in the myocardial specimens of sepsis cases compared to controls (Table 3 ). Table 3 Differentially expressed miRNAs in the myocardia of sepsis patients and controls. miRNA Group Mean RPM SD Mean Difference 95% CI FDR log2(FC) hsa-miR-10400-5p Case 355 693 323 -61–707 0.0014 3.5 Control 32 15 hsa-miR-4488 Case 473 1016 420 -143–983 0.0024 3.1 Control 53 43 hsa-miR-3196 Case 106 157 83 -4–171 0.0034 2.3 Control 23 18 hsa-miR-4508 Case 57 97 44 -10–97 0.01 2.1 Control 13 7 hsa-miR-146b-5p Case 849 606 622 248–995 0.0042 1.9 Control 227 345 hsa-miR-3960 Case 698 769 461 26–896 0.043 1.5 Control 237 199 hsa-miR-320d Case 57 47 37 9–65 0.019 1.4 Control 20 21 hsa-miR-320c Case 75 54 46 14–78 0.013 1.3 Control 29 24 hsa-miR-4787-5p Case 98 103 60 3–118 0.019 1.3 Control 37 18 hsa-miR-21-3p Case 248 134 134 32–236 0.035 1.1 Control 114 139 hsa-miR-12136 Case 5836 3225 2933 1065–4800 0.013 1 Control 2903 1194 hsa-miR-155-5p Case 166 103 83 20–147 0.029 0.98 Control 82 62 hsa-miR-320b Case 117 66 56 16–97 0.019 0.92 Control 61 36 hsa-miR-4449 Case 75 26 31 14–48 0.015 0.76 Control 44 20 hsa-let-7c-5p Case 1718 465 592 229–954 0.045 0.59 Control 1126 503 hsa-miR-652-3p Case 2294 423 735 431–1039 0.0031 0.54 Control 1559 390 hsa-miR-98-5p Case 2467 499 606 296–915 0.017 0.40 Control 1861 305 hsa-miR-28-3p Case 1950 215 355 204–507 0.017 0.27 Control 1595 190 hsa-miR-140-3p Case 2591 496 -428 -780 – -76 0.0497 -0.25 Control 3019 443 hsa-miR-181a-3p Case 106 14 -31 -54 – -8 0.0493 -0.38 Control 136 40 hsa-miR-423-3p Case 2654 484 -783 -1425 – -140 0.0497 -0.39 Control 3436 1088 hsa-miR-93-5p Case 3027 728 -955 -1737 – -173 0.033 -0.42 Control 3982 1287 hsa-miR-181c-3p Case 66 18 -25 -42 – -8 0.031 -0.49 Control 91 26 hsa-miR-127-3p Case 328 97 -143 -230 – -56 0.014 -0.55 Control 471 133 hsa-miR-199b-5p Case 1340 490 -676 -1164 – -187 0.031 -0.61 Control 2016 784 hsa-miR-486-5p Case 3006 911 -1564 -2317 – -812 0.0042 -0.64 Control 4570 1094 hsa-miR-199a-5p Case 2449 761 -1406 -2323 – -489 0.0042 -0.67 Control 3856 1558 hsa-miR-654-3p Case 40 16 -25 -39 – -12 0.03 -0.72 Control 66 20 hsa-miR-136-3p Case 122 36 -79 -119 – -39 0.0034 -0.73 Control 201 66 hsa-miR-363-3p Case 260 85 -193 -284 – -101 0.0014 -0.83 Control 453 150 hsa-miR-144-5p Case 214 150 -281 -407 – -156 0.0016 -1.2 Control 496 184 hsa-miR-451a Case 27244 15987 -44281 -72335 – -16226 0.0014 -1.4 Control 71525 48944 Mean, standard deviation (SD), and mean difference with 95% confidence interval (CI) were calculated from normalized miRNA read counts (reads per million, RPM). Differential expression analysis was performed for miRNA sequencing data using edgeR and is presented as the false discovery rate (FDR) and log2 of fold change (FC). Eight miRNAs had log2FC > 1 and a significant change in the mean difference based on the 95% CI: miR-12136, miR-146b-5p, miR-155-5p, miR-21-3p, miR-320c, miR-320d, miR-3960, miR-4488, and miR-4787-5p. MiR-12136 had the largest increase in mean counts in sepsis hearts, and miR-146b-5p had the highest fold change. Two miRs, miR-144-5p and miR-451a, had log2FC < -1 and a significant change in the mean difference based on the 95% CI. MiR-451a had the largest difference in mean counts and highest fold decrease. Enrichment analysis was performed for the predicted target genes of the 32 differentially expressed miRNAs to identify regulated biological processes. The pathways targeted by the differentially expressed miRNAs included cell adhesion, cell death and apoptosis, cardiovascular growth and development, and inflammation and immune response (Table 4 ). Table 4 miRNAs categorized according to the enriched biological pathways of target genes. Pathway Cell adhesion (n = 6) hsa-miR-146b-5p, hsa-miR-4488, hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, hsa-miR-3960 Cell death and apoptosis (n = 4) hsa-miR-146b-5p, hsa-miR-93-5p, hsa-miR-98-5p, let-7c-5p Cardiovascular growth and development (n = 3) hsa-miR-155-5p, hsa-miR-98-5p, let-7c-5p Inflammation and immune response (n = 3) hsa-miR-146b-5p, hsa-miR-155-5p, hsa-miR-93-5p DISCUSSION In this study using postmortem myocardial specimens from septic patients and non-septic controls, we found that eight miRNAs were up-regulated and two down-regulated in septic myocardial specimens compared to non-septic control specimens. The miRNAs regulating genes were related to cell adhesion, cardiovascular growth and development, cell viability, and inflammation and immune response. These findings may be associated with the development of septic cardiomyopathy. To the best of our knowledge, the present study is the first to investigate miRNA expression using FFPE cardiac tissue from patients with sepsis. A previous study investigated myocardial specimens obtained from patients who died due to sepsis but focused on messenger RNA (mRNA) expression in tissue samples that were snap frozen or placed on dry ice [ 19 ]. In that study, the mRNA expression pattern in the hearts of septic patients showed significant reductions in the levels of mRNAs associated with proteins responsible for cardiac energy production and contractility. In our study, the predicted miRNA target genes were associated with cardiovascular functions, cell adhesion, cell viability, and inflammation and immune response. Compared to controls, septic heart tissue samples had the highest up-regulated fold change in miR-146b-5p, which has been shown to be up-regulated in fibroblasts, endothelial cells, and macrophages under hypoxic conditions during cardiac injury and repair in the infarcted myocardium of mice and the serum of myocardial ischemia patients. [ 20 ]. Its predicted target genes regulate cell adhesion, apoptosis, and inflammation and immune response. MiR-146-5p up-regulation has been found to be cardioprotective and local inhibition of the miRNA significantly restores cardiac remodeling and function in both mouse and porcine infarct models [ 20 ]. In contrast to a mouse sepsis model of cecal ligation and puncture, a previous study reported that miR-146b protected against sepsis-induced myocardial injury by suppressing expression of IL-1β and myocardium apoptosis [ 21 ]. The largest increase in the mean counts in sepsis occurred with miR-12136, the specific function of which in sepsis or septic cardiomyopathy is not known, but the miRNA is involved in the process of mRNA translation [ 22 ]. We also found that miR-155-5p was up-regulated in septic patients’ myocardial tissue samples compared to controls. The biological function regulated by the predicted target genes of miR-155-5p include cardiovascular growth and development, as well as inflammation and immune response. Previous data indicate that miR-155-5p exerts a significant influence on multiple pathways associated with sepsis and renal damage, and a direct relationship exists between the activation of miR-155-5p and increased expression of pro-inflammatory IL-6 cytokine and chemoattractant IL-8 cytokine [ 23 , 24 ]. Earlier studies showed that up-regulation of miR-155 has cardioprotective properties [ 25 ]. In rat hearts, miR-155 levels were increased after intraperitoneal lipopolysaccharide injection. Inhibition of miR-155 significantly down-regulated the apoptosis of cardiomyocytes, whereas overexpression of miR-155 significantly up-regulated the apoptosis of cardiomyocytes and in vivo ejection fraction and significantly increased fractional shortening and heart weight [ 26 ]. MiR-155 has been shown to be highly up-regulated in endotoxemic mice, as well as in human endothelial cells, increasing endothelial leakage in a tight junction protein-dependent manner [ 27 ]. Septic endothelial dysfunction and capillary leakage cause tissue edema, and myocardial edema has been suggested to play a role in the pathophysiology of septic cardiomyopathy [ 4 ]. The upregulation of miR-155 in the present study may support these previous findings. In a Chinese study with elderly severe septic shock patients, miR-155 and miR-143 were found to be potential useful biomarkers with relation to serum TNF-α, IL-6, CK-MB, and cTnI levels [ 28 ]. We also identified increased levels of miR-21-3p in septic hearts. In a previous study, miR-21-3p was shown to be up-regulated in mouse hearts with LPS-induced cardiac dysfunction, and pharmacological inhibition of miR-21-3p led to preservation of cardiac function (preserved ejection fraction and fractional shortening) and improved survival [ 29 ]. The same study also found that miR-21-3p levels were significantly increased in the plasma of sepsis patients with cardiac dysfunction compared to patients without septic cardiac dysfunction, but the mechanistic link between miR-21-3p and septic cardiac dysfunction in humans has not been shown. Overexpression of miR-21-3p has also been shown to exacerbate myocardial inflammation, whereas downregulation suppresses cardiomyocyte apoptosis in LPS-treated rats [ 30 ]. In our series, the expression of vascular smooth muscle regulator (miR-4787-5p) was up-regulated compared to controls. MiR-4787-5p regulates vascular smooth muscle cell apoptosis, and its overexpression has been shown to have diagnostic value in patients with acute aortic dissection [ 31 ]. Our study demonstrated that miR-320c, miR-320d, miR-3960, and