Non-alcoholic steatohepatitis (NASH) is a metabolic disorder that often leads to other severe liver diseases, yet treatment options are limited. Endoplasmic reticulum (ER) stress is an important pathogenetic mechanism of NASH and plays a key role in tandem steatosis as well as liver inflammation. This study aims to develop a progressive NASH model through sustained lipid accumulation and to elucidate its molecular mechanism through IRE1α/TRAF2 complex. Male SD rats were fed a high-fat diet (HFD) for 4, 8, and 12 weeks to induce progressive NASH. MRNA sequencing and PPI analysis were used to screen core genes. Transmission electron microscopy, immunofluorescence staining, ELISA, qRT-PCR, and Western blotting were used at each time point to compare differences between each index of progressive NASH at 4, 8, and 12 weeks. Sustained lipid accumulation led to structural disruption of the ER, a reduction in ER number, and an increase of lipid droplet aggregation in hepatocytes. Persistent lipid accumulation led to a persistent increase in mRNA and protein expression of the IRE1α/TRAF2 complex, IKK/IκB/NF-κB signaling pathway and ASK1/JNK1 signaling pathway, and TNF-α, IL-1β, and IL-6 also continued to increase. Persistent lipid accumulation led to a persistent exacerbation of ER stress and inflammation in progressive NASH via the IRE1α/TRAF2 complex.

The associations between female infertility and epithelial ovarian cancer (EOC) or endometrial cancer (EC) have been reported in observational studies, but its causal relationship remains unknown. We intended to assess the causal effect of female infertility on EOCs and ECs using a two-sample Mendelian Randomization (MR) approach. Large pooled genome-wide association study (GWAS) datasets for female infertility (6481 cases and 68 969 controls), EOC (25 509 cases and 40 941 controls), and EC (12 906 cases and 108 979 controls) were derived from public GWAS databases and published studies. The Inverse Variance Weighted method, Weighted Median method, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier test were adopted for MR analyses. Our results suggested that genetically predicted infertility was positively associated with the risk of EOC (OR = 1.117, 95% CI = 1.003–1.245, P = .045), but did not find a causal relationship between infertility and EC (OR = 1.081, 95% CI = 0.954–1.224, P = .223). As to the reverse direction, our study did not obtain evidence from genetics that EOCs (OR = 0.974, 95% CI = 0.825–1.150, P = .755) and ECs (OR = 1.039, 95% CI = 0.917–1.177, P = .548) were associated with an increased risk of infertility. This large MR analysis supported a causal association between female infertility and increased risk of EOCs, but did not find a causal relationship between infertility and ECs. What is already known on this topic: Many studies have revealed that some people are more susceptible to undergoing cancers than others. Understanding the risk drivers that confer cancer risk will provide vital information to help public health officials make decisions that will benefit these at-risk populations. Observational studies have shown that female infertility is a high risk factor for ovarian cancer and endometrial cancer (EC), but the causal relationship has not been established. What this study adds: Our two-sample Mendelian Randomization study supported a causal association between female infertility and increased risk of epithelial ovarian cancers (EOCs), but did not find a causal relationship between infertility and ECs. No clear evidence was found for a potential causal effect of EOCs and ECs on female infertility. How this study might affect research, practice, or policy Our findings suggested that patients with infertility were at higher risk of developing EOC compared to the general population. Increased knowledge and regular surveillance of EOC in infertile women may help to identify early EOC.

Background: The causal relationship between work‐related factors and amyotrophic lateral sclerosis (ALS) is unclear. We used a Mendelian randomization (MR) analysis to investigate the unconfounded association between work‐related factors and ALS.Methods: Univariable MR analyses were conducted to evaluate the causal effects of work‐related factors on ALS. Instrumental variables from the UK Biobank on work‐related factors (n = 263,615) were used as proxies. The outcome dataset used ALS (n case = 20,806, n control = 59,804) summary‐level data from a large‐scale genome‐wide association study based on European ancestry. MR analysis used inverse variance weighted (IVW), MR‐Egger, and weighted median (WM) to assess causal effects and other methods of MR for sensitivity analysis. Further multivariable MR analyses were performed to explore potential mediating effects.Results: In univariable MR, IVW methods support evidence that genetically determined job involves heavy manual or physical work (OR = 2.04, 95% CI: 1.26–3.31; p = .004) was associated with an increased risk of ALS, and the WM methods also confirm this result (OR = 2.36, 95% CI: 1.30–4.28; p = .005). No evidence of heterogeneity or horizontal pleiotropy was found in the results. In multivariable MR, the association was absent after adjusting for smoking and blood pressure.Conclusions: Our MR analysis results demonstrate the potential causal relationship between jobs that involve heavy manual or physical work and ALS, which might be mediated by smoking and high systolic blood pressure.

