Delicate and thin fibrous septa indicate a regression tendency in metabolic dysfunction-associated steatohepatitis patients with advanced fibrosis

Delicate and thin fibrous septa indicate a regression tendency in metabolic dysfunction-associated steatohepatitis patients with advanced fibrosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Delicate and thin fibrous septa indicate a regression tendency in metabolic dysfunction-associated steatohepatitis patients with advanced fibrosis Xiaofei Tong, Yameng Sun, Qianyi Wang, Xinyan Zhao, Wei Chen, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4392304/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Aug, 2024 Read the published version in Hepatology International → Version 1 posted 4 You are reading this latest preprint version Abstract Background and Aims: Metabolic dysfunction-associated steatohepatitis (MASH)-related fibrosis is reversible. However, the dynamic morphology change in fibrosis regression remains unclear. We aim to explore the morphological characteristics of fibrosis regression in advanced MASH patients. Methods Clinical and histological data of 79 biopsy-proved MASH patients with advanced fibrosis (F3-F4) were reviewed. The second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) image technology was used to quantitively identify the R (regressive) septa from P (progressive) septa and PS (perisinusoidal) fibrosis. Non-invasive tests were used to compare the fibrosis level of the with and without R septa groups. Transcriptomics was used to explore hub genes and the underlying mechanism of the formation of R septa. Results The R septa were different from the P septa and PS fibrosis in detail collagen quantitation identified by SHG/TPEF technology. The R septa were found in MASH fibrosis-regressed patients, which met the definition of the “Beijing classification”. Therefore, patients were divided into two groups according to septa morphology: with R septa ( n = 10, 12.7%), and without R septa ( n = 69, 87.3%). Patients with R septa had lower values in most non-invasive tests, especially for liver stiffness (12.3 vs. 19.4 kPa, p = 0.010), and FAST (FibroScan®-AST) score (0.43 vs. 0.70, p = 0.003). Transcriptomics analysis showed the expression of five hub fibrogenic genes including Col3A1 , BGN , Col4A1 , THBS2 and Col4A2 in the with R septa group were significantly lower. Conclusions The R septa can be differentiated from the P septa and PS fibrosis by quantitative assessment of SHG/TPEF, and it represents a tendency of fibrosis regression in MASH patients. Metabolic dysfunction-associated steatohepatitis fibrosis regression R septa collagen quantitative parameters fibrogenic genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Metabolic dysfunction - associated fatty liver disease (MAFLD) is a global public health issue that affects more than 25% of the world population[ 1 , 2 ]. The progress of MAFLD is closely related to the metabolic state, including obesity, insulin resistance, and diabetes mellitus[ 3 ]. Metabolic dysfunction-associated steatohepatitis (MASH) is the severe subclassification of MAFLD, and the typical pathological manifestations include steatosis, ballooning, and lobular inflammation[ 4 , 5 ]. MASH patients can develop liver fibrosis. Fibrosis is the most important prognostic factor for MASH patients [ 6 – 8 ]. The progression of fibrosis can increase liver-related mortality exponentially. It is known that liver fibrosis can be reversed if the etiology is controlled[ 9 , 10 ]. In terms of regression of fibrosis, Wanless and colleagues described the pathological changes of cirrhotic regression after successful viral suppression or eradication in chronic hepatitis B (CHB) patients[ 11 ]. They found that the delicate perforated fibrous septa was one of the characteristics of a “hepatic repair complex (HRC)”. Continuing this theory, our team explored the role of the dynamic morphological change of the fibrous septa in assessing fibrosis regression of CHB patients and proposed the “Beijing classification”[ 12 ]. In this new pathology evaluation system, fibrosis quality was highlighted, especially the thin, densely compacted stroma named “regressive septa (R septa)”. Our team further described collagen features of “regressive septa” in CHB patients and identified it from “progressive septa (P septa)” by qFibrosis® parameters based on second harmonic generation/two photon excitation fluorescence (SHG/TPEF) technology [ 13 ]. Compared with traditional liver pathology staining, SHG/TPEF technology can quantify the collagen characteristics of liver fibrosis, thereby observing more subtle morphological and structural changes in fibrosis. MASH-related fibrosis is also reversible. A study observed the effect of weight loss on the histology of MASH through lifestyle modification. Underwent lifestyle changes for 52 weeks, there were 56 (19%) patients who got fibrosis regression in the second biopsy. The highest rates of fibrosis regression occurred in patients with weight loss ≥ 10%[ 14 ]. With changes in lifestyle and weight, liver biopsy may show a progression or regression style in fibrosis morphology. Based on the preliminary work of our team and the “HRC” theory, we speculate that the dynamic change of MASH fibrosis can also be reflected in the morphology of fibrous septa in one liver biopsy. Whether the emergence of R septa represents a trend of fibrosis regression in MASH patients needs to be verified. Further, perisinusoidal (PS) fibrosis is one of the characteristics of MASH fibrosis which is not easy to distinguish from the delicate R septa. So a quantitative approach is needed to identify the R septa from PS fibrosis. Besides, the mechanism underlying the emergence of R septa remains unclear. Therefore, we conducted the current study to identify the detailed collagen structure of the R septa by SHG/TPEF technology. Also, we aim to clarify the relationship between the R septa and MASH fibrosis regression by paired liver biopsies. Furthermore, we will explore the overall fibrosis level of patients with and without R septa and the differentially expressed genes of the two groups. Materials and Methods Study population This study consisted of consecutive patients who were more than 18 years old with histologically proven MAFLD. These data were collected from January 2008 to December 2022 at the Liver Research Center in Beijing Friendship Hospital, Capital Medical University, Beijing, China. The diagnosis of MAFLD was based on the presence of ≥ 5% hepatic steatosis and the lack of secondary causes of hepatic fat accumulation[ 15 ]. We provide health education and regular follow-up to all enrolled MAFLD patients. The exclusion criteria include those: (1) with alcohol consumption of more than 20 g/day for men and more than 10 g/day for women (2) with the coexistence of liver diseases including chronic hepatitis B or C, autoimmune hepatitis, drug-induced liver injury (DILI), hemochromatosis, primary sclerosing cholangitis, primary biliary cholangitis, Wilson’s disease, inherited metabolic liver disease, or other causes of chronic liver disease including medications that can cause fatty liver (3) who were on treatment with drugs associated with hepatic steatosis (4) who underwent bariatric surgery (5) any malignant tumor or severe system disease. This study was approved by the Hospital’s Ethical Board, with each subject having signed a written informed consent form. A flowchart for patient inclusion and exclusion is presented in Fig. 1 . Clinical and laboratory data Demographic data and a history of co-morbidities including hypertension, diabetes mellitus, and coronary heart disease (CHD) were documented. Physical examinations and anthropometric measurements including body weight, height, and blood pressure were completed at baseline. Routine biochemical variables were collected. Homeostatic Model Assessment (HOMA) of insulin resistance was calculated as the previously published formula: HOMA-IR = FI [mIU/mL] × FG [mmol/L]/22.5)[ 16 ]. Genotyping Genomic DNA was isolated from peripheral blood using a TIANamp® Blood DNA Kit (DP348; Tiangen Biotech, Beijing, China). Genotyping of PNPLA3 rs738409 C > G and TM6SF2 rs58542926 C > T variant was performed by the TaqMan® single nucleotide polymorphism (SNP) allelic discrimination assay (Applied Biosystem; Foster City, CA, USA). Non-invasive tests for MASH Vibration-controlled transient elastography (VCTE) was performed in patients who fasted for at least two hours using a FibroScan®-502 device (EchoSens, Paris, France) with M-probe or XL-probe (automatically selected) based on previously described standard procedures[ 17 ]. FibroScan® -AST score (FAST) was calculated per the previously published formula[ 18 ]: FAST = e –1·65 + 1·07 × In (LSM) + 2·66 * 10−⁸ × CAP³ – 63.3 × AST−¹ 1 + e –1·65 + 1·07 × In (LSM) + 2·66 * 10−⁸ × CAP³ – 63.3 × AST−¹ MRI-PDFF and MRE were performed using a 3.0T field strength MRI (750W, GE Healthcare, Milwaukee, WI, USA). Patients were at least fasting 6 hours before MRI scanning. A well-trained radiologist who was blind to the clinical and histological data drew the region of interest (ROI) to assess fat fraction (9 ROIs) and calculate the elastogram (3 ROIs). MAST score was calculated as previously published formula[ 19 ]: MAST = -12.17 + 7.07 log MRE + 0.037 PDFF + 3.55 log AST. Fibrosis-4 (FIB-4) index and AST to PLT ratio index (APRI) were calculated using routine clinical and laboratory parameters obtained at the time of liver biopsy referring to the following formula: FIB-4 = [Age (years) × AST (U/L)]/[PLT (× 10 9 /L) × ALT (U/L)1/2]; APRI = AST (U/L)/AST upper limit of normal/PLT (× 10 9 /L) × 100. Histologic assessment Percutaneous liver biopsy was performed guided by ultrasound and all specimens were stained by H&E, reticulin, and Masson’s trichrome. The pathology assessment was done by two specialized liver pathologists (XYZ & LW) who were blind to clinical information according to the MASH-CRN system. The R and P septa were assessed by a hepatologist (YMS) and pathologist (XYZ). The liver biopsy specimens included in the study were of sufficient quality to ensure interpretability, and cases that were too short or incomplete were excluded. RNA-sequencing and bioinformatics analysis Total RNA was extracted from FFPE samples using AllPure FFPE DNA/RNA Kit (Megan), then was quantified using the Qubit RNA HS Assay (ThermoFisher). RNA quality was assessed using a Qsep100™ Bio-Fragment Analyzer(Bioptic)and RNA cartridge. Uniquely indexed duplicate libraries from each sample and workflow were pooled for 2 × 100 bp paired-end sequencing on a MGISEQ-2000 (MGI). Differentially expressed genes (DEGs) were analyzed and visualized by the limma package of R software V.4.1.2. Statistical significance of DEGs was determined by p |2.0|. GO and KEGG analysis was performed by the clusterProfiler package of R software. The PPI network was predicted using Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org ) (version 11.5) online database. An interaction with a combined score > 0.4 was considered statistically significant. The PPI networks were drawn using Cytoscape (version 3.9.1) and the most significant module in the PPI networks was identified using Cyothubba (version 0.1). The top five key nodes were selected and