miR-4488 were up-regulated in heart tissue specimens. Their predicted target genes regulate genes related to cell adhesion. However, studies of these miRNAs are sparse. In an experimental study, miR-4488 was found to inhibit the accumulation of inflammatory proteins in venous endothelial cells (vECs) [ 32 ]. MiR-451a had the largest difference in mean counts and highest fold decrease in septic heart specimens compared to controls. A rat model has shown that miR-451a expression decreases in ischemia reperfusion injury, and up-regulation of miR-451a in myocardial tissue reduces the area of myocardial infarction, attenuates myocardial injury, and reduces myocardial cell apoptosis [ 33 ]. Human studies have shown that miR-451a expression is elevated in the plasma of patients with acute myocardial infarction compared with unstable coronary disease and healthy control groups [ 34 ], ánd that miR-451 has a regulatory role in ischemic heart injury [ 35 ]. The decreased miR-451a count in septic hearts compared with controls in the present study may support the assumption that myocardial dysfunction in sepsis is not caused by ischemia. MiR-144-5p was also down-regulated in sepsis and has been shown to be associated with the macrophage response to vascular inflammation [ 36 ]. Clinical significance As miRNAs have tissue-specific expression and can be secreted into blood, they could offer a biomarker tool for detecting myocardial dysfunction or possibly help guide treatment and monitor the treatment response in sepsis. Potential clinical use in diagnostics or prognostication of miRNA blood levels in the ICU setting would require accurate, rapid, and low-cost assays with multimarker panels of numerous miRNAs because of the heterogenous nature of the septic response. It is logical to think that the use of miRNAs as biomarkers will become routine following technological development [ 37 ]. In addition to biomarkers, the first animal studies imply that miRNAs may offer possible therapeutic interventions through restoration of the dysregulated immune system [ 38 ]. Limitations We were not able to differentiate distributive shock and septic cardiomyopathy. It would have required systematic echocardiography studies or other methods to record cardiac function, such as routine use of a pulmonary artery catheter. However, these two forms of cardiovascular dysfunction often coexist. Furthermore, the collection of myocardial tissue samples was not standardized, as the autopsies were performed for clinical diagnostic purposes. The selected control subjects were heterogeneous, deceased due to traffic accident, self-inflicted injury, or sudden cardiac death. They were matched for age, gender, and history of coronary artery disease, and there was no evidence of infection. Due to the small sample size and wide 95% CIs for differences in the mean miRNA counts, we chose a 2-fold increase as significant. As far as we know, there is no scientifically established clinically meaningful difference in each miRNA. MiRNA profiling studies have demonstrated that even subtle alterations in miRNA expression, such as a 1.5-fold difference, may exert a notable influence on cellular biology [ 39 ]. Finally, due to the small sample size, the reported associations cannot reliably be drawn as causal. The results must be interpreted as observational and hypothesis-generating. Despite its limitations, our study brings new insight into the cardiac response in sepsis at the organ level. Several miRNAs seem to be integrated in the pathophysiology of the septic myocardial response, either promoting or inhibiting cardiac damage. The up-regulated miRNAs were related to endothelial adhesion, inflammation, and apoptosis, and the down-regulated miRNAs to cardiovascular functions, cell viability, and immune response. Further studies are needed to explore whether soluble miRNAs may serve as biomarkers or prognostic tools for septic myocardial dysfunction. Conclusions Several regulatory miRNAs were up- or down-regulated in the myocardial tissue of septic patients compared to non-septic subjects. The predicted target genes of miRNAs were associated with biological functions related to cardiovascular functions, cell viability, cell adhesion, regulation of inflammatory and immune response, and endothelial response. Further studies are needed to explore whether these findings are associated with the development of septic cardiomyopathy. Abbreviations APACHE II Acute Physiology and Chronic Health Evaluation II BMI body mass index CI confidence interval CK-MB creatine kinase myocardial band cTnI cardiac troponin I CRP C-reactive protein FDR false discovery rate FFPE formalin-fixed paraffin-embedded ICU Intensive Care Unit IL interleukin IPA Ingenuity Pathway Analysis Log2FC logarithmic fold change LPS lipopolysaccharide miRNA micro RNA mRNA messenger RNA PCT procalcitonin RNA ribonucleic acid RPM reads per million SD standard deviation SOFA Sequential Organ Failure Assessment TNF tumor necrosis factor vEC venous endothelial cell Declarations Ethics approval The study protocol was approved by the hospital administration and ethics committee of Oulu University Hospital and the Northern Ostrobothnia Hospital District (72/2021; 33/2021). All research was performed in accordance with relevant guidelines and regulations in accordance with the Declaration of Helsinki. The specimens were collected during autopsy for routine histological examination and preserved in the Biobank Borealis of Northern Finland. For the present study, the specimens were obtained with sample donation permission from the scientific board committee of the Biobank of Northern Finland. The use of myocardial tissue obtained from the medicolegal autopsies as case control samples was approved by the Finnish Institute for Health and Welfare (THL/873/5.05.00/2023; THL/697/5.05.00/2017). Availability of data and materials All data generated or analyzed during this study are included in this published article. Competing interests The authors declare no competing interests. Funding This study was financially supported by the State Funding for University level Health Research, Oulu University Hospital, Wellbeing Services, County of North Ostrobothnia. Authors’ contributions PL, TS, HS, HS, RK, LP, JK, and TAK participated in the study design. PL, TS, and TAK collected the sepsis case samples. LP and KP collected and prepared the control tissue samples. SS and SS provided the laboratory analyses and performed the statistical analyses. PL, TS, RK, and TAK drafted the manuscript. All authors interpreted the data, helped to form the scientific content of the manuscript, and read and approved the final manuscript. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801-10. Ince C, Mayeux PR, Nguyen T, Gomez H, Kellum JA, Ospina-Tascón GA, et al. The endothelium in sepsis. Shock. 2016;45(3):259-70. Joffre J, Hellman J, Ince C, Ait-Oufella H. Endothelial responses in sepsis. Am J Respir Crit Care Med. 2020;202(3):361-70. Boissier F, Aissaoui N. Septic cardiomyopathy: diagnosis and management. J Intensive Med. 2022;2:8-16. Bartel DP. Metazoan microRNAs. Cell. 2018;173(1):20-51. Antonakos N, Gilbert C, Théroude C, Schrijver IT, Roger T. Modes of action and diagnostic value of miRNAs in sepsis. Front Immunol. 2022;13:951798. Formosa A, Turgeon P, Dos Santos CC. Role of miRNA dysregulation in sepsis. Mol Med. 2022;28(1):99. Wu M, LI G, Wang W, Ren H. Emerging roles of microRNAs in septic cardiomyopathy. Front Pharmacol. 2023:14:1181372. Shan Z, Qin S, Li W, et al. An endocrine genetic signal between blood cells and vascular smooth muscle cells: role of microRNA-223 in smooth muscle function and atherogenesis. J Am Coll Cardiol. 2015;65:2526–37. Jansen F, Stumpf T, Proebsting S, et al. Intercellular transfer of miR-126-3p by endothelial microparticles reduces vascular smooth muscle cell proliferation and limits neointima formation by inhibiting LRP6. J Mol Cell Cardiol. 2017;104:43–52. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29. Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine. Crit Care Med. 1998;26(11):1793-800. Li J, Smyth P, Flavin R, Cahill S, Denning K, Aherne S, et al. Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 2007;7:36. Siebolts U, Varnholt H, Drebber U, et al. Tissues from routine pathology archives are suitable for microRNA analyses by quantitative PCR. J Clin Pathol. 2009;62:84-8. Kakimoto Y, Kamiguchi H, Ochiai E, Satoh F, Osawa M. MicroRNA stability in postmortem FFPE tissues: quantitative analysis using autoptic samples from acute myocardial infarction patients. PLoS One. 2015;10:e0129338. Kakimoto Y, Tanaka M, Kamiguchi H, Ochiai E, Osawa M. MicroRNA stability in FFPE tissue samples: dependence on GC content. PLoS One. 2016;11:e0163125. Azzalini E, De Martino E, Fattorini P, Canzonieri V, Stanta G, Bonin S. Reliability of miRNA analysis from fixed and paraffin-embedded tissues. Int J Mol Sci. 2019;20:4819. Diendorfer A, Khamina K, Pultar M, Hackl M. miND (miRNA NGS Discovery Pipeline): a small RNA-Seq analysis pipeline and report generator for microRNA biomarker discovery studies. F1000Res. 2022;11(233). Matkovich S, Al Khiami B, Efimov I, Evans S, Vader J, Jain A, et al. Widespread down-regulation of cardiac mitochondrial and sarcomeric genes in patients with sepsis. Crit Care Med. 2017;45:407-14. Liao Y, Li H, Cao H, Dong Y, Gao L, Liu Z, et al. Therapeutic silencing miR-146b-5p improves cardiac remodeling in a porcine model of myocardial infarction by modulating the wound reparative phenotype. Protein Cell. 2021;12(3):194–212. Wang X, Yu Y. MiR-146b protect against sepsis induced mice myocardial injury through inhibition of Notch1. J Mol Histol. 