Metabolic dysfunction-associated fatty liver disease （MAFLD） is the chronic liver disease with the highest prevalence rate globally， and its disease spectrum encompasses a progressive continuum from hepatic steatosis to metabolic dysfunction-associated steatohepatitis， liver fibrosis， liver cirrhosis， and eventually hepatocellular carcinoma. In recent years， the research paradigm of MAFLD has shifted from “lipid accumulation” to “lipid metabolic reprogramming”， which， as a critical link in disease development and progression， is regulated by multidimensional molecular networks. This article systematically reviews the molecular mechanisms of lipid metabolic reprogramming in MAFLD from the following aspects： it analyzes the core role of lipid metabolism disorders， with a focus on how dysregulation of membranous organelle interactions， such as endoplasmic reticulum-mitochondria contacts， mediates metabolic imbalance； it concentrates on novel programmed cell death pathways induced by lipotoxicity， especially ferroptosis， and reveals the pathways through which they exacerbate liver injury； furthermore， it comprehensively analyzes the multi-layered regulatory network of the key signaling pathways such as adenosine monophosphate-activated protein kinase and nuclear factor-erythroid 2-related factor 2 and explores the emerging mechanisms such as liquid-liquid phase separation and microprotein regulation； finally， it provides an outlook on the potential therapeutic strategies targeting critical nodes of lipid metabolic reprogramming， so as to provide a theoretical basis for investigating the pathological mechanisms of MAFLD and developing precise interventions.

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

In most types of erectile dysfunction, particularly in advanced stages, typical pathological features observed are reduced parenchymal cells coupled with increased tissue fibrosis. However, the current treatment methods have shown limited success in reversing these pathologic changes. Recent research has revealed that changes in autophagy levels, along with alterations in apoptosis and fibrosis-related proteins, are linked to the progression of erectile dysfunction, suggesting a significant association. Autophagy, known to significantly affect cell fate and tissue fibrosis, is currently being explored as a potential treatment modality for erectile dysfunction. However, these present studies are still in their nascent stage, and there are limited experimental data available. This review analyzes erectile dysfunction from a pathological perspective. It provides an in-depth overview of how autophagy is involved in the apoptotic processes of smooth muscle and endothelial cells and its role in the fibrotic processes occurring in the cavernosum. This study aimed to develop a theoretical framework for the potential effectiveness of autophagy in preventing and treating erectile dysfunction, thus encouraging further investigation among researchers in this area.
Male ; Humans ; Autophagy/physiology* ; Apoptosis/physiology* ; Erectile Dysfunction/physiopathology* ; Fibrosis ; Penis/pathology* ; Animals ; Endothelial Cells/pathology* ; Myocytes, Smooth Muscle/pathology*

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.

Objective To investigate the current situation and influencing factors of latent tuberculosis infection(LTBI)among close contacts of positive etiology pulmonary tuberculosis(PTB)patients,provide basis for formula-ting intervention measures for LTBI.Methods A multi-stage stratified cluster random sampling method was used to select close contacts of positive etiology PTB patients from 39 districts and counties in Chongqing City as the study objects.Demographic information was collected by questionnaire survey and the infection of Mycobacterium tuberculosis was detected by interferon gamma release assay(IGRA).The influencing factors of LTBI were analyzed by x2 test and binary logistic regression model.Results A total of 2 591 close contacts were included,the male to female ratio was 0.69∶1,with the mean age of(35.72±16.64)years.1 058 cases of LTBI were detected,Myco-bacterium tuberculosis latent infection rate was 40.83％.Univariate analysis showed that the infection rate was dif-ferent among peoples of different age,body mass index(BMI),occupation,education level,marital status,wheth-er they had chronic disease or major surgery history,whether they lived together with the indicator case,and whether the cumulative contact time with the indicator case ≥250 hours,difference were all statistically significant(all P＜0.05);infection rate presented increased trend with the increase of age and BMI(both P＜0.001),and decreased trend with the increase of education(P＜0.05).Logistic regression analysis showed that age 45-54 years old(OR=1.951,95％CI:1.031-3.693),age 55-64 years old(OR=2.473,95％CI:1.279-4.781),other occupations(OR=0.530,95％CI:0.292-0.964),teachers(OR=0.439,95％CI:0.242-0.794),students(OR=0.445,95％CI:0.233-0.851),junior high school education or below(OR=1.412,95％CI:1.025-1.944),BMI＜18.5 kg/m2(OR=0.762,95％CI:0.586-0.991),co-living with indicator cases(OR=1.621,95％CI1.316-1.997)and cumu-lative contact time with indicator cases ≥250 hours(OR=1.292,95％CI:1.083-1.540)were the influential fac-tors for LTBI(all P＜0.05).Conclusion The close contacts with positive etiology PTB have a high latent infection rate of Mycobacterium tuberculosis,and it is necessary to pay attention to close contacts of high age,farmers,and frequent contact with patients,and take timely targeted interventions to reduce the risk of occurrence of disease.

Background: Intrahepatic cholangiocarcinoma (ICC) is a highly desmoplastic tumor with poor prognosis even after curative resection. We investigated the associations between the composition of the ICC stroma and immune cell infiltration and aimed to develop a stromal-immune signature to predict prognosis in surgically treated ICC. Patients and methods: We recruited 359 ICC patients and performed immunohistochemistry to detect α-smooth muscle actin (α-SMA), CD3, CD4, CD8, Foxp3, CD68, and CD66b. Aniline was used to stain collagen deposition. Survival analyses were performed to detect prognostic values of these markers. Recursive partitioning for a discrete-time survival tree was applied to define a stromal-immune signature with distinct prognostic value. We delineated an integrated stromal-immune signature based on immune cell subpopulations and stromal composition to distinguish subgroups with different recurrence-free survival (RFS) and overall survival (OS) time. Results: We defined four major patterns of ICC stroma composition according to the distributions of α-SMA and collagen: dormant (α-SMAlow/collagenhigh), fibrogenic (α-SMAhigh/collagenhigh), inert (α-SMAlow/collagenlow), and fibrolytic (α-SMAhigh/collagenlow). The stroma types were characterized by distinct patterns of infiltration by immune cells. We divided patients into six classes. Class I, characterized by high CD8 expression and dormant stroma, displayed the longest RFS and OS, whereas Class VI, characterized by low CD8 expression and high CD66b expression, displayed the shortest RFS and OS. The integrated stromal-immune signature was consolidated in a validation cohort. Conclusion We developed and validated a stromal-immune signature to predict prognosis in surgically treated ICC. These findings provide new insights into the stromal-immune response to ICC.