ranked by degree. Quantification of fibrosis by SHG/TPEF technique Unstained liver biopsy specimens of ten MASH patients with R septa were imaged by using SHG/TPEF microscopy (Genesis®200, HistoIndex Pte. Ltd, Singapore)[ 13 , 20 ]. A total of 82 regressive septa, 81 progressive septa, and 84 perisinusoidal fibrosis regions were labeled manually on the SHG/TPEF image. The regressive septa and progressive septa were distinguished according to the “Beijing classification” definition. Two types of parameters were analyzed: (1) The morphological feature of septa, including area, length and width; (2) The collagen and cellular areas within septa or PS region, including collagen area proportion, number of fibers per unit area, percentage of aggravated collagen, percentage of distributed collagen, and percentage of cellular area. The parameters for septa region were quantified based on the automated segmentation algorithm as described previously[ 13 ]. Matlab 2015a was used to select features (The MathWorks, Inc., Natick, MA). Statistical analysis Statistical analyses were performed using SPSS version 22.0 (IBM, Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation (SD), and compared using the student’s t-test if they were normally distributed, or described as median (IQR) and compared using Mann-Whitney test if nonnormally distributed when appropriate. Categorical variables were presented as counts (percentage, %) and were compared by Chi-squared test or Fisher’s exact test as appropriate. The receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic power of quantitative parameters. All statistical tests were two-sided. A p < 0.05 was considered to be statistically significant. Results Identification of the regressive septa, progressive septa, and perisinusoidal fibrosis by SHG/TPEF quantification We showed a representative case with a fibrosis stage of F3 who had both P septa, R septa, and PS fibrosis (Fig. 2 ). To quantify the pathological structure, we labeled 81 P septa, 82 R septa, and 84 PS regions manually according to the definition of “Beijing classification” in the 10 advanced fibrosis patients who had R septa on the SHG/TPEF image. Three of the 10 patients had significant weight loss and confirmed fibrosis regression by secondary liver biopsy. We differentiated R septa from P septa and PS fibrosis by qFibrosis® parameters. For the morphology feature of septa, as Fig. 3 A showed, the R septa had a lower value of septa area (10951 vs. 61238 µm 2 ), septa length (461 vs. 623 µm), mean septa width (15 vs. 56 µm), and max septa width (37 vs. 113 µm) than the P septa. For the collagen and cellular areas within septa, the R septa had a lower number of fibers per unit area (14375 vs. 16266), percentage of cellular area (0.07 vs. 0.13), and a higher percentage of distributed collagen (0.03 vs. 0.02) than the P septa (all p < 0.05). In addition, the R septa and PS fibrosis were morphologically distinct in qFibrosis® parameters. The R septa had a higher collagen proportion area (0.42 vs. 0.05), number of fibers per unit area (14375 vs. 1832), percentage of aggravated collagen (0.38 vs. 0.03), percentage of distributed collagen (0.03 vs. 0.02), and a lower percentage of cellular area (0.07 vs. 0. 66) than the PS fibrosis (all p < 0.05). ( Fig. 3 A ) We verified the identification ability of the above parameters. We found that septa area, mean septa width, and max septa width had a good performance to identify P and R septa (all AUROC > 0.9, p < 0.001). For the identification of R septa and PS fibrosis, parameters including collagen area proportion, number of fibers per unit area, percentage of aggravated collagen, and the percentage of cellular area had good performance (all AUROC > 0.9, p < 0.001) ( Supplementary Fig. 1 ). The fibers of the R septa were more compact than PS fibrosis Figure 3 B-C showed the mean minimal distance between each fiber in R septa and PS fibrosis. We found fibers in R septa were more tightly packed and aggregated than in PS fibrosis. Figure 3 D showed the calculation diagram of the mean minimal distance between fibers. We then found that the mean minimal distance between R septa was significantly smaller than PS fibrosis (8.9 vs. 13, p < 0.001) with an AUROC 0.838 (Fig. 3 E). Dynamic change of stroma/parenchymal qualities from thick septa to thin septa was found in fibrosis-regressed MASH patients Dynamic histological evaluation was conducted in six MASH patients (Fig. 4 ). The three patients in Fig. 4 A got fibrosis regression through lifestyle modification (from F4 to F3). They lost 15.7%, 10.4%, and 12.9% of initial weight in the second liver biopsy during a medium 27 months following-up. We found that along with the fibrosis reversing, the shape of fibrosis septa changed from wide, loose to thin and dense type. Such delicate fibrous R septa were a hallmark of the fibrosis regression according to the “Beijing classification” we proposed in previous research. The R septa were found in all three patients who got fibrosis regression. In Fig. 4 B, the three patients did not acquire fibrosis regression during a medium of 37 months following-up. Clearly, we found no R septa in their second liver biopsies. Instead, we found the P septa persisted in the liver biopsies and tended to be worsened. Clinical, genetic, and pathological characteristics in patients with and without R septa To clarify the significance of the R septa, we further observed the patients with advanced fibrosis in our cohort. A total of 340 consecutive patients with a suspected diagnosis of MAFLD by liver biopsy were preliminarily enrolled in this study. There were 9 patients excluded for those under 18 years old, and 8 patients for diagnosing other liver diseases according to the documented history and testing. Among the 323 patients who fulfilled the inclusion criteria, 79 of whom had advanced fibrosis (F3-F4). There were 10 patients allocated into the “with R septa” group ( n = 10, 12.7%) based on at least one R septa found in the specimen. The other 69 patients were assigned to the “without R septa” group (69, 87.3%) (Fig. 1 ). The male proportion and baseline weight in the with R septa group were significantly higher than the without R septa group (male proportion: 50.0% vs. 15.9%, p < 0.05; weight: 77.0 vs 66.0, p < 0.05). Besides, there was no significant difference in age (59 vs. 63, p = 0.647), BMI (27.1 vs. 25.8, p = 0.319), the prevalence of T2DM (40.0% vs. 44.9%, p = 1.000), hypertension (70.0% vs. 39.1%, p = 0.133), and CHD (20.0% vs. 8.7%, p = 0.585) between the two groups. The demographic and clinical characteristics of the included patients were displayed in Table 1 . Table 1 Baseline clinical and histological characteristics of patients with and without R septa Variables With R septa Without R septa P Value Demographic n 10 69 Age, years 59.0 (50.0, 69.3) 63.0 (57.0, 67.0) 0.647 Sex (male), n (%) 5 (50.0) 11 (15.9) 0.012 BMI, kg/m² 27.1 (24.8, 29.8) 25.8 (24.0, 28.2) 0.319 Weight, kg 77.0 (71.8, 82.6) 66.0 (61.5, 75.0) 0.019 Metabolic comorbidity T2D 4 (40.0) 31 (44.9) 1.000 Hypertension 7 (70.0) 27 (39.1) 0.133 CHD 2 (20.0) 6 (8.7) 0.585 Genetic profile PNPLA3 , rs738409, n (%) GG 6 (75.0) 31 (56.4) 0.538 CG/GG 10 (100) 50 (90.9) 1.000 TM6SF2 , rs58542926, n (%) CC 8 (100) 43 (78.2) 0.334 CT/TT 0 12 (21.8) Biochemical profile PLT, ×10⁹/L 147.2 ± 54.6 142.8 ± 56.5 0.820 ALB, g/L 39.7 (37.2, 40.3) 39.6 (37.9, 42.3) 0.617 ALT, IU/L 38.5 (23.0, 85.0) 52.0 (31.0, 99.5) 0.342 AST, IU/L 40.0 (24.5, 73.2) 55.7 (34.2, 73.1) 0.209 ALP, IU/L 80.9 ± 28.3 97.3 ± 30.5 0.114 GGT, IU/L 54.0 (25.0, 94.3) 62.0 (39.0, 91.0) 0.481 FBG, mmol/L 5.5 (4.5, 6.7) 5.8 (5.1, 6.6) 0.431 FINS, µIU/mL 15.6 ± 9.1 18.9 ± 9.1 0.501 HOMA-IR 3.8 ± 2.7 5.2 ± 2.7 0.349 TG, mmol/L 1.4 (1.0, 1.7) 1.5 (1.1, 2.1) 0.459 CHOL, mmol/L 4.4 ± 1.0 4.8 ± 0.9 0.218 HDL-C, mmol/L 1.0 ± 0.3 1.2 ± 0.3 0.254 LDL-C, mmol/L 2.5 ± 0.6 2.8 ± 0.7 0.256 TBIL, µmol/L 19.2 (15.1, 22.6) 15.7 (11.8, 20.1) 0.160 UA, µmol/L 385.9 (292.3, 399.7) 333.9 (272.5, 378.7) 0.249 Histology characteristic Steatosis 0.958 1 8 (80.0) 52 (75.4) 2 and 3 2 (20.0) 17 (24.6) Steatosis distribution 0.369 Perivenular 4 (40.0) 12 (17.9) Periportal 0 1 (1.5) Azonal 6 (60.0) 47 (70.1) Pan-acinar 0 7 (10.5) Lobular inflammation 1.000 1 6 (60.0) 37 (53.6) 2 4 (40.0) 26 (37.7) 3 0 6 (8.7) Ballooning 1.000 1 2 (20.0) 11 (15.9) 2 8 (80.0) 58 (84.1) NAS score 0.943 3–4 6 (60.0) 35 (53.0) 5–8 4 (40.0) 31 (47.0) Fibrosis stage 3 9 (90.0) 35 (50.7) 0.046 4 1 (10.0) 34 (49.3) Abbreviations : BMI, body mass index; T2D, type 2 diabetes; CHD, coronary heart disease; PNPLA3 , patatin-like-phospholipase domain-containing protein 3; TM6SF2 , transmembrane 6 superfamily member 2; PLT, platelet; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostatic model assessment for insulin resistance; TG, triglyceride; CHOL, cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TBIL, total bilirubin; UA, uric acid; NAS score, NAFLD activity score As indicated in Table 1 , there was no significant difference in the distribution of SNPs including CC and CG + GG genotypes in PNPLA3 , CC and CT + TT genotypes in TM6SF2 between the two groups. As far as the hematological and biochemical characteristics, no significant difference was found in PLT, ALB, ALT, AST, ALP, GGT, FBG, FINS, HOMA-IR, TG, CHOL, HDL-C, LDL-C, TBIL, and UA level between the two groups (all p > 0.05). The comparisons of histological characteristics between patients with and without R septa were displayed in Table 1 . The fibrosis stage distribution in the two groups was different ( p = 0.046). In the with R septa group, there were 9 (90%) F3 patients and 1 (1% ) F4 patient. In the without R septa group, F3 and F4 patients were evenly distributed (50% vs. 50%). For the grade of steatosis amount, steatosis distribution, lobular inflammation, ballooning, and NAS score, there was no significant difference between the two groups (all p > 0.05). Comparing non-invasive tests in patients with and without R septa As shown in Fig. 5 , patients with R septa had a significantly lower value of LSM assessed by Fibroscan® (12.3 vs. 19.4 kPa, p = 0.010; F3: 12.3 vs. 16.3 kPa, p = 0.342; F4: 12.3 vs. 22.5 kPa, p = 0.001), and FAST score (0.43 vs. 0.70, p = 0.003, F3: 0.43 vs. 0.70, p = 0.025; F4: 0.43 vs. 0.72, p = 0.008). To correct the different proportions of fibrosis levels in the two groups, we compared with the same fibrosis grade and found that the FAST score of the “with R septa” group was significantly lower than that of the “without R septa” group both in F3 and F4 patients. The value of LSM assessed by MRE, MAST, FIB-4 and APRI in patients with R septa showed a lower tendency than those without R septa patients although there was no statistical difference. DEGs identification and hub gene finding involved in patients with and without R septa We performed RNA-sequencing analysis in 22 patients with F3 fibrosis. Among them, 6 patients were with R septa and 16 patients were without R septa. The DEGs were screened by the limma package of R software ( p -value 1) and a total of 85 DEGs were identified (49 down-regulated genes and 36 up-regulated genes). The DEGs were shown by volcano map (Fig. 6 A) and the top 50 genes according to |fold change| were shown by heatmap in Fig. 6 B. The GO and KEGG analysis was performed (Fig. 6 C-D ) . In the cell composition of GO analysis, the DEGs were mainly enriched in the collagen-containing extracellular matrix. In the molecular function, the DEGs were mainly enriched in extracellular matrix structural constituents. For KEGG analysis, DEGs were mainly enriched in protein digestion and absorption, focal adhesion, and extracellular matrix (ECM)-receptor interaction pathways. The PPI networks were constructed using the STRING database ( http://string-db.org ). As shown in Fig. 6 E, the top five most influential genes were Col3A1 , BGN , Col4A1 , THBS2 , Col4A2 sorted by degree, and their protein structure were shown in Fig. 6 F. We compared the TPM (Transcripts Per kilobase of exonmodel per Million mapped reads) of Col3A1 (32.3 vs . 