2018;49(4):411-7. Garg P, Jamal F, Srivastava P. Deciphering the role of precursor miR-12136 and miR-8485 in the progression of intellectual disability (ID). IBRO Neurosci Rep. 2022;13:393-401. Petejova N, Martinek A, Zadrazil J, Klementa V, Pribylova L, Bris R, et al. Expression and 7-day time course of circulating microRNAs in septic patients treated with nephrotoxic antibiotic agents. BMC Nephrol. 2022;23:111. Pfeiffer D, Roßmanith E, Lang I, Falkenhagen D. MiR-146a, miR-146b, and miR-155 increase expression of IL-6 and IL-8 and support HSP10 in an in vitro sepsis model. PLoS ONE. 2017;12:e0179850. Manetti AC, Maiese A, Di Paolo M, De Matteis A, La Russa R, Turillazzi E, et al. MicroRNAs and sepsis-induced cardiac dysfunction: a systematic review. Int J Mol Sci. 2020;22(1):321. Lin Y, Hu J, Chen J, Chen S, Cai Y, Lin C. MiR-155 protects against sepsis-induced cardiomyocyte apoptosis via activation of NO/cGMP signaling pathway by eNOS. Trop J Pharm Res. 2022;21(9):1851-8. Etzrodt V, Idowu TO, Schenk H, Seeliger B, Prasse A, Thamm K, et al. Role of endothelial microRNA 155 on capillary leakage in systemic inflammation. Crit Care. 2021;25(1):76. Dou H, Hu F, Wang W, Ling L, Wang D, Liu F. Serum MiR-155 and MiR-143 can be used as prognostic markers for severe sepsis/septic shock in the elderly. Int J Clin Exp Med. 2020;13(6):3771-80. Wang H, Bei Y, Shen S, Huang P, Shi J, Zhang J, et al. miR-21-3p controls sepsis-associated cardiac dysfunction via regulating SORBS2. J Mol Cell Cardiol. 2016:94:43-53. Gong M, Tao L, Li X. MicroRNA-21-3p/Rcan1 signaling axis affects apoptosis of cardiomyocytes of sepsis rats. Gen Physiol Biophys. 2023;42(3):217-27. Wang L, Zhang S, Xu Z, Zhang J, Li L, Zhao G. The diagnostic value of microRNA-4787-5p and microRNA-4306 in patients with acute aortic dissection. Am J Transl Res. 2017;9(11):5138-49. Shao-Yu Fang S-Y, Huang C-W, Huang T-C, Yadav A, Chiu J-J, Wu C-C. Reduction in microRNA-4488 expression induces NFκB translocation in venous endothelial cells under arterial flow. Cardiovasc Drugs Ther. 2021;35(1):61-71. Cao J, Da Y, Li H, Peng Y, Hu X. Upregulation of microRNA-451 attenuates myocardial I/R injury by suppressing HMGB1. PLoS One. 2020;15(7):e0235614. Xu L, Tian L, Yan Z, Wang J, Xue T, Sun Q. Diagnostic and prognostic value of miR-486-5p, miR-451a, miR-21-5p and monocyte to high-density lipoprotein cholesterol ratio in patients with acute myocardial infarction. Heart Vessels. 2023;38(3):318-31. Deng HY, He ZY, Dong ZC, Zhang YL, Han X, Li HH. MicroRNA-451a attenuates angiotensin II-induced cardiac fibrosis and inflammation by directly targeting T-box1. Physiol Biochem. 2022;78(1):257-69. Chimnonso PO, Ipe J, Simpson E, Liu Y, Skaar TC, Kreutz RP. MicroRNA sequencing in patients with coronary artery disease: considerations for use as biomarker for thrombotic risk. Clin Transl Sci. 2022;15(8):1946–58. Condrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cretoiu D, et al. miRNAs as biomarkers in disease: latest findings regarding their role in diagnosis and prognosis. Cells. 2020;9(2):27. Dragomir MP, Fuentes-Mattei E, Winkle M, Okubo K, Bayraktar R, Knutsen E, et al. Anti-miR-93-5p therapy prolongs sepsis survival by restoring the peripheral immune response. J Clin Invest. 2023;133(14):e158348. Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10:R64. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Aug, 2024 Reviews received at journal 15 Aug, 2024 Reviewers agreed at journal 05 Aug, 2024 Reviews received at journal 10 Jun, 2024 Reviewers agreed at journal 30 May, 2024 Reviewers invited by journal 30 May, 2024 Editor assigned by journal 30 May, 2024 Editor invited by journal 23 May, 2024 Submission checks completed at journal 23 May, 2024 First submitted to journal 21 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4455151","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309809355,"identity":"43ad87ef-ed7d-4f13-89e5-1ee632e27d4f","order_by":0,"name":"Pasi Lehto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJCCAwwFDAkMDMwHGIAMBvZmorQYMCTwMLABtRkwMPAcJsoesBYeA4iWAwQUm7Mffwi0xSbPnr3n42Meg8MMPOwEtFj25BgAtaQV8/Cc3WwM1sJMyEkHckB+OZzYI5G7TRqkxZ6glvPPHwC1/AdqyXkmTZwtNxJADjsA0sJGnBbLGW8MDiQYJCf2nDlmbDjHIJ2HoBZz/vTHHz5U2CW2tzc/fPCmwlqOh/8AAYeBiAQkAR4CdkC1jIJRMApGwSjACwBnGzzcxP0lQwAAAABJRU5ErkJggg==","orcid":"","institution":"Oulu University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Pasi","middleName":"","lastName":"Lehto","suffix":""},{"id":309809358,"identity":"4dfd8109-a2c8-4213-9535-d15508c8c4a8","order_by":1,"name":"Taru Saukko","email":"","orcid":"","institution":"Oulu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Taru","middleName":"","lastName":"Saukko","suffix":""},{"id":309809362,"identity":"353a79c1-68bd-4750-a043-b65509870bbb","order_by":2,"name":"Hanna Säkkinen","email":"","orcid":"","institution":"Oulu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Säkkinen","suffix":""},{"id":309809367,"identity":"a6e19da6-fb90-4d8a-99ac-0f85cc35247b","order_by":3,"name":"Hannu Syrjälä","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Hannu","middleName":"","lastName":"Syrjälä","suffix":""},{"id":309809369,"identity":"702dfa89-60eb-4e98-ba13-13af2de3e195","order_by":4,"name":"Risto Kerkelä","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Risto","middleName":"","lastName":"Kerkelä","suffix":""},{"id":309809371,"identity":"f186a024-736c-4b3b-9834-7be19179a02f","order_by":5,"name":"Sini Skarp","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Sini","middleName":"","lastName":"Skarp","suffix":""},{"id":309809372,"identity":"e9179efb-8af6-4ac4-b48b-9242e7796dea","order_by":6,"name":"Samu Saarimäki","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Samu","middleName":"","lastName":"Saarimäki","suffix":""},{"id":309809373,"identity":"b8182115-bc16-421d-b175-687d69632dea","order_by":7,"name":"Lasse Pakanen","email":"","orcid":"","institution":"Oulu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lasse","middleName":"","lastName":"Pakanen","suffix":""},{"id":309809374,"identity":"06557ad9-545d-4f7d-bfb9-28b083ad502d","order_by":8,"name":"Katja Porvari","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Porvari","suffix":""},{"id":309809375,"identity":"821b88c4-f9a4-473a-b9ce-b435af22a666","order_by":9,"name":"Jaana Karhu","email":"","orcid":"","institution":"Oulu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jaana","middleName":"","lastName":"Karhu","suffix":""},{"id":309809376,"identity":"45d7190a-d450-4361-b7b1-466057510d0a","order_by":10,"name":"Tero Ala-Kokko","email":"","orcid":"","institution":"Oulu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tero","middleName":"","lastName":"Ala-Kokko","suffix":""}],"badges":[],"createdAt":"2024-05-21 13:26:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4455151/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4455151/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-81114-6","type":"published","date":"2024-11-27T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70382682,"identity":"af2b7955-3b77-4b60-bc7e-2b4b550f3dd7","added_by":"auto","created_at":"2024-12-02 16:29:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":762108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4455151/v1/88546469-6f5f-4db7-b8d1-a9597bd5f860.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Postmortem analyses of myocardial microRNA expression in sepsis ","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSepsis is a life-threatening dysregulated host immune response to infection that leads to multisystem organ damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Pathophysiological features include a complex network of pro-inflammatory responses, immune suppression, and endothelial dysfunction and cell damage from uncontrolled inflammation, which includes myocardial inflammatory responses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This systemic immune response leads to septic shock with circulatory maldistribution associated with peripheral vasodilation, arterial and capillary shunting, and sepsis-induced myocardial dysfunction leading to organ damage and high risk of death. Septic cardiomyopathy has been defined as reversible acute uni- or bi-ventricular systolic or diastolic dysfunction with reduced contractility not caused by ischemic coronary heart disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) are a class of non-coding RNA genes that regulate mRNA expression to affect various physiological processes through the adjustment of protein levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. MiRNAs regulate multiple pathways that form a complex network. Single miRNAs can target hundreds of genes, inhibiting or promoting inflammatory and innate immune responses, a key role in sepsis-induced endothelial and organ dysfunction, including sepsis-related myocardial dysfunction [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, circulating miRNAs can act as signaling molecules and induce hormone-like responses in target cells and tissues [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, human studies on organ-specific cardiac miRNA tissue levels in sepsis are lacking. We were interested in the expression of miRNA in postmortem myocardial specimens of critically ill septic patients and non-septic controls.