Background and Objectives: Liver fibrosis results from chronic inflammation. Qijia Rougan decoction, a traditional Chinese medicinal formulation, shows hepatoprotective potential, yet its mechanisms remain unclear. This study aims to investigate its antifibrotic effects and underlying mechanisms. Methods: Rat liver fibrosis was induced by carbon tetrachloride (CCl ₄ ) and ethanol exposure. Histopathological assessment was performed using hematoxylin–eosin (HE) and Masson’s trichrome staining. Hepatic stellate cell (HSC) activation and autophagic processes were examined through western blot analysis, immunofluorescence staining, and other in vitro assays. Components of Qijia Rougan decoction were analyzed by BATMAN‐TCM platform. The pharmacological network was constructed using BATMAN‐TCM platform, while disease‐related targets were identified through DisGeNET database. Pathway enrichment analysis was conducted using KEGG pathway database. Results: Significant reductions in hepatic index and serum biomarkers (ALT, AST, ALP, TBA, and γ‐GT) were observed following Qijia Rougan decoction treatment, with maximal efficacy at 6 weeks. The decoction downregulated of LC3B and α‐SMA expression in fibrotic tissues. In vitro, it suppressed LPS‐induced α‐SMA expression and autophagosome formation in HSC‐T6 cells. Network pharmacology analysis of Qijia Rougan decoction identified 274 bioactive compounds and 12,883 potential targets, with pathway analysis indicating PI3K/AKT signaling as the predominant regulatory mechanism. Conclusion: Qijia Rougan decoction alleviates liver fibrosis, potentially by inhibiting HSC activation and autophagy processes via PI3K/AKT/mTOR pathway.

Background and Objective: Non-alcoholic fatty liver disease (NAFLD) represents the most prevalent chronic hepatic metabolic disorder globally. Without timely intervention, it can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis, and even hepatocellular carcinoma. Early detection and diagnosis are critical for disease management. metabolomics, a powerful tool for identifying diagnostic metabolic biomarkers of diseases, is frequently integrated with machine learning (ML) algorithms to improve analytical efficiency. This study aims to compare serum metabolomic profiles between NAFLD patients and healthy controls, identify differential metabolites, and employ machine learning algorithms to discover biomarkers with diagnostic value. Methods: This study enrolled 26 healthy controls and 165 patients diagnosed with NAFLD via ultrasound, and performed serum untargeted metabolomics analysis. Specifically, metabolomics techniques were used to detect serum metabolites, while orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to screen for significantly differential metabolites between groups and conduct pathway enrichment analysis. In the ML phase, the dataset was split at an 8:2 ratio: 80% of the data (131 NAFLD cases and 21 healthy controls) was used for model training, and 20% (34 NAFLD cases and five healthy controls) served as an independent test set to validate model performance. Results: Metabolomic differential analysis identified 942 significantly differential metabolites (656 upregulated and 286 downregulated) between the NAFLD and healthy control groups, which were primarily enriched in caffeine metabolism, cholesterol metabolism, and the FoxO and AMPK signaling pathways. After training and validating machine learning models, serum metabolites maresin 1, canavaninosuccinate, paraxanthine, and 1-methyluric acid demonstrated robust diagnostic performance for NAFLD and can serve as independent predictive biomarkers, with 1-methyluric acid exhibiting the highest diagnostic contribution. Conclusion: Integration of untargeted metabolomics and machine learning effectively distinguishes NAFLD patients from healthy controls. cholesterol metabolism, caffeine metabolism, and the FoxO and AMPK signaling pathways may participate in NAFLD pathogenesis. ML-validated metabolites 1-methyluric acid, paraxanthine, canavaninosuccinate, and maresin one hold potential as diagnostic biomarkers and therapeutic targets for NAFLD, with 1-methyluric acid exhibiting the highest diagnostic relevance. In summary, serum metabolomics provides stable, accurate biomarkers for NAFLD early warning and diagnosis, and this study offers data and resource support for optimizing its clinical.