62.7), BGN (100.2 vs. 172.4), Col4A1 (7.7 vs. 13.6 ), THBS2 (3.9 vs.7.3 ), Col4A2 (7.7 vs. 10.5) genes in the two groups, all the value in the with R septa group were significantly lower than the without R septa group (all p < 0.05) (Fig. 6 G). Discussion In this study, we distinguish R septa from P septa and PS fibrosis through the collagen quantitative method by SHG/TPEF technology. Besides, we found the appearance of delicate and dense R septa indicating fibrosis regression in advanced fibrosis MASH patients. We further demonstrated the phenomenon and possible mechanism of lower fibrosis levels in patients with R septa by noninvasive tests and transcriptomics. This study is the continuation of our previous work on fibrosis regression. In Sun et al ’s study, a new histological evaluation system “Beijing classification” was proposed. In this system, the PIR score was used to evaluate the dynamic changes in the quality of fibrosis in CHB patients with advanced fibrosis stage: predominantly progressive (thick/broad/loose/pale septa with inflammation); predominately regressive (delicate/thin/dense/splitting septa); and indeterminate, which displayed an overall balance[ 12 ]. This new staging system highlighted the quality of fibrosis and can infer the dynamic direction of fibrosis [ 21 ]. Although the “Beijing classification” was created in CHB patients, based on the concept of “hepatic repair complex (HRC)”, the morphological changes of fibrosis regression may be common in chronic liver diseases of different etiologies. We confirmed this theory in secondary liver biopsies of partially successful weight loss patients in our MAFLD cohort. One strength of this study is that we could find the regression tendency of MASH fibrosis by one biopsy which continues the idea of “Beijing classification”. The main finding of our study is that this delicate, thin, dense, splitting septa in MASH also indicates fibrosis regression. This is an expansion of the “Beijing classification” pathological evaluation system, from viral hepatitis to other liver diseases, from a liver disease characterized by portal fibrosis to perisinusoidal fibrosis. In this study, the percentage of R septa did not reach the criteria of “predominantly regressive” of the PIR system (50%)[ 12 ]. The main reason we believe is the lack of effective drugs for MASH, so it is very difficult for MASH patients to achieve fibrosis regression as much as CHB patients. Even so, we found that the appearance of R septa, although did not reach the percentage of 50%, can still show the tendency of the good direction of MASH fibrosis. We believe our conclusion can be used to help determine the efficacy of new MASH drug development, and can more sensitively detect the regression of fibrosis, which predicts the improvement of outcome in MASH patients. The other strength of this study is that we could precisely identify the delicate R septa from PS fibrosis. MASH fibrosis is characterized by PS fibrosis, which is a key pathological feature different from viral hepatitis[ 22 ]. The R septa and PS fibrosis were both thin and delicate which was hard to distinguish from each other. Identifying the R septa is especially important for judging whether fibrosis is in a regressive or progressive stage according to the theory of “Beijing classification”. To solve this problem, we applied the SHG/TPEF technology and compared the detailed collagen parameters between R septa and PS fibrosis. The SHG/TPEF can quantitatively show the structure and characteristics of collagen without traditional staining. Although the R septa looked as thin as PS fibrosis on regular collagen staining, through quantitative analysis, the R septa had a significant difference with PS fibrosis. Further, by comparing the distance between fibers, we found that the fibers in R septa were more compact, and the fibers in PS fibrosis were more diffuse, which was also a key difference between the two structures. The analysis of DEGs provides insights into why the presence of R septa suggests fibrosis regression. In our study, most DEGs were concentrated on the ECM function and related pathways. Fibrogenic genes including Col3A1 , Col4A1 , Col4A2 , BGN , THBS2 , and THBS1 were down-regulated in patients with R septa and up-regulated in patients without R septa. The function of these genes reveals the underlying mechanism of fibrosis regression in patients with R septa. The Col3A1 gene encodes the pro-alpha1 chains of type III collagen (COL III). The COL III formation biomarker ProC3 has been reported to be associated with lobular inflammation, ballooning, and elevated fibrosis stage in MASH[ 23 ]. The Col4A1 and Col4A2 are both key genes that encode type IV collagen (COL IV), which is the major structural component of basement membranes[ 24 ]. Accordingly, the overexpression of COL IV in the space of Disse may serve as perisinusoidal basement membrane formation, which is associated with capillarization of the hepatic sinusoid and liver fibrosis[ 24 , 25 ]. BGN encodes a member of the small leucine-rich proteoglycan (SLRP) family of proteins, which play a role in collagen fibril assembly in multiple tissues[ 26 ]. Downregulation of BGN could reduce collagen deposition and inhibit the TGF-β1/Smad pathway to reduce apoptosis, inflammation, and DNA damage in fibrotic livers[ 27 ]. The protein encoded by THBS2 belongs to the thrombospondin family. It is a disulfide-linked homotrimeric glycoprotein that mediates cell-to-cell and cell-to-matrix interactions. A recent study showed that THBS2 positively correlated with inflammation and ballooning according to MAFLD activity score, and had a good diagnosing ability to identify MASH and advanced fibrosis patients[ 28 ]. Another multi-transcriptome analysis revealed that THBS2 and COL4A2 were positively associated with each other in liver fibrosis progression[ 29 ]. This study has a few limitations. First, this is a cross-sectional study, and whether patients with R septa have a better prognosis requires a long-term follow-up. Although our team has carried out follow-ups of advanced MASH fibrosis patients with different types of septa, the endpoints of MASH appear slowly and require a longer observation time. We believe that the emergence of R septa can have a beneficial impact on MASH patients, but long-term observation is still needed to confirm. Second, the sample size of this study is limited, especially for subjects with R septa. With the successful development of new MASH drugs, we believe there will be more patients with MASH fibrosis regression in the future, and a larger sample size will be available to further verify our theory. Finally, the key genes that were related to the emergence of R septa in our study need more experiments to explore the specific mechanism. In conclusion, the appearance of R septa is a sign of regression in MASH patients with advanced fibrosis. The R septa resemble perisinusoidal fibrosis but can be differentiated by quantitative means of new technology. Abbreviations BMI, body mass index; T2D, type 2 diabetes; CHD, coronary heart disease; PNPLA3 , patatin-like-phospholipase domain-containing protein 3; TM6SF2 , transmembrane 6 superfamily member 2; PLT, platelet; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostatic model assessment for insulin resistance; TG, triglyceride; CHOL, cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TBIL, total bilirubin; UA, uric acid; NAS score, MAFLD activity score; LSM, liver stiffness measurement; VCTE, vibration-controlled transient elastography; MRE, magnetic resonance elastography; FAST score, FibroScan ® -AST score; MAST score, MRI-based score; FIB-4, Fibrosis-4 score; APRI, AST to PLT ratio index; H&E, hematoxylin and eosin; IQR, interquartile range; SD, standard deviation; MAFLD, metabolic dysfunction-associated fatty liver disease; MASH, metabolic dysfunction-associated steatohepatitis; NASH-CRN, nonalcoholic steatohepatitis-clinical research network; NITs, non-invasive tests; SNPs, single nucleotide polymorphisms; DEGs, differentially expressed genes; Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author contributions Study design: HY, XFT. Data collection: XFT, YMS, QYW, MYZ, XNW, CLS, JJZ, MHZ, XJO. Liver biopsy assessment: XYZ, YMS. Statistical analysis: XFT, XYZ. Manuscript writing: XFT. Genotype analysis: XFT, QYW. Critical revision of the manuscript: HY, JDJ. Funding This study was supported by the “Beijing Hospitals Authority Clinical Medicine Development of special funding support (Award number: ZLRK202301)” and the “National Natural Science Foundation of China (Award number: 82130018)”. Acknowledgment Not applicable. Ethical approval The study was conducted by the principles enshrined in the Declaration of Helsinki and the Good Clinical Practices. The Ethics Committee of Beijing Friendship Hospital, Capital Medical University approved the study protocol (approval number: 2015-P2-070-01). Informed consent All patients gave written informed consent prior to their enrollment. Consent for publication All authors had access to the study data and reviewed and approved the final manuscript. References Feng G, Valenti L, Wong V, Fouad Y, Yilmaz Y, Kim W, et al. Recompensation in cirrhosis: unraveling the evolving natural history of nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol. 2024;21:46–56. Younossi ZM. Non-alcoholic fatty liver disease - A global public health perspective. J Hepatol. 2019;70:531–44. Younossi ZM, Golabi P, de Avila L, Paik JM, Srishord M, Fukui N, et al. The global. epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis. J Hepatol. 2019;71:793–801. Singh S, Allen A, Wang Z, Prokop L, Murad M, Loomba R. Fibrosis progression in. nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and meta-analysis of paired-biopsy studies. Clin Gastroenterol Hepatol. 2015;13:643–54. Ampuero J, Aller R, Gallego-Duran R, Banales JM, Crespo J, Garcia-Monzon C, et al. 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Histology of nonalcoholic fatty liver disease and. nonalcoholic steatohepatitis in adults and children. Clin Liver Dis. 2016;20:293–312. Luo Y, Oseini A, Gagnon R, Charles ED, Sidik K, Vincent R, et al. An evaluation of the. collagen fragments related to fibrogenesis and fibrolysis in nonalcoholic steatohepatitis. Sci Rep. 2018;8:12414. Mak KM, Mei R. Basement membrane type IV collagen and laminin an overview of their biology and value. Anat Rec (Hoboken). 