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThis retrospective observational study was conducted at Oulu University Hospital, Oulu, Finland, an academic tertiary referral hospital. The study protocol was approved by the hospital administration and the ethics committee of Oulu University Hospital and the Northern Ostrobothnia Hospital District (72/2021; 33/2021). All research was performed in accordance with relevant guidelines and regulations in accordance with the Declaration of Helsinki. The use of myocardial tissue obtained from the medicolegal autopsies as case control samples was approved by the Finnish Institute for Health and Welfare (THL/873/5.05.00/2023; THL/697/5.05.00/2017).\u003c/p\u003e \u003cp\u003eAll adult intensive care unit (ICU) patients who died with sepsis during the years 2015\u0026ndash;2020 and underwent clinical autopsy with available myocardial specimens were included in this study. The specimens were collected during autopsy for routine histological examination and preserved in the Biobank Borealis of Northern Finland. For the present study, the specimens were obtained with sample donation permission from the scientific board committee of the Biobank of Northern Finland.\u003c/p\u003e \u003cp\u003eMyocardial tissue samples for non-septic controls were collected during medicolegal autopsies at the Finnish Institute for Health and Welfare. Finnish law requires a medicolegal autopsy to be carried out when the death is caused by non-natural conditions, such as accident, suicide, or homicide, or when the death is unexpected. The selected control subjects had either trauma or coronary artery disease (sudden cardiac death) as the underlying cause of death, and they were matched for age, sex, and presence of coronary artery disease.\u003c/p\u003e \u003cp\u003eSepsis was defined according to the American College of Chest Physicians/Society of Critical Care Medicine Criteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. All ICU sepsis patients were treated by a multidisciplinary team of intensivists and infectious disease specialists in our 26-bed, closed adult ICU. Intensive care treatment was performed according to normal ICU protocols and sepsis guidelines. The decision to perform a postmortem examination was made at the discretion of the treating physician or following a request from the next of kin.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical data collection\u003c/h3\u003e\n\u003cp\u003eClinical parameters were collected from the ICU clinical data management system database (Centricity Critical Care, Clinisoft, GE Healthcare). Illness severity was determined by the Acute Physiology and Chronic Health (APACHE II) score, which was recorded on admission. Organ failure was defined by the Sequential Organ Failure Assessment (SOFA) score, which is commonly used in ICU settings [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Data were obtained regarding age, sex, focus of infection, chronic illnesses, vasoactive use, and other ICU treatments, as well as the length of stay in the ICU and hospital. Clinical laboratory and microbiological samples were analyzed by commercially available laboratory methods in the hospital\u0026rsquo;s accredited central laboratory (NordLab, Oulu University Hospital, Oulu, Finland). The control tissue samples were linked to the subjects\u0026rsquo; limited health data, which included age, sex, and major chronic diseases.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAutopsy material collection and tissue sample preparation\u003c/h2\u003e\n \u003cp\u003eThe autopsies of the sepsis cases were performed by the Department of Pathology in Oulu University Hospital. Following the cessation of vital functions, post-mortem preparations were conducted within the confines of the intensive care ward prior to the cadaver\u0026apos;s transfer to the morgue. The time at ambient room temperature typically lasted a couple of hours. At the morgue, cadavers were stored at a temperature of 4\u0026deg;C. The median time from death to autopsy was 5 days (range: 3\u0026ndash;6 days). During the autopsy, cardiac specimens were routinely collected from the septum and left ventricular free wall. Standard procedures were used for preparation. Specimens were fixed in 10% formalin for approximately 24 hours. After fixation, the specimens were processed in a tissue processor, dehydrated in 100% alcohol, and cleared with xylene before being embedded in paraffin and stored at room temperature. The medicolegal autopsies of the control cases were performed in the Forensic Medicine Unit, Finnish Institute for Health and Welfare, Oulu, Finland, and the tissue preparations at the Department of Forensic Medicine, University of Oulu, Oulu, Finland. The specimen preparation was done according to the standard procedures mentioned above with the main difference of an uncertain time in temperatures above 4\u0026deg;C before forensic investigation of the cadaver.\u003c/p\u003e\n \u003cp\u003eTissue samples were preserved as formalin-fixed paraffin-embedded (FFPE) blocks due to their excellent tissue morphology preservation over time. This method prevents tissue autolysis, enabling histological analysis. Isolating nucleic acids from these samples is important for understanding disease mechanisms at the genomic level. MiRNAs are very short (18\u0026ndash;23 nucleotides) and less affected by fixation/embedding, allowing their recovery from FFPE samples even after many years, possibly decades. The expression levels of isolated miRNAs are directly comparable in both frozen and FFPE tissue samples [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. FFPE cardiac tissue specimens and the quality of cardiac tissue specimens were verified by hematoxylin-eosin staining and microscopic analysis before being sectioned for miRNA analysis, using a fresh microtome blade for each block.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA isolation and miRNA analysis\u003c/h2\u003e\n \u003cp\u003eRNA was isolated from FFPE cardiac samples using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Invitrogen). The RealSeq Single Index kit (RealSeq Biosciences) was used for library preparation, and sequencing was performed on Novaseq SP100 (Illumina) using single end reads. Sequencing data were analyzed using the miND\u0026reg; analysis pipeline [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. The quality of the NGS data was evaluated using fastQC v0.11.9 and multiQC v1.14. Reads were adapter trimmed using cutadapt v3.3 and mapped to GRCh38.p12 and miRBase v22.1 with bowtie v1.3.0 and miRDeep2 v2.0.1.2.\u003c/p\u003e\n \u003cp\u003eDifferentially expressed miRNAs were identified using edgeR v3.32. The target genes for each miRNA were identified utilizing the miRNA target filter within Ingenuity Pathway Analysis (IPA) software (Qiagen) by incorporating target predictions from TargetScan human, Ingenuity Expert Findings, miRecords, and Tarbase databases. Predicted miRNA targets were retrieved by IPA software, and pathway enrichment and classification were performed using PantherDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.pantherdb.org/\u003c/span\u003e\u003c/span\u003e). The target filter prioritized targets that were either experimentally observed or predicted with high confidence, ensuring the exclusion of less reliable predictions from the target gene lists. Enrichment analysis for the target genes of each miRNA was performed using PantherDB\u0026rsquo;s overrepresentation test (GO biological process complete).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eFor continuous variables, we used medians with 25th -75th percentiles or means with standard deviations; and counts (%) were used for categorical variables. Comparisons of continuous variables were analyzed using the t-test for independent samples. We also report the 95% confidence intervals (CIs) of the differences between the mean miRNA counts.\u003c/p\u003e\n \u003cp\u003eMiRNAs were considered up- or down-regulated if the FDR was \u0026lt;\u0026thinsp;0.05 and logarithmic fold change (log2FC)\u0026thinsp;\u0026ge;\u0026thinsp;1 for up-regulated or log2FC \u0026le; -1 for down-regulated miRNAs. The mean difference and 95% CI was calculated for normalized read counts. Analyses were performed using SPSS for Windows software (IBM SPSS Statistics for Windows, version 25.0; IBM Corp., Armonk, New York).\u003c/p\u003e\n \u003cp\u003eThis study was designed to be descriptive, and the sample size was feasible for the autopsy material available and judged to be adequate to provide reasonable data. Two-tailed P-values were calculated and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant in all analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe identified a total of 15 patients deceased due to sepsis and who had autopsies performed with myocardial tissue samples available. All of the myocardial specimens for the analysis in this study were obtained from the left ventricular free wall.\u003c/p\u003e \u003cp\u003eIn the sepsis group, the foci of infection was pulmonary (47%), gastrointestinal (33%), skin (13%), or urinary (7%). Ten (67%) of the sepsis cases had a laboratory confirmed positive blood culture. The median stay in the ICU was 1.3 days, whereas hospital stay was 2.6 days. Cause of death for the 15 control cases was accidental trauma (n\u0026thinsp;=\u0026thinsp;3) or self-inflicted injury (n\u0026thinsp;=\u0026thinsp;2) leading to death on-site, or sudden cardiac arrest (n\u0026thinsp;=\u0026thinsp;10) with coronary artery disease being the underlying cause of death. None had signs of acute infection at autopsy. There were no significant differences regarding gender, age, or major chronic illnesses between the septic and control subjects (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDemographics of the controls and patients with myocardial autopsy specimens.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 [57.5\u0026ndash;77.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 [58.5\u0026ndash;77.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illnesses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\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\u003eData are expressed as median [25th -75th percentile] or n (%).\u003c/p\u003e \u003cp\u003eIn both groups, 67% of the subjects had coronary artery disease and the median age was 72 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eDemographics and clinical data for sepsis patients with myocardial autopsy specimens.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (57\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (22\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity of disease scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHEII, admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (19\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA, admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFocus of infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory values\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 (1.4\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (99\u0026ndash;353)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT, admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1 (3.3\u0026ndash;61.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, highest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4 (5.9\u0026ndash;16.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU treatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInotropes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal replacement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (1.1\u0026ndash;12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3(0.5\u0026ndash;6.8)\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\u003eData are expressed as median (25th \u0026minus;\u0026thinsp;75th PCT) or n (%).\u003c/p\u003e \u003cp\u003eA total of 1753 miRNAs were detected in the miRNA sequencing, 267 of which were abundantly expressed in the samples and subjected to differential expression analysis. We identified 32 differentially expressed miRNAs in the myocardial specimens of sepsis cases compared to controls (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eDifferentially expressed miRNAs in the myocardia of sepsis patients and controls.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean RPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003elog2(FC)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-10400-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-61\u0026ndash;707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\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\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-4488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-143\u0026ndash;983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\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\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-3196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-4\u0026ndash;171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-4508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-10\u0026ndash;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-146b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e248\u0026ndash;995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-3960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e26\u0026ndash;896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-320d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-320c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14\u0026ndash;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\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\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-4787-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\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\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u0026ndash;118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-21-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e32\u0026ndash;236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-12136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1065\u0026ndash;4800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-155-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e20\u0026ndash;147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\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\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-320b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16\u0026ndash;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-4449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14\u0026ndash;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\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\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-let-7c-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e229\u0026ndash;954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-652-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e431\u0026ndash;1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-98-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e296\u0026ndash;915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-28-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e204\u0026ndash;507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-140-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-780 \u0026ndash; -76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-181a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-54 \u0026ndash; -8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-423-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1425 \u0026ndash; -140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-93-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1737 \u0026ndash; -173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-181c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-42 \u0026ndash; -8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-127-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-230 \u0026ndash; -56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-199b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1164 \u0026ndash; -187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-486-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2317 \u0026ndash; -812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-199a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2323 \u0026ndash; -489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-654-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\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\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-39 \u0026ndash; -12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-136-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-119 \u0026ndash; -39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-363-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-284 \u0026ndash; -101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-144-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-407 \u0026ndash; -156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-451a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-44281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-72335 \u0026ndash; -16226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean, standard deviation (SD), and mean difference with 95% confidence interval (CI) were calculated from normalized miRNA read counts (reads per million, RPM). Differential expression analysis was performed for miRNA sequencing data using edgeR and is presented as the false discovery rate (FDR) and log2 of fold change (FC).