2017;300:1371–90. Wells RG. Cellular sources of extracellular matrix in hepatic fibrosis. Clin Liver Dis. 2008;12:759–68. Appunni S, Rubens M, Ramamoorthy V, Anand V, Khandelwal M, Sharma A. Biglycan: an emerging small leucine-rich proteoglycan (SLRP) marker and its clinicopathological significance. Mol Cell Biochem. 2021;476:3935–50. Yu M, He X, Song X, Gao J, Pan J, Zhou T, et al. Biglycan promotes hepatic fibrosis. through activating heat shock protein 47. Liver Int. 2022;43:500–12. Kozumi K, Kodama T, Murai H, Sakane S, Govaere O, Cockell S, et al. Transcriptomics i. dentify thrombospondin-2 as a biomarker for NASH and advanced liver fibrosis. Hepatology. 2021;74:2452–66. Chen W, Wu X, Yan X, Xu A, Yang A, You H. Multitranscriptome analyses reveal. prioritized genes specifically associated with liver fibrosis progression independent of etiology. Am J Physiol Gastrointest Liver Physiol. 2019;316:G744–54. Supplementary Files SupplementaryFigure1.jpg Supplementary Figure 1 Diagnosis values for qFibrosis ® parameters to identify R and P septa (AUROC > 0.7, p 0.9, p < 0.001). Cite Share Download PDF Status: Published Journal Publication published 16 Aug, 2024 Read the published version in Hepatology International → Version 1 posted Editorial decision: Major Revisions Needed 31 May, 2024 Reviewers agreed at journal 10 May, 2024 Reviewers invited by journal 10 May, 2024 First submitted to journal 08 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-4392304","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301206022,"identity":"7f63e5db-d46a-4772-8f3e-25bddde0ce04","order_by":0,"name":"Xiaofei Tong","email":"","orcid":"","institution":"Beijing Friendship Hospital，Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Tong","suffix":""},{"id":301206023,"identity":"83ff1f83-cda5-4f91-a357-17d7073e34eb","order_by":1,"name":"Yameng Sun","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital 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03:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4392304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4392304/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12072-024-10719-w","type":"published","date":"2024-08-16T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57035714,"identity":"46f57fa9-1e6d-459a-929d-48e3c36f0960","added_by":"auto","created_at":"2024-05-23 18:35:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":507341,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchartfor study design.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/203cb37af91f89c17d36e168.jpg"},{"id":57035717,"identity":"75e5b71e-fc44-4c49-87bc-0350b2a839f0","added_by":"auto","created_at":"2024-05-23 18:35:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2718855,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images for P septa, R septa, and PS fibrosis in MASH patients using reticulin staining, SHG/TPEF image, and digital image. In the digital image, color green represented septa, color red represented the portal area, color blue represented perisinusoidal fibrosis.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/6c433f1c6788404ad990ad9e.jpg"},{"id":57035719,"identity":"22dc7576-b786-41bf-b93b-8720d99783b2","added_by":"auto","created_at":"2024-05-23 18:35:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2049868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eqFibrosis\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e® \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003equantification was used to identify the P septa, R septa, and PS fibrosis.\u003c/strong\u003e (A) \u003cstrong\u003eR septa \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003evs\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. P septa\u003c/strong\u003e: The R septa had a lower value of septa area (10951 \u003cem\u003evs.\u003c/em\u003e 61238 μm\u003csup\u003e2\u003c/sup\u003e), septa length (461 \u003cem\u003evs.\u003c/em\u003e 623 μm), mean septa width (15 \u003cem\u003evs.\u003c/em\u003e 56 μm), and max septa width (37 \u003cem\u003evs.\u003c/em\u003e 113 μm) in \u003cstrong\u003esepta morphology\u003c/strong\u003e. The R septa had a lower number of fibers per unit area (14375 \u003cem\u003evs.\u003c/em\u003e 16266), percentage of cellular area (7 \u003cem\u003evs.\u003c/em\u003e 13), and a higher percentage of distributed collagen (3 \u003cem\u003evs.\u003c/em\u003e 2) in \u003cstrong\u003ecollagen and cellular parameters within septa\u003c/strong\u003e (all \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05). \u003cstrong\u003eR septa \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003evs\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. PS fibrosis\u003c/strong\u003e: The R septa had a higher collagen area proportion (42\u003cem\u003e vs.\u003c/em\u003e 5), number of fibers per unit area (14375 \u003cem\u003evs. \u003c/em\u003e1832), percentage of aggravated collagen (37\u003cem\u003e vs.\u003c/em\u003e3),\u0026nbsp; percentage of distributed collagen (3\u003cem\u003e vs.\u003c/em\u003e 2), and a lower percentage of cellular area (7 \u003cem\u003evs. \u003c/em\u003e66) than the PS fibrosis (all \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(B-C) The blue arrow on SHG/TPEF image showed the distance between fibers of R septa and PS fibrosis. (D) Calculation formula of the mean minimal distance of fibers. (E) The mean minimal distance of fibers in R septa was lower than PS fibrosis \u0026nbsp;(8.9 \u003cem\u003evs.\u003c/em\u003e 13, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/52fca9ccc6bec70c14a1c65f.jpg"},{"id":57035720,"identity":"ecf84e90-c3ca-4756-8e9d-230a14c23102","added_by":"auto","created_at":"2024-05-23 18:35:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1085188,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative cases of MASH fibrosis regression and non-regression assessed by the NASH-CRN system. (A) The delicate, thin, and dense R septa were present in the patients with fibrosis regression. (B) There were no R septa present in the three cases without fibrosis regression.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/83173f79fb1ae8c113b184d2.jpg"},{"id":57035721,"identity":"dac266ca-0064-43f4-b7f3-5588d12064cc","added_by":"auto","created_at":"2024-05-23 18:35:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":809934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-invasive tests between patients with and without R septa. \u003c/strong\u003e(A) The liver stiffness (LSM) assessed by Fibroscan in patients with and without R septa (Total \u003cem\u003ep \u003c/em\u003e= 0.010, \u003cem\u003ep\u003c/em\u003e= 0.342 in F3 patients, \u003cem\u003ep\u003c/em\u003e = 0.001 in F4 patients).\u003cstrong\u003e \u003c/strong\u003e(B) The FAST score in patients with and without R septa (Total \u003cem\u003ep\u003c/em\u003e = 0.003, \u003cem\u003ep\u003c/em\u003e = 0.025 in F3 patients, \u003cem\u003ep\u003c/em\u003e = 0.008 in F4 patients).\u003cstrong\u003e \u003c/strong\u003e(C) The LSM assessed by MRE in patients with and without R septa (Total \u003cem\u003ep\u003c/em\u003e = 0.080, \u003cem\u003ep\u003c/em\u003e = 0.704 in F3 patients, \u003cem\u003ep\u003c/em\u003e = 0.002 in F4 patients).\u003cstrong\u003e \u003c/strong\u003e(D) The\u003cstrong\u003e \u003c/strong\u003eMAST score in patients with and without R septa (Total \u003cem\u003ep\u003c/em\u003e = 0.110, \u003cem\u003ep\u003c/em\u003e = 0.531 in F3 patients, \u003cem\u003ep\u003c/em\u003e = 0.224 in F4 patients). (E)\u003cstrong\u003e \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eFIB-4 score\u003cstrong\u003e \u003c/strong\u003ein patients with and without R septa (Total \u003cem\u003ep\u003c/em\u003e = 0.378, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.999 in F3 patients, \u003cem\u003ep\u003c/em\u003e = 0.290 in F4 patients). (F) The APRI score\u003cstrong\u003e \u003c/strong\u003ein patients with and without R septa (Total \u003cem\u003ep\u003c/em\u003e = 0.093, \u003cem\u003ep\u003c/em\u003e = 0.180 in F3 patients, \u003cem\u003ep\u003c/em\u003e = 0.276 in F4 patients).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/585e3e8c26bb9c7e9f4cb310.jpg"},{"id":57035718,"identity":"80395b24-cb9c-415e-8362-65ba575e68eb","added_by":"auto","created_at":"2024-05-23 18:35:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1125573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe analysis of DEGs in patients with R septa and without R septa. \u003c/strong\u003e(A) Vocano map of DEGs. The red points represent up-regulated genes. The blue points represent down-regulated genes. The gray points represent genes with no significant difference. FC is the fold change. (B) Pheatmap of DEGs in patients with R septa and without R septa. (C-D) GO and KEGG analysis showed DEGs were most enriched in ECM related pathway. (E) The PPI network showed \u003cem\u003eCol3A1\u003c/em\u003e, \u003cem\u003eBGN\u003c/em\u003e, \u003cem\u003eCol4A1\u003c/em\u003e, \u003cem\u003eTHBS2\u003c/em\u003e, \u003cem\u003eCol4A2\u003c/em\u003e were the five hub genes between patients with and without R septa. (F) The protein structure and degree of the five hub genes. (G) The TPM of \u003cem\u003eCol3A1\u003c/em\u003e(32.3 \u003cem\u003evs\u003c/em\u003e. 62.7), \u003cem\u003eBGN\u003c/em\u003e (100.2 \u003cem\u003evs. \u003c/em\u003e172.4), \u003cem\u003eCol4A1\u003c/em\u003e (7.7 \u003cem\u003evs. 13.6\u003c/em\u003e), \u003cem\u003eTHBS2\u003c/em\u003e (3.9 \u003cem\u003evs.7.3\u003c/em\u003e), \u003cem\u003eCol4A2\u003c/em\u003e (7.7 \u003cem\u003evs. \u003c/em\u003e10.5) geneswere lower in patients with R than without R septa (all\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/3e6b2a8ec918cfe23b93e49f.jpg"},{"id":63071226,"identity":"ca0ba1c1-aa48-4ebe-8095-0c07fe5f97d1","added_by":"auto","created_at":"2024-08-22 20:04:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9275106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/c516b371-13e8-41d7-b1c6-ab8c244ff749.pdf"},{"id":57035716,"identity":"cb239228-13b8-4634-b862-1ba95b27fb30","added_by":"auto","created_at":"2024-05-23 18:35:46","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":553600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiagnosis values for qFibrosis\u003csup\u003e® \u003c/sup\u003eparameters to identify R and P septa (AUROC \u0026gt; 0.7, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), R septa and PS fibrosis (all AUROC \u0026gt; 0.9, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4392304/v1/bac155d51272e34c11892bce.jpg"}],"financialInterests":"","formattedTitle":"Delicate and thin fibrous septa indicate a regression tendency in metabolic dysfunction-associated steatohepatitis patients with advanced fibrosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction\u003cb\u003e-\u003c/b\u003eassociated fatty liver disease (MAFLD) is a global public health issue that affects more than 25% of the world population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The progress of MAFLD is closely related to the metabolic state, including obesity, insulin resistance, and diabetes mellitus[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Metabolic dysfunction-associated steatohepatitis (MASH) is the severe subclassification of MAFLD, and the typical pathological manifestations include steatosis, ballooning, and lobular inflammation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. MASH patients can develop liver fibrosis. Fibrosis is the most important prognostic factor for MASH patients [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The progression of fibrosis can increase liver-related mortality exponentially.