\u003c/p\u003e \u003cp\u003eEight miRNAs had log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 and a significant change in the mean difference based on the 95% CI: miR-12136, miR-146b-5p, miR-155-5p, miR-21-3p, miR-320c, miR-320d, miR-3960, miR-4488, and miR-4787-5p. MiR-12136 had the largest increase in mean counts in sepsis hearts, and miR-146b-5p had the highest fold change. Two miRs, miR-144-5p and miR-451a, had log2FC \u0026lt; -1 and a significant change in the mean difference based on the 95% CI. MiR-451a had the largest difference in mean counts and highest fold decrease. Enrichment analysis was performed for the predicted target genes of the 32 differentially expressed miRNAs to identify regulated biological processes. The pathways targeted by the differentially expressed miRNAs included cell adhesion, cell death and apoptosis, cardiovascular growth and development, and inflammation and immune response (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003emiRNAs categorized according to the enriched biological pathways of target genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCell adhesion (n\u0026thinsp;=\u0026thinsp;6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-146b-5p, hsa-miR-4488, hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, hsa-miR-3960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCell death and apoptosis (n\u0026thinsp;=\u0026thinsp;4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-146b-5p, hsa-miR-93-5p, hsa-miR-98-5p, let-7c-5p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCardiovascular growth and development (n\u0026thinsp;=\u0026thinsp;3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-155-5p, hsa-miR-98-5p, let-7c-5p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInflammation and immune response (n\u0026thinsp;=\u0026thinsp;3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-146b-5p, hsa-miR-155-5p, hsa-miR-93-5p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study using postmortem myocardial specimens from septic patients and non-septic controls, we found that eight miRNAs were up-regulated and two down-regulated in septic myocardial specimens compared to non-septic control specimens. The miRNAs regulating genes were related to cell adhesion, cardiovascular growth and development, cell viability, and inflammation and immune response. These findings may be associated with the development of septic cardiomyopathy.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, the present study is the first to investigate miRNA expression using FFPE cardiac tissue from patients with sepsis. A previous study investigated myocardial specimens obtained from patients who died due to sepsis but focused on messenger RNA (mRNA) expression in tissue samples that were snap frozen or placed on dry ice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In that study, the mRNA expression pattern in the hearts of septic patients showed significant reductions in the levels of mRNAs associated with proteins responsible for cardiac energy production and contractility. In our study, the predicted miRNA target genes were associated with cardiovascular functions, cell adhesion, cell viability, and inflammation and immune response.\u003c/p\u003e \u003cp\u003eCompared to controls, septic heart tissue samples had the highest up-regulated fold change in miR-146b-5p, which has been shown to be up-regulated in fibroblasts, endothelial cells, and macrophages under hypoxic conditions during cardiac injury and repair in the infarcted myocardium of mice and the serum of myocardial ischemia patients. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Its predicted target genes regulate cell adhesion, apoptosis, and inflammation and immune response. MiR-146-5p up-regulation has been found to be cardioprotective and local inhibition of the miRNA significantly restores cardiac remodeling and function in both mouse and porcine infarct models [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast to a mouse sepsis model of cecal ligation and puncture, a previous study reported that miR-146b protected against sepsis-induced myocardial injury by suppressing expression of IL-1β and myocardium apoptosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The largest increase in the mean counts in sepsis occurred with miR-12136, the specific function of which in sepsis or septic cardiomyopathy is not known, but the miRNA is involved in the process of mRNA translation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also found that miR-155-5p was up-regulated in septic patients\u0026rsquo; myocardial tissue samples compared to controls. The biological function regulated by the predicted target genes of miR-155-5p include cardiovascular growth and development, as well as inflammation and immune response. Previous data indicate that miR-155-5p exerts a significant influence on multiple pathways associated with sepsis and renal damage, and a direct relationship exists between the activation of miR-155-5p and increased expression of pro-inflammatory IL-6 cytokine and chemoattractant IL-8 cytokine [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Earlier studies showed that up-regulation of miR-155 has cardioprotective properties [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In rat hearts, miR-155 levels were increased after intraperitoneal lipopolysaccharide injection. Inhibition of miR-155 significantly down-regulated the apoptosis of cardiomyocytes, whereas overexpression of miR-155 significantly up-regulated the apoptosis of cardiomyocytes and \u003cem\u003ein vivo\u003c/em\u003e ejection fraction and significantly increased fractional shortening and heart weight [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. MiR-155 has been shown to be highly up-regulated in endotoxemic mice, as well as in human endothelial cells, increasing endothelial leakage in a tight junction protein-dependent manner [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Septic endothelial dysfunction and capillary leakage cause tissue edema, and myocardial edema has been suggested to play a role in the pathophysiology of septic cardiomyopathy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The upregulation of miR-155 in the present study may support these previous findings. In a Chinese study with elderly severe septic shock patients, miR-155 and miR-143 were found to be potential useful biomarkers with relation to serum TNF-α, IL-6, CK-MB, and cTnI levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also identified increased levels of miR-21-3p in septic hearts. In a previous study, miR-21-3p was shown to be up-regulated in mouse hearts with LPS-induced cardiac dysfunction, and pharmacological inhibition of miR-21-3p led to preservation of cardiac function (preserved ejection fraction and fractional shortening) and improved survival [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The same study also found that miR-21-3p levels were significantly increased in the plasma of sepsis patients with cardiac dysfunction compared to patients without septic cardiac dysfunction, but the mechanistic link between miR-21-3p and septic cardiac dysfunction in humans has not been shown. Overexpression of miR-21-3p has also been shown to exacerbate myocardial inflammation, whereas downregulation suppresses cardiomyocyte apoptosis in LPS-treated rats [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our series, the expression of vascular smooth muscle regulator (miR-4787-5p) was up-regulated compared to controls. MiR-4787-5p regulates vascular smooth muscle cell apoptosis, and its overexpression has been shown to have diagnostic value in patients with acute aortic dissection [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study demonstrated that miR-320c, miR-320d, miR-3960, and miR-4488 were up-regulated in heart tissue specimens. Their predicted target genes regulate genes related to cell adhesion. However, studies of these miRNAs are sparse. In an experimental study, miR-4488 was found to inhibit the accumulation of inflammatory proteins in venous endothelial cells (vECs) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMiR-451a had the largest difference in mean counts and highest fold decrease in septic heart specimens compared to controls. A rat model has shown that miR-451a expression decreases in ischemia reperfusion injury, and up-regulation of miR-451a in myocardial tissue reduces the area of myocardial infarction, attenuates myocardial injury, and reduces myocardial cell apoptosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Human studies have shown that miR-451a expression is elevated in the plasma of patients with acute myocardial infarction compared with unstable coronary disease and healthy control groups [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], \u0026aacute;nd that miR-451 has a regulatory role in ischemic heart injury [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The decreased miR-451a count in septic hearts compared with controls in the present study may support the assumption that myocardial dysfunction in sepsis is not caused by ischemia. MiR-144-5p was also down-regulated in sepsis and has been shown to be associated with the macrophage response to vascular inflammation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical significance\u003c/h2\u003e \u003cp\u003eAs miRNAs have tissue-specific expression and can be secreted into blood, they could offer a biomarker tool for detecting myocardial dysfunction or possibly help guide treatment and monitor the treatment response in sepsis. Potential clinical use in diagnostics or prognostication of miRNA blood levels in the ICU setting would require accurate, rapid, and low-cost assays with multimarker panels of numerous miRNAs because of the heterogenous nature of the septic response. It is logical to think that the use of miRNAs as biomarkers will become routine following technological development [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition to biomarkers, the first animal studies imply that miRNAs may offer possible therapeutic interventions through restoration of the dysregulated immune system [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWe were not able to differentiate distributive shock and septic cardiomyopathy. It would have required systematic echocardiography studies or other methods to record cardiac function, such as routine use of a pulmonary artery catheter. However, these two forms of cardiovascular dysfunction often coexist. Furthermore, the collection of myocardial tissue samples was not standardized, as the autopsies were performed for clinical diagnostic purposes.\u003c/p\u003e \u003cp\u003eThe selected control subjects were heterogeneous, deceased due to traffic accident, self-inflicted injury, or sudden cardiac death. They were matched for age, gender, and history of coronary artery disease, and there was no evidence of infection. Due to the small sample size and wide 95% CIs for differences in the mean miRNA counts, we chose a 2-fold increase as significant. As far as we know, there is no scientifically established clinically meaningful difference in each miRNA. MiRNA profiling studies have demonstrated that even subtle alterations in miRNA expression, such as a 1.5-fold difference, may exert a notable influence on cellular biology [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Finally, due to the small sample size, the reported associations cannot reliably be drawn as causal. The results must be interpreted as observational and hypothesis-generating. Despite its limitations, our study brings new insight into the cardiac response in sepsis at the organ level. Several miRNAs seem to be integrated in the pathophysiology of the septic myocardial response, either promoting or inhibiting cardiac damage. The up-regulated miRNAs were related to endothelial adhesion, inflammation, and apoptosis, and the down-regulated miRNAs to cardiovascular functions, cell viability, and immune response. Further studies are needed to explore whether soluble miRNAs may serve as biomarkers or prognostic tools for septic myocardial dysfunction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSeveral regulatory miRNAs were up- or down-regulated in the myocardial tissue of septic patients compared to non-septic subjects. The predicted target genes of miRNAs were associated with biological functions related to cardiovascular functions, cell viability, cell adhesion, regulation of inflammatory and immune response, and endothelial response. Further studies are needed to explore whether these findings are associated with the development of septic cardiomyopathy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPACHE II Acute Physiology and Chronic Health Evaluation II\u003c/p\u003e\n\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eCI confidence interval\u003c/p\u003e\n\u003cp\u003eCK-MB creatine kinase myocardial band\u003c/p\u003e\n\u003cp\u003ecTnI cardiac troponin I\u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein\u003c/p\u003e\n\u003cp\u003eFDR false discovery rate\u003c/p\u003e\n\u003cp\u003eFFPE formalin-fixed paraffin-embedded\u003c/p\u003e\n\u003cp\u003eICU Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eIL interleukin\u003c/p\u003e\n\u003cp\u003eIPA Ingenuity Pathway Analysis\u003c/p\u003e\n\u003cp\u003eLog2FC logarithmic fold change\u003c/p\u003e\n\u003cp\u003eLPS lipopolysaccharide\u003c/p\u003e\n\u003cp\u003emiRNA micro RNA\u003c/p\u003e\n\u003cp\u003emRNA messenger RNA\u003c/p\u003e\n\u003cp\u003ePCT procalcitonin\u003c/p\u003e\n\u003cp\u003eRNA ribonucleic acid\u003c/p\u003e\n\u003cp\u003eRPM reads per million\u003c/p\u003e\n\u003cp\u003eSD standard deviation\u003c/p\u003e\n\u003cp\u003eSOFA Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eTNF tumor necrosis factor\u003c/p\u003e\n\u003cp\u003evEC venous endothelial cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;Ethics approval\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the hospital administration and ethics committee of Oulu University Hospital and the Northern Ostrobothnia Hospital District (72/2021; 33/2021). All research was performed in accordance with relevant guidelines and regulations \u0026nbsp;in accordance with the Declaration of Helsinki. The specimens were collected during autopsy for routine histological examination and preserved in the Biobank Borealis of Northern Finland. For the present study, the specimens were obtained with sample donation permission from the scientific board committee of the Biobank of Northern Finland. The use of myocardial tissue obtained from the medicolegal autopsies as case control samples was approved by the Finnish Institute for Health and Welfare (THL/873/5.05.00/2023; THL/697/5.05.00/2017).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the State Funding for University level Health Research, Oulu University Hospital, Wellbeing Services, County of North Ostrobothnia.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003ePL, TS, HS, HS, RK, LP, JK, and TAK participated in the study design. PL, TS, and TAK collected the sepsis case samples. LP and KP collected and prepared the control tissue samples. SS and SS provided the laboratory analyses and performed the statistical analyses. PL, TS, RK, and TAK drafted the manuscript. All authors interpreted the data, helped to form the scientific content of the manuscript, and read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801-10.\u003c/li\u003e\n\u003cli\u003eInce C, Mayeux PR, Nguyen T, Gomez H, Kellum JA, Ospina-Tasc\u0026oacute;n GA, et al. The endothelium in sepsis. Shock. 2016;45(3):259-70.\u003c/li\u003e\n\u003cli\u003eJoffre J, Hellman J, Ince C, Ait-Oufella H. Endothelial responses in sepsis. Am J Respir Crit Care Med. 2020;202(3):361-70.\u003c/li\u003e\n\u003cli\u003eBoissier F, Aissaoui N. Septic cardiomyopathy: diagnosis and management. J Intensive Med. 2022;2:8-16.\u003c/li\u003e\n\u003cli\u003eBartel DP. Metazoan microRNAs. Cell. 2018;173(1):20-51.\u003c/li\u003e\n\u003cli\u003eAntonakos N, Gilbert C, Th\u0026eacute;roude C, Schrijver IT, Roger T. Modes of action and diagnostic value of miRNAs in sepsis. Front Immunol. 2022;13:951798. \u003c/li\u003e\n\u003cli\u003eFormosa\u003csup\u003e \u003c/sup\u003eA, Turgeon\u003csup\u003e \u003c/sup\u003eP, Dos Santos CC. Role of miRNA dysregulation in sepsis. Mol Med. 2022;28(1):99.\u003c/li\u003e\n\u003cli\u003eWu M, LI G, Wang W, Ren H. Emerging roles of microRNAs in septic cardiomyopathy. Front Pharmacol. 2023:14:1181372.\u003c/li\u003e\n\u003cli\u003eShan Z, Qin S, Li W, et al. An endocrine genetic signal between blood cells and vascular smooth muscle cells: role of microRNA-223 in smooth muscle function and atherogenesis. J Am Coll Cardiol. 2015;65:2526\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eJansen F, Stumpf T, Proebsting S, et al. Intercellular transfer of miR-126-3p by endothelial microparticles reduces vascular smooth muscle cell proliferation and limits neointima formation by inhibiting LRP6. J Mol Cell Cardiol. 2017;104:43\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29. \u003c/li\u003e\n\u003cli\u003eVincent JL, de Mendon\u0026ccedil;a A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on \u0026quot;sepsis-related problems\u0026quot; of the European Society of Intensive Care Medicine. Crit Care Med. 1998;26(11):1793-800.\u003c/li\u003e\n\u003cli\u003eLi J, Smyth P, Flavin R, Cahill S, Denning K, Aherne S, et al. Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 2007;7:36. \u003c/li\u003e\n\u003cli\u003eSiebolts U, Varnholt H, Drebber U, et al. Tissues from routine pathology archives are suitable for microRNA analyses by quantitative PCR. J Clin Pathol. 2009;62:84-8.\u003c/li\u003e\n\u003cli\u003eKakimoto Y, Kamiguchi H, Ochiai E, Satoh F, Osawa M. MicroRNA stability in postmortem FFPE tissues: quantitative analysis using autoptic samples from acute myocardial infarction patients. PLoS One. 2015;10:e0129338.\u003c/li\u003e\n\u003cli\u003eKakimoto Y, Tanaka M, Kamiguchi H, Ochiai E, Osawa M. MicroRNA stability in FFPE tissue samples: dependence on GC content. PLoS One. 2016;11:e0163125.\u003c/li\u003e\n\u003cli\u003eAzzalini E, De Martino E, Fattorini P, Canzonieri V, Stanta G, Bonin S. Reliability of miRNA analysis from fixed and paraffin-embedded tissues. Int J Mol Sci. 2019;20:4819.\u003c/li\u003e\n\u003cli\u003eDiendorfer A, Khamina K, Pultar M, Hackl M. miND (miRNA NGS Discovery Pipeline): a small RNA-Seq analysis pipeline and report generator for microRNA biomarker discovery studies. F1000Res. 2022;11(233).