\u003c/p\u003e \u003cp\u003eIt is known that liver fibrosis can be reversed if the etiology is controlled[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In terms of regression of fibrosis, Wanless and colleagues described the pathological changes of cirrhotic regression after successful viral suppression or eradication in chronic hepatitis B (CHB) patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They found that the delicate perforated fibrous septa was one of the characteristics of a \u0026ldquo;hepatic repair complex (HRC)\u0026rdquo;. Continuing this theory, our team explored the role of the dynamic morphological change of the fibrous septa in assessing fibrosis regression of CHB patients and proposed the \u0026ldquo;Beijing classification\u0026rdquo;[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this new pathology evaluation system, fibrosis quality was highlighted, especially the thin, densely compacted stroma named \u0026ldquo;regressive septa (R septa)\u0026rdquo;. Our team further described collagen features of \u0026ldquo;regressive septa\u0026rdquo; in CHB patients and identified it from \u0026ldquo;progressive septa (P septa)\u0026rdquo; by qFibrosis\u0026reg; parameters based on second harmonic generation/two photon excitation fluorescence (SHG/TPEF) technology [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Compared with traditional liver pathology staining, SHG/TPEF technology can quantify the collagen characteristics of liver fibrosis, thereby observing more subtle morphological and structural changes in fibrosis.\u003c/p\u003e \u003cp\u003eMASH-related fibrosis is also reversible. A study observed the effect of weight loss on the histology of MASH through lifestyle modification. Underwent lifestyle changes for 52 weeks, there were 56 (19%) patients who got fibrosis regression in the second biopsy. The highest rates of fibrosis regression occurred in patients with weight loss\u0026thinsp;\u0026ge;\u0026thinsp;10%[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. With changes in lifestyle and weight, liver biopsy may show a progression or regression style in fibrosis morphology. Based on the preliminary work of our team and the \u0026ldquo;HRC\u0026rdquo; theory, we speculate that the dynamic change of MASH fibrosis can also be reflected in the morphology of fibrous septa in one liver biopsy. Whether the emergence of R septa represents a trend of fibrosis regression in MASH patients needs to be verified. Further, perisinusoidal (PS) fibrosis is one of the characteristics of MASH fibrosis which is not easy to distinguish from the delicate R septa. So a quantitative approach is needed to identify the R septa from PS fibrosis. Besides, the mechanism underlying the emergence of R septa remains unclear.\u003c/p\u003e \u003cp\u003eTherefore, we conducted the current study to identify the detailed collagen structure of the R septa by SHG/TPEF technology. Also, we aim to clarify the relationship between the R septa and MASH fibrosis regression by paired liver biopsies. Furthermore, we will explore the overall fibrosis level of patients with and without R septa and the differentially expressed genes of the two groups.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study consisted of consecutive patients who were more than 18 years old with histologically proven MAFLD. These data were collected from January 2008 to December 2022 at the Liver Research Center in Beijing Friendship Hospital, Capital Medical University, Beijing, China. The diagnosis of MAFLD was based on the presence of \u0026ge;\u0026thinsp;5% hepatic steatosis and the lack of secondary causes of hepatic fat accumulation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We provide health education and regular follow-up to all enrolled MAFLD patients.\u003c/p\u003e \u003cp\u003eThe exclusion criteria include those: (1) with alcohol consumption of more than 20 g/day for men and more than 10 g/day for women (2) with the coexistence of liver diseases including chronic hepatitis B or C, autoimmune hepatitis, drug-induced liver injury (DILI), hemochromatosis, primary sclerosing cholangitis, primary biliary cholangitis, Wilson\u0026rsquo;s disease, inherited metabolic liver disease, or other causes of chronic liver disease including medications that can cause fatty liver (3) who were on treatment with drugs associated with hepatic steatosis (4) who underwent bariatric surgery (5) any malignant tumor or severe system disease.\u003c/p\u003e \u003cp\u003e This study was approved by the Hospital\u0026rsquo;s Ethical Board, with each subject having signed a written informed consent form. A flowchart for patient inclusion and exclusion is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical and laboratory data\u003c/h2\u003e \u003cp\u003eDemographic data and a history of co-morbidities including hypertension, diabetes mellitus, and coronary heart disease (CHD) were documented. Physical examinations and anthropometric measurements including body weight, height, and blood pressure were completed at baseline. Routine biochemical variables were collected. Homeostatic Model Assessment (HOMA) of insulin resistance was calculated as the previously published formula: HOMA-IR\u0026thinsp;=\u0026thinsp;FI [mIU/mL] \u0026times; FG [mmol/L]/22.5)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eGenomic DNA was isolated from peripheral blood using a TIANamp\u0026reg; Blood DNA Kit (DP348; Tiangen Biotech, Beijing, China). Genotyping of \u003cem\u003ePNPLA3\u003c/em\u003e rs738409 C\u0026thinsp;\u0026gt;\u0026thinsp;G and \u003cem\u003eTM6SF2\u003c/em\u003e rs58542926 C\u0026thinsp;\u0026gt;\u0026thinsp;T variant was performed by the TaqMan\u0026reg; single nucleotide polymorphism (SNP) allelic discrimination assay (Applied Biosystem; Foster City, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNon-invasive tests for MASH\u003c/h2\u003e \u003cp\u003eVibration-controlled transient elastography (VCTE) was performed in patients who fasted for at least two hours using a FibroScan\u0026reg;-502 device (EchoSens, Paris, France) with M-probe or XL-probe (automatically selected) based on previously described standard procedures[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. FibroScan\u0026reg; -AST score (FAST) was calculated per the previously published formula[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFAST =\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ee \u003csup\u003e\u0026ndash;1\u0026middot;65 + 1\u0026middot;07 \u0026times; In (LSM) + 2\u0026middot;66 * 10\u0026minus;⁸ \u0026times; CAP\u0026sup3; \u0026ndash; 63.3 \u0026times; AST\u0026minus;\u0026sup1;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;e \u003csup\u003e\u0026ndash;1\u0026middot;65 + 1\u0026middot;07 \u0026times; In (LSM) + 2\u0026middot;66 * 10\u0026minus;⁸ \u0026times; CAP\u0026sup3; \u0026ndash; 63.3 \u0026times; AST\u0026minus;\u0026sup1;\u003c/sup\u003e\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\u003eMRI-PDFF and MRE were performed using a 3.0T field strength MRI (750W, GE Healthcare, Milwaukee, WI, USA). Patients were at least fasting 6 hours before MRI scanning. A well-trained radiologist who was blind to the clinical and histological data drew the region of interest (ROI) to assess fat fraction (9 ROIs) and calculate the elastogram (3 ROIs). MAST score was calculated as previously published formula[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]: MAST = -12.17\u0026thinsp;+\u0026thinsp;7.07 log MRE\u0026thinsp;+\u0026thinsp;0.037 PDFF\u0026thinsp;+\u0026thinsp;3.55 log AST. Fibrosis-4 (FIB-4) index and AST to PLT ratio index (APRI) were calculated using routine clinical and laboratory parameters obtained at the time of liver biopsy referring to the following formula: FIB-4 = [Age (years) \u0026times; AST (U/L)]/[PLT (\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; ALT (U/L)1/2]; APRI\u0026thinsp;=\u0026thinsp;AST (U/L)/AST upper limit of normal/PLT (\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; 100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHistologic assessment\u003c/h2\u003e \u003cp\u003ePercutaneous liver biopsy was performed guided by ultrasound and all specimens were stained by H\u0026amp;E, reticulin, and Masson\u0026rsquo;s trichrome. The pathology assessment was done by two specialized liver pathologists (XYZ \u0026amp; LW) who were blind to clinical information according to the MASH-CRN system. The R and P septa were assessed by a hepatologist (YMS) and pathologist (XYZ). The liver biopsy specimens included in the study were of sufficient quality to ensure interpretability, and cases that were too short or incomplete were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA-sequencing and bioinformatics analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from FFPE samples using AllPure FFPE DNA/RNA Kit (Megan), then was quantified using the Qubit RNA HS Assay (ThermoFisher). RNA quality was assessed using a Qsep100\u0026trade; Bio-Fragment Analyzer(Bioptic)and RNA cartridge. Uniquely indexed duplicate libraries from each sample and workflow were pooled for 2 \u0026times; 100 bp paired-end sequencing on a MGISEQ-2000 (MGI).\u003c/p\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were analyzed and visualized by the limma package of R software V.4.1.2. Statistical significance of DEGs was determined by \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change of \u0026gt; |2.0|. GO and KEGG analysis was performed by the clusterProfiler package of R software. The PPI network was predicted using Search Tool for the Retrieval of Interacting Genes (STRING; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (version 11.5) online database. An interaction with a combined score\u0026thinsp;\u0026gt;\u0026thinsp;0.4 was considered statistically significant. The PPI networks were drawn using Cytoscape (version 3.9.1) and the most significant module in the PPI networks was identified using Cyothubba (version 0.1). The top five key nodes were selected and ranked by degree.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of fibrosis by SHG/TPEF technique\u003c/h2\u003e \u003cp\u003eUnstained liver biopsy specimens of ten MASH patients with R septa were imaged by using SHG/TPEF microscopy (Genesis\u0026reg;200, HistoIndex Pte. Ltd, Singapore)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A total of 82 regressive septa, 81 progressive septa, and 84 perisinusoidal fibrosis regions were labeled manually on the SHG/TPEF image. The regressive septa and progressive septa were distinguished according to the \u0026ldquo;Beijing classification\u0026rdquo; definition. Two types of parameters were analyzed: (1) The morphological feature of septa, including area, length and width; (2) The collagen and cellular areas within septa or PS region, including collagen area proportion, number of fibers per unit area, percentage of aggravated collagen, percentage of distributed collagen, and percentage of cellular area. The parameters for septa region were quantified based on the automated segmentation algorithm as described previously[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Matlab 2015a was used to select features (The MathWorks, Inc., Natick, MA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 22.0 (IBM, Armonk, NY, USA). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and compared using the student\u0026rsquo;s t-test if they were normally distributed, or described as median (IQR) and compared using Mann-Whitney test if nonnormally distributed when appropriate. Categorical variables were presented as counts (percentage, %) and were compared by Chi-squared test or Fisher\u0026rsquo;s exact test as appropriate. The receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic power of quantitative parameters. All statistical tests were two-sided. A \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the regressive septa, progressive septa, and perisinusoidal fibrosis by SHG/TPEF quantification\u003c/h2\u003e \u003cp\u003eWe showed a representative case with a fibrosis stage of F3 who had both P septa, R septa, and PS fibrosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To quantify the pathological structure, we labeled 81 P septa, 82 R septa, and 84 PS regions manually according to the definition of \u0026ldquo;Beijing classification\u0026rdquo; in the 10 advanced fibrosis patients who had R septa on the SHG/TPEF image. Three of the 10 patients had significant weight loss and confirmed fibrosis regression by secondary liver biopsy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe differentiated R septa from P septa and PS fibrosis by qFibrosis\u0026reg; parameters. For the morphology feature of septa, as Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA showed, the R septa had a lower value of septa area (10951 \u003cem\u003evs.\u003c/em\u003e 61238 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e), septa length (461 \u003cem\u003evs.\u003c/em\u003e 623 \u0026micro;m), mean septa width (15 \u003cem\u003evs.\u003c/em\u003e 56 \u0026micro;m), and max septa width (37 \u003cem\u003evs.\u003c/em\u003e 113 \u0026micro;m) than the P septa. For the collagen and cellular areas within septa, the R septa had a lower number of fibers per unit area (14375 \u003cem\u003evs.\u003c/em\u003e 16266), percentage of cellular area (0.07 \u003cem\u003evs.\u003c/em\u003e 0.13), and a higher percentage of distributed collagen (0.03 \u003cem\u003evs.\u003c/em\u003e 0.02) than the P septa (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, the R septa and PS fibrosis were morphologically distinct in qFibrosis\u0026reg; parameters. The R septa had a higher collagen proportion area (0.42 \u003cem\u003evs.\u003c/em\u003e 0.05), number of fibers per unit area (14375 \u003cem\u003evs.\u003c/em\u003e 1832), percentage of aggravated collagen (0.38 \u003cem\u003evs.\u003c/em\u003e 0.03), percentage of distributed collagen (0.03 \u003cem\u003evs.\u003c/em\u003e 0.02), and a lower percentage of cellular area (0.07 \u003cem\u003evs. 0.\u003c/em\u003e66) than the PS fibrosis (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe verified the identification ability of the above parameters. We found that septa area, mean septa width, and max septa width had a good performance to identify P and R septa (all AUROC\u0026thinsp;\u0026gt;\u0026thinsp;0.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For the identification of R septa and PS fibrosis, parameters including collagen area proportion, number of fibers per unit area, percentage of aggravated collagen, and the percentage of cellular area had good performance (all AUROC\u0026thinsp;\u0026gt;\u0026thinsp;0.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe fibers of the R septa were more compact than PS fibrosis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C showed the mean minimal distance between each fiber in R septa and PS fibrosis. We found fibers in R septa were more tightly packed and aggregated than in PS fibrosis. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD showed the calculation diagram of the mean minimal distance between fibers. We then found that the mean minimal distance between R septa was significantly smaller than PS fibrosis (8.9 \u003cem\u003evs.\u003c/em\u003e 13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with an AUROC 0.838 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDynamic change of stroma/parenchymal qualities from thick septa to thin septa was found in fibrosis-regressed MASH patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDynamic histological evaluation was conducted in six MASH patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The three patients in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA got fibrosis regression through lifestyle modification (from F4 to F3). They lost 15.7%, 10.4%, and 12.9% of initial weight in the second liver biopsy during a medium 27 months following-up. We found that along with the fibrosis reversing, the shape of fibrosis septa changed from wide, loose to thin and dense type. Such delicate fibrous R septa were a hallmark of the fibrosis regression according to the \u0026ldquo;Beijing classification\u0026rdquo; we proposed in previous research. The R septa were found in all three patients who got fibrosis regression. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, the three patients did not acquire fibrosis regression during a medium of 37 months following-up. Clearly, we found no R septa in their second liver biopsies. Instead, we found the P septa persisted in the liver biopsies and tended to be worsened.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical, genetic, and pathological characteristics in patients with and without R septa\u003c/h2\u003e \u003cp\u003eTo clarify the significance of the R septa, we further observed the patients with advanced fibrosis in our cohort. A total of 340 consecutive patients with a suspected diagnosis of MAFLD by liver biopsy were preliminarily enrolled in this study. There were 9 patients excluded for those under 18 years old, and 8 patients for diagnosing other liver diseases according to the documented history and testing. Among the 323 patients who fulfilled the inclusion criteria, 79 of whom had advanced fibrosis (F3-F4). There were 10 patients allocated into the \u0026ldquo;with R septa\u0026rdquo; group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10, 12.7%) based on at least one R septa found in the specimen. The other 69 patients were assigned to the \u0026ldquo;without R septa\u0026rdquo; group (69, 87.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The male proportion and baseline weight in the with R septa group were significantly higher than the without R septa group (male proportion: 50.0% \u003cem\u003evs.\u003c/em\u003e 15.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; weight: 77.0 \u003cem\u003evs\u003c/em\u003e 66.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Besides, there was no significant difference in age (59 \u003cem\u003evs.\u003c/em\u003e63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.647), BMI (27.1 \u003cem\u003evs.\u003c/em\u003e 25.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.319), the prevalence of T2DM (40.0% \u003cem\u003evs.\u003c/em\u003e 44.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000), hypertension (70.0% \u003cem\u003evs.\u003c/em\u003e 39.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.133), and CHD (20.0% \u003cem\u003evs.\u003c/em\u003e 8.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.585) between the two groups. The demographic and clinical characteristics of the included patients were displayed in 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\u003eBaseline clinical and histological characteristics of patients with and without R septa\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith R septa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout R septa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \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\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.0 (50.0, 69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 (57.0, 67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.1 (24.8, 29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8 (24.0, 28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.0 (71.8, 82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.0 (61.5, 75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic comorbidity\u003c/b\u003e\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\u003eT2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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\u003e7 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenetic profile\u003c/b\u003e\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\u003e\u003cb\u003ePNPLA3\u003c/b\u003e, \u003cb\u003ers738409, n (%)\u003c/b\u003e\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\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCG/GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTM6SF2\u003c/b\u003e, \u003cb\u003ers58542926, n (%)\u003c/b\u003e\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\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT/TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (21.8)\u003c/p\u003e \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\u003e\u003cb\u003eBiochemical profile\u003c/b\u003e\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\u003ePLT, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147.2\u0026thinsp;\u0026plusmn;\u0026thinsp;54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.8\u0026thinsp;\u0026plusmn;\u0026thinsp;56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.7 (37.2, 40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.6 (37.9, 42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.5 (23.0, 85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.0 (31.0, 99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.0 (24.5, 73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.7 (34.2, 73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3\u0026thinsp;\u0026plusmn;\u0026thinsp;30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.0 (25.0, 94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.0 (39.0, 91.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5 (4.5, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 (5.1, 6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFINS, \u0026micro;IU/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (1.0, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.1, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2 (15.1, 22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7 (11.8, 20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385.9 (292.3, 399.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.9 (272.5, 378.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology characteristic\u003c/b\u003e\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\u003eSteatosis\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 \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (75.4)\u003c/p\u003e \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\u003e2 and 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (24.6)\u003c/p\u003e \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\u003eSteatosis distribution\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 \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerivenular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (17.9)\u003c/p\u003e \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\u003ePeriportal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \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\u003eAzonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (70.1)\u003c/p\u003e \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\u003ePan-acinar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10.5)\u003c/p\u003e \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\u003eLobular inflammation\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 \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (53.6)\u003c/p\u003e \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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (37.7)\u003c/p\u003e \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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.7)\u003c/p\u003e \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\u003eBallooning\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 \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (15.9)\u003c/p\u003e \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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (84.1)\u003c/p\u003e \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\u003eNAS score\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 \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (53.0)\u003c/p\u003e \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\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (47.0)\u003c/p\u003e \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\u003eFibrosis stage\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: BMI, body mass index; T2D, type 2 diabetes; CHD, coronary heart disease; \u003cem\u003ePNPLA3\u003c/em\u003e, patatin-like-phospholipase domain-containing protein 3; \u003cem\u003eTM6SF2\u003c/em\u003e, transmembrane 6 superfamily member 2; PLT, platelet; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostatic model assessment for insulin resistance; TG, triglyceride; CHOL, cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TBIL, total bilirubin; UA, uric acid; NAS score, NAFLD activity score\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\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there was no significant difference in the distribution of SNPs including CC and CG\u0026thinsp;+\u0026thinsp;GG genotypes in \u003cem\u003ePNPLA3\u003c/em\u003e, CC and CT\u0026thinsp;+\u0026thinsp;TT genotypes in \u003cem\u003eTM6SF2\u003c/em\u003e between the two groups. As far as the hematological and biochemical characteristics, no significant difference was found in PLT, ALB, ALT, AST, ALP, GGT, FBG, FINS, HOMA-IR, TG, CHOL, HDL-C, LDL-C, TBIL, and UA level between the two groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe comparisons of histological characteristics between patients with and without R septa were displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The fibrosis stage distribution in the two groups was different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). In the with R septa group, there were 9 (90%) F3 patients and 1 (1% ) F4 patient. In the without R septa group, F3 and F4 patients were evenly distributed (50% \u003cem\u003evs.\u003c/em\u003e 50%). For the grade of steatosis amount, steatosis distribution, lobular inflammation, ballooning, and NAS score, there was no significant difference between the two groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparing non-invasive tests in patients with and without R septa\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, patients with R septa had a significantly lower value of LSM assessed by Fibroscan\u0026reg; (12.3 \u003cem\u003evs.\u003c/em\u003e 19.4 kPa, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; F3: 12.3 \u003cem\u003evs.\u003c/em\u003e 16.3 kPa, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.342; F4: 12.3 \u003cem\u003evs.\u003c/em\u003e 22.5 kPa, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and FAST score (0.43 \u003cem\u003evs.\u003c/em\u003e 0.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, F3: 0.43 \u003cem\u003evs.\u003c/em\u003e 0.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025; F4: 0.43 \u003cem\u003evs.\u003c/em\u003e 0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). To correct the different proportions of fibrosis levels in the two groups, we compared with the same fibrosis grade and found that the FAST score of the \u0026ldquo;with R septa\u0026rdquo; group was significantly lower than that of the \u0026ldquo;without R septa\u0026rdquo; group both in F3 and F4 patients. The value of LSM assessed by MRE, MAST, FIB-4 and APRI in patients with R septa showed a lower tendency than those without R septa patients although there was no statistical difference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDEGs identification and hub gene finding involved in patients with and without R septa\u003c/h2\u003e \u003cp\u003eWe performed RNA-sequencing analysis in 22 patients with F3 fibrosis. Among them, 6 patients were with R septa and 16 patients were without R septa. The DEGs were screened by the limma package of R software (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, LogFC\u0026thinsp;\u0026gt;\u0026thinsp;1) and a total of 85 DEGs were identified (49 down-regulated genes and 36 up-regulated genes). The DEGs were shown by volcano map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and the top 50 genes according to |fold change| were shown by heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB. The GO and KEGG analysis was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D\u003cb\u003e)\u003c/b\u003e. In the cell composition of GO analysis, the DEGs were mainly enriched in the collagen-containing extracellular matrix. In the molecular function, the DEGs were mainly enriched in extracellular matrix structural constituents. For KEGG analysis, DEGs were mainly enriched in protein digestion and absorption, focal adhesion, and extracellular matrix (ECM)-receptor interaction pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PPI networks were constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, the top five most influential genes were \u003cem\u003eCol3A1\u003c/em\u003e, \u003cem\u003eBGN\u003c/em\u003e, \u003cem\u003eCol4A1\u003c/em\u003e, \u003cem\u003eTHBS2\u003c/em\u003e, \u003cem\u003eCol4A2\u003c/em\u003e sorted by degree, and their protein structure were shown \u003cb\u003ein\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF. We compared the TPM (Transcripts Per kilobase of exonmodel per Million mapped reads) of \u003cem\u003eCol3A1\u003c/em\u003e(32.3 \u003cem\u003evs\u003c/em\u003e. 62.7), \u003cem\u003eBGN\u003c/em\u003e (100.2 \u003cem\u003evs.\u003c/em\u003e 172.4), \u003cem\u003eCol4A1\u003c/em\u003e (7.7 \u003cem\u003evs. 13.6\u003c/em\u003e), \u003cem\u003eTHBS2\u003c/em\u003e (3.9 \u003cem\u003evs.7.3\u003c/em\u003e), \u003cem\u003eCol4A2\u003c/em\u003e (7.7 \u003cem\u003evs.\u003c/em\u003e 10.5) genes in the two groups, all the value in the with R septa group were significantly lower than the without R septa group (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we distinguish R septa from P septa and PS fibrosis through the collagen quantitative method by SHG/TPEF technology. Besides, we found the appearance of delicate and dense R septa indicating fibrosis regression in advanced fibrosis MASH patients. We further demonstrated the phenomenon and possible mechanism of lower fibrosis levels in patients with R septa by noninvasive tests and transcriptomics.\u003c/p\u003e \u003cp\u003eThis study is the continuation of our previous work on fibrosis regression. In Sun \u003cem\u003eet al\u003c/em\u003e\u0026rsquo;s study, a new histological evaluation system \u0026ldquo;Beijing classification\u0026rdquo; was proposed. In this system, the PIR score was used to evaluate the dynamic changes in the quality of fibrosis in CHB patients with advanced fibrosis stage: predominantly progressive (thick/broad/loose/pale septa with inflammation); predominately regressive (delicate/thin/dense/splitting septa); and indeterminate, which displayed an overall balance[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This new staging system highlighted the quality of fibrosis and can infer the dynamic direction of fibrosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although the \u0026ldquo;Beijing classification\u0026rdquo; was created in CHB patients, based on the concept of \u0026ldquo;hepatic repair complex (HRC)\u0026rdquo;, the morphological changes of fibrosis regression may be common in chronic liver diseases of different etiologies. We confirmed this theory in secondary liver biopsies of partially successful weight loss patients in our MAFLD cohort.\u003c/p\u003e \u003cp\u003eOne strength of this study is that we could find the regression tendency of MASH fibrosis by one biopsy which continues the idea of \u0026ldquo;Beijing classification\u0026rdquo;. The main finding of our study is that this delicate, thin, dense, splitting septa in MASH also indicates fibrosis regression. This is an expansion of the \u0026ldquo;Beijing classification\u0026rdquo; pathological evaluation system, from viral hepatitis to other liver diseases, from a liver disease characterized by portal fibrosis to perisinusoidal fibrosis. In this study, the percentage of R septa did not reach the criteria of \u0026ldquo;predominantly regressive\u0026rdquo; of the PIR system (50%)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The main reason we believe is the lack of effective drugs for MASH, so it is very difficult for MASH patients to achieve fibrosis regression as much as CHB patients. Even so, we found that the appearance of R septa, although did not reach the percentage of 50%, can still show the tendency of the good direction of MASH fibrosis. We believe our conclusion can be used to help determine the efficacy of new MASH drug development, and can more sensitively detect the regression of fibrosis, which predicts the improvement of outcome in MASH patients.\u003c/p\u003e \u003cp\u003eThe other strength of this study is that we could precisely identify the delicate R septa from PS fibrosis. MASH fibrosis is characterized by PS fibrosis, which is a key pathological feature different from viral hepatitis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The R septa and PS fibrosis were both thin and delicate which was hard to distinguish from each other. Identifying the R septa is especially important for judging whether fibrosis is in a regressive or progressive stage according to the theory of \u0026ldquo;Beijing classification\u0026rdquo;. To solve this problem, we applied the SHG/TPEF technology and compared the detailed collagen parameters between R septa and PS fibrosis. The SHG/TPEF can quantitatively show the structure and characteristics of collagen without traditional staining. Although the R septa looked as thin as PS fibrosis on regular collagen staining, through quantitative analysis, the R septa had a significant difference with PS fibrosis. Further, by comparing the distance between fibers, we found that the fibers in R septa were more compact, and the fibers in PS fibrosis were more diffuse, which was also a key difference between the two structures.