\u003c/li\u003e\n\u003cli\u003eMatkovich S, Al Khiami B, Efimov I, Evans S, Vader J, Jain A, et al. Widespread down-regulation of cardiac mitochondrial and sarcomeric genes in patients with sepsis. Crit Care Med. 2017;45:407-14.\u003c/li\u003e\n\u003cli\u003eLiao Y, Li H, Cao H, Dong Y, Gao L, Liu Z, et al. Therapeutic silencing miR-146b-5p improves cardiac remodeling in a porcine model of myocardial infarction by modulating the wound reparative phenotype. Protein Cell. 2021;12(3):194\u0026ndash;212.\u003c/li\u003e\n\u003cli\u003eWang X, Yu Y. MiR-146b protect against sepsis induced mice myocardial injury through inhibition of Notch1. J Mol Histol. 2018;49(4):411-7. \u003c/li\u003e\n\u003cli\u003eGarg P, Jamal F, Srivastava P. Deciphering the role of precursor miR-12136 and miR-8485 in the progression of intellectual disability (ID). IBRO Neurosci Rep. 2022;13:393-401.\u003c/li\u003e\n\u003cli\u003ePetejova N, Martinek A, Zadrazil J, Klementa V, Pribylova L, Bris R, et al. Expression and 7-day time course of circulating microRNAs in septic patients treated with nephrotoxic antibiotic agents. BMC Nephrol. 2022;23:111.\u003c/li\u003e\n\u003cli\u003ePfeiffer D, Ro\u0026szlig;manith E, Lang I, Falkenhagen D. MiR-146a, miR-146b, and miR-155 increase expression of IL-6 and IL-8 and support HSP10 in an in vitro sepsis model. PLoS ONE. 2017;12:e0179850.\u003c/li\u003e\n\u003cli\u003eManetti AC, Maiese A, Di Paolo M, De Matteis A, La Russa R, Turillazzi E, et al. MicroRNAs and sepsis-induced cardiac dysfunction: a systematic review. Int J Mol Sci. 2020;22(1):321.\u003c/li\u003e\n\u003cli\u003eLin Y, Hu J, Chen J, Chen S, Cai Y, Lin C. MiR-155 protects against sepsis-induced cardiomyocyte apoptosis via activation of NO/cGMP signaling pathway by eNOS. Trop J Pharm Res. 2022;21(9):1851-8.\u003c/li\u003e\n\u003cli\u003eEtzrodt V, Idowu TO, Schenk H, Seeliger B, Prasse A, Thamm K, et al. Role of endothelial microRNA 155 on capillary leakage in systemic inflammation. Crit Care. 2021;25(1):76.\u003c/li\u003e\n\u003cli\u003eDou H, Hu F, Wang W, Ling L, Wang D, Liu F. Serum MiR-155 and MiR-143 can be used as prognostic markers for severe sepsis/septic shock in the elderly. Int J Clin Exp Med. 2020;13(6):3771-80.\u003c/li\u003e\n\u003cli\u003eWang H, Bei Y, Shen S, Huang P, Shi J, Zhang J, et al. miR-21-3p controls sepsis-associated cardiac dysfunction via regulating SORBS2. J Mol Cell Cardiol. 2016:94:43-53.\u003c/li\u003e\n\u003cli\u003eGong M, Tao L, Li X. MicroRNA-21-3p/Rcan1 signaling axis affects apoptosis of cardiomyocytes of sepsis rats. Gen Physiol Biophys. 2023;42(3):217-27.\u003c/li\u003e\n\u003cli\u003eWang L, Zhang S, Xu Z, Zhang J, Li L, Zhao\u003csup\u003e \u003c/sup\u003eG. The diagnostic value of microRNA-4787-5p and microRNA-4306 in patients with acute aortic dissection. Am J Transl Res. 2017;9(11):5138-49.\u003c/li\u003e\n\u003cli\u003eShao-Yu Fang S-Y, Huang C-W, Huang T-C, Yadav A, Chiu J-J, Wu C-C. Reduction in microRNA-4488 expression induces NF\u0026kappa;B translocation in venous endothelial cells under arterial flow. Cardiovasc Drugs Ther. 2021;35(1):61-71.\u003c/li\u003e\n\u003cli\u003eCao J, Da Y, Li H, Peng Y, Hu X.\u003csup\u003e \u003c/sup\u003eUpregulation of microRNA-451 attenuates myocardial I/R injury by suppressing HMGB1. PLoS One. 2020;15(7):e0235614.\u003c/li\u003e\n\u003cli\u003eXu\u003csup\u003e \u003c/sup\u003eL, Tian L, Yan Z, Wang J, Xue T, Sun Q. Diagnostic and prognostic value of miR-486-5p, miR-451a, miR-21-5p and monocyte to high-density lipoprotein cholesterol ratio in patients with acute myocardial infarction. Heart Vessels. 2023;38(3):318-31.\u003c/li\u003e\n\u003cli\u003eDeng HY, He ZY, Dong ZC, Zhang YL, Han X, Li HH. MicroRNA-451a attenuates angiotensin II-induced cardiac fibrosis and inflammation by directly targeting T-box1. Physiol Biochem. 2022;78(1):257-69.\u003c/li\u003e\n\u003cli\u003eChimnonso PO,\u003csup\u003e \u003c/sup\u003e Ipe J,\u003csup\u003e \u003c/sup\u003e Simpson E, Liu Y,\u003csup\u003e \u003c/sup\u003eSkaar TC, Kreutz RP. MicroRNA sequencing in patients with coronary artery disease: considerations for use as biomarker for thrombotic risk. Clin Transl Sci. 2022;15(8):1946\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eCondrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cretoiu D, et al. miRNAs as biomarkers in disease: latest findings regarding their role in diagnosis and prognosis. Cells. 2020;9(2):27.\u003c/li\u003e\n\u003cli\u003eDragomir MP, Fuentes-Mattei E, Winkle M, Okubo K, Bayraktar R, Knutsen E, et al. Anti-miR-93-5p therapy prolongs sepsis survival by restoring the peripheral immune response. J Clin Invest. 2023;133(14):e158348.\u003c/li\u003e\n\u003cli\u003eMestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10:R64.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microRNA, septic cardiomyopathy, sepsis","lastPublishedDoi":"10.21203/rs.3.rs-4455151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4455151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis can lead to myocardial depression, playing a significant role in sepsis pathophysiology, clinical care, and outcome. To gain more insight into the pathophysiology of the myocardial response in sepsis, we investigated the expression of microRNA in myocardial autopsy specimens in critically ill deceased with sepsis and non-septic controls.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e \u003cp\u003eIn this retrospective observational study, we obtained myocardial tissue samples collected during autopsy from adult patients deceased with sepsis (n\u0026thinsp;=\u0026thinsp;15) for routine histological examination. We obtained control myocardial tissue specimens (n\u0026thinsp;=\u0026thinsp;15) from medicolegal autopsies of cadavers whose cause of death was injury or who were found dead at home and the cause of death was coronary artery disease with sudden cardiac arrest. RNA was isolated from formalin-fixed paraffin- embedded (FFPE) cardiac samples using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Invitrogen). Differentially expressed miRNAs were identified using edgeR v3.32. MicroRNA was considered up- or down-regulated if the false discovery rate was \u0026lt;\u0026thinsp;0.05 and logarithmic fold change (log2FC)\u0026thinsp;\u0026ge;\u0026thinsp;1 for up-regulated or log2FC \u0026le; -1 for down-regulated miRNAs. The mean difference and 95% confidence interval (CI) was calculated for normalized read counts. Predicted miRNA targets were retrieved using Ingenuity Pathway Analysis (IPA) software, and pathway enrichment and classification were performed using PantherDB.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDifferential expression analysis identified a total of 32 miRNAs in the myocardial specimens. Eight miRNAs had a significant change in the mean difference based on the 95% CI, with the largest increase in mean counts in septic samples with hsa-miR-12136 and the highest fold change with hsa-miR-146b-5p. The threshold for down-regulated miRNAs in sepsis compared to controls was obtained with hsa-miR-144-5p and hsa-miR-451a, with the latter having the largest decrease in mean counts and fold decrease.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSeveral regulatory miRNAs were up- or down-regulated in the myocardial tissue of patients deceased with sepsis compared to non-septic subjects. The predicted target genes of miRNAs are associated with biological functions related to cardiovascular functions, cell viability, cell adhesion, and regulation of inflammatory and immune response.\u003c/p\u003e","manuscriptTitle":"Postmortem analyses of myocardial microRNA expression in sepsis ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 08:25:22","doi":"10.21203/rs.3.rs-4455151/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-16T05:56:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-15T07:23:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61476067719073017857000034642856589584","date":"2024-08-05T11:05:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-10T10:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230159154873483000365426421381294731075","date":"2024-05-31T02:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-30T08:59:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-30T08:54:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-23T08:50:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-23T08:45:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-21T13:25:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4bfaa269-983f-49c8-9e45-cf3fb549cea9","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T16:02:51+00:00","versionOfRecord":{"articleIdentity":"rs-4455151","link":"https://doi.org/10.1038/s41598-024-81114-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-27 15:57:46","publishedOnDateReadable":"November 27th, 2024"},"versionCreatedAt":"2024-06-04 08:25:22","video":"","vorDoi":"10.1038/s41598-024-81114-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-81114-6","workflowStages":[]},"version":"v1","identity":"rs-4455151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4455151","identity":"rs-4455151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}