\u003c/p\u003e \u003cp\u003eThe analysis of DEGs provides insights into why the presence of R septa suggests fibrosis regression. In our study, most DEGs were concentrated on the ECM function and related pathways. Fibrogenic genes including \u003cem\u003eCol3A1\u003c/em\u003e, \u003cem\u003eCol4A1\u003c/em\u003e, \u003cem\u003eCol4A2\u003c/em\u003e, \u003cem\u003eBGN\u003c/em\u003e, \u003cem\u003eTHBS2\u003c/em\u003e, and \u003cem\u003eTHBS1\u003c/em\u003e were down-regulated in patients with R septa and up-regulated in patients without R septa. The function of these genes reveals the underlying mechanism of fibrosis regression in patients with R septa.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eCol3A1\u003c/em\u003e gene encodes the pro-alpha1 chains of type III collagen (COL III). The COL III formation biomarker ProC3 has been reported to be associated with lobular inflammation, ballooning, and elevated fibrosis stage in MASH[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The \u003cem\u003eCol4A1\u003c/em\u003e and \u003cem\u003eCol4A2\u003c/em\u003e are both key genes that encode type IV collagen (COL IV), which is the major structural component of basement membranes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Accordingly, the overexpression of COL IV in the space of Disse may serve as perisinusoidal basement membrane formation, which is associated with capillarization of the hepatic sinusoid and liver fibrosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cem\u003eBGN\u003c/em\u003e encodes a member of the small leucine-rich proteoglycan (SLRP) family of proteins, which play a role in collagen fibril assembly in multiple tissues[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Downregulation of \u003cem\u003eBGN\u003c/em\u003e could reduce collagen deposition and inhibit the TGF-β1/Smad pathway to reduce apoptosis, inflammation, and DNA damage in fibrotic livers[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The protein encoded by \u003cem\u003eTHBS2\u003c/em\u003e belongs to the thrombospondin family. It is a disulfide-linked homotrimeric glycoprotein that mediates cell-to-cell and cell-to-matrix interactions. A recent study showed that THBS2 positively correlated with inflammation and ballooning according to MAFLD activity score, and had a good diagnosing ability to identify MASH and advanced fibrosis patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another multi-transcriptome analysis revealed that THBS2 and COL4A2 were positively associated with each other in liver fibrosis progression[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has a few limitations. First, this is a cross-sectional study, and whether patients with R septa have a better prognosis requires a long-term follow-up. Although our team has carried out follow-ups of advanced MASH fibrosis patients with different types of septa, the endpoints of MASH appear slowly and require a longer observation time. We believe that the emergence of R septa can have a beneficial impact on MASH patients, but long-term observation is still needed to confirm. Second, the sample size of this study is limited, especially for subjects with R septa. With the successful development of new MASH drugs, we believe there will be more patients with MASH fibrosis regression in the future, and a larger sample size will be available to further verify our theory. Finally, the key genes that were related to the emergence of R septa in our study need more experiments to explore the specific mechanism.\u003c/p\u003e \u003cp\u003eIn conclusion, the appearance of R septa is a sign of regression in MASH patients with advanced fibrosis. The R septa resemble perisinusoidal fibrosis but can be differentiated by quantitative means of new technology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, body mass index;\u0026nbsp;T2D, type 2 diabetes; CHD, coronary heart disease;\u0026nbsp;\u003cem\u003ePNPLA3\u003c/em\u003e, patatin-like-phospholipase domain-containing protein 3; \u003cem\u003eTM6SF2\u003c/em\u003e,\u0026nbsp;transmembrane 6 superfamily member 2;\u0026nbsp;PLT, platelet; ALB, albumin; ALT,\u0026nbsp;alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostatic model assessment for insulin resistance; TG,\u0026nbsp;triglyceride; CHOL, cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TBIL, total bilirubin; UA, uric acid; NAS score, MAFLD activity score; LSM, liver stiffness measurement; VCTE, vibration-controlled transient elastography; MRE, magnetic resonance elastography; FAST score,\u0026nbsp;FibroScan\u003csup\u003e\u0026reg;\u003c/sup\u003e-AST score; MAST score, MRI-based score; FIB-4, Fibrosis-4 score; APRI, AST to PLT ratio index; H\u0026amp;E, hematoxylin and eosin; IQR, interquartile range; SD, standard deviation; MAFLD, metabolic dysfunction-associated fatty liver disease; MASH, metabolic dysfunction-associated steatohepatitis; NASH-CRN, nonalcoholic steatohepatitis-clinical research network; NITs, non-invasive tests; SNPs, single nucleotide polymorphisms; DEGs, differentially expressed genes;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: HY, XFT. Data collection: XFT, YMS, QYW, MYZ, XNW, CLS, JJZ, MHZ, XJO. Liver biopsy assessment: XYZ, YMS. Statistical analysis: XFT, XYZ. Manuscript writing: XFT. Genotype analysis: XFT, QYW. Critical revision of the manuscript: HY, JDJ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by\u0026nbsp;the \u0026ldquo;Beijing Hospitals Authority Clinical Medicine Development of special funding support (Award number: ZLRK202301)\u0026rdquo; and the \u0026ldquo;National Natural Science Foundation of China (Award number: 82130018)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted by the principles enshrined in the Declaration of Helsinki and the Good Clinical Practices. The Ethics Committee of Beijing Friendship Hospital, Capital Medical University approved the study protocol\u0026nbsp;(approval number: 2015-P2-070-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients gave written informed consent prior to their enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors had access to the study data and reviewed and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng G, Valenti L, Wong V, Fouad Y, Yilmaz Y, Kim W, et al. Recompensation in cirrhosis: unraveling the evolving natural history of nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol. 2024;21:46\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounossi ZM. 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Am J Physiol Gastrointest Liver Physiol. 2019;316:G744\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"hepatology-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hepi","sideBox":"Learn more about [Hepatology International](https://www.springer.com/journal/12072)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/hepi/default.aspx","title":"Hepatology International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Metabolic dysfunction-associated steatohepatitis, fibrosis regression, R septa, collagen quantitative parameters, fibrogenic genes","lastPublishedDoi":"10.21203/rs.3.rs-4392304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4392304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aims:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolic dysfunction-associated steatohepatitis (MASH)-related fibrosis is reversible. However, the dynamic morphology change in fibrosis regression remains unclear. We aim to explore the morphological characteristics of fibrosis regression in advanced MASH patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and histological data of 79 biopsy-proved MASH patients with advanced fibrosis (F3-F4) were reviewed. The second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) image technology was used to quantitively identify the R (regressive) septa from P (progressive) septa and PS (perisinusoidal) fibrosis. Non-invasive tests were used to compare the fibrosis level of the with and without R septa groups. Transcriptomics was used to explore hub genes and the underlying mechanism of the formation of R septa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R septa were different from the P septa and PS fibrosis in detail collagen quantitation identified by SHG/TPEF technology. The R septa were found in MASH fibrosis-regressed patients, which met the definition of the “Beijing classification”. Therefore, patients were divided into two groups according to septa morphology: with R septa (\u003cem\u003en\u003c/em\u003e = 10, 12.7%), and without R septa (\u003cem\u003en\u003c/em\u003e = 69, 87.3%). Patients with R septa had lower values in most non-invasive tests, especially for liver stiffness (12.3 \u003cem\u003evs.\u003c/em\u003e 19.4 kPa, \u003cem\u003ep\u003c/em\u003e = 0.010), and FAST (FibroScan®-AST) score (0.43 \u003cem\u003evs.\u003c/em\u003e 0.70, \u003cem\u003ep\u003c/em\u003e = 0.003). Transcriptomics analysis showed the expression of five hub fibrogenic genes including \u003cem\u003eCol3A1\u003c/em\u003e, \u003cem\u003eBGN\u003c/em\u003e, \u003cem\u003eCol4A1\u003c/em\u003e, \u003cem\u003eTHBS2\u003c/em\u003e and \u003cem\u003eCol4A2\u003c/em\u003e in the with R septa group were significantly lower.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R septa can be differentiated from the P septa and PS fibrosis by quantitative assessment of SHG/TPEF, and it represents a tendency of fibrosis regression in MASH patients.\u003c/p\u003e","manuscriptTitle":"Delicate and thin fibrous septa indicate a regression tendency in metabolic dysfunction-associated steatohepatitis patients with advanced fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 18:35:41","doi":"10.21203/rs.3.rs-4392304/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-05-31T19:30:42+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-11T02:02:39+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-11T01:00:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Hepatology International","date":"2024-05-08T23:28:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"hepatology-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hepi","sideBox":"Learn more about [Hepatology International](https://www.springer.com/journal/12072)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/hepi/default.aspx","title":"Hepatology International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a33c69ae-1b7c-4467-b482-4264d6db577d","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:34:19+00:00","versionOfRecord":{"articleIdentity":"rs-4392304","link":"https://doi.org/10.1007/s12072-024-10719-w","journal":{"identity":"hepatology-international","isVorOnly":false,"title":"Hepatology International"},"publishedOn":"2024-08-16 15:58:24","publishedOnDateReadable":"August 16th, 2024"},"versionCreatedAt":"2024-05-23 18:35:41","video":"","vorDoi":"10.1007/s12072-024-10719-w","vorDoiUrl":"https://doi.org/10.1007/s12072-024-10719-w","workflowStages":[]},"version":"v1","identity":"rs-4392304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4392304","identity":"rs-4392304","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}