ObjectiveTo identify protein biomarkers and potential therapeutic targets associated with metabolic dysfunction-associated fatty liver disease （MAFLD） through an integrative multi-omics analysis. MethodsLarge-scale plasma protein quantitative trait locus data were integrated with MAFLD genome-wide association study data， and a proteome-wide Mendelian randomization analysis was performed. Summary data-based Mendelian Randomization （SMR）/heterogeneity in dependent instruments （HEIDI） test and colocalization analysis were used to assess significant proteins. The independent samples t-test was used for comparison of continuous data with homogeneity of variance between two groups， and the Welch-corrected t-test was used for comparison of continuous data with heterogeneity of variance. The transcriptomic validation results were compared with the Mendelian randomization results to evaluate the changing trends of gene expression， and boxplots were used to display the expression distribution of candidate genes between groups. ResultsAmong 7 130 proteins analyzed， 40 showed a causal relationship with MAFLD， among which 26 increased the risk of MAFLD and 14 exerted a protective effect against MAFLD （FDR<0.05）. Nine proteins were supported by SMR/HEIDI （P_SMR<0.05， P_HEIDI>0.05）， and 14 showed a strong colocalization signal （PPH₄>0.9）. The transcriptomic analysis identified 5 differentially expressed proteins （all P<0.05）， i.e.， adenosine diphosphate-ribosylation factor-like GTPase 2， insulin-like growth factor binding protein 2， casein kinase 1 delta （CSNK1D）， leucine-rich repeat neuronal 1， and moesin （MSN）. In vitro validation showed upregulated expression levels of MSN （1.612±0.346 vs 0.569±0.077， P<0.05）， 14-3-3 protein theta （YWHAQ） （1.267±0.126 vs 0.313±0.029， P<0.05）， and CSNK1D （0.871±0.025 vs 0.431±0.080， P<0.05） and a downregulated expression level of histone deacetylase 8 （0.637±0.059 vs 1.097±0.260， P<0.05） in the MAFLD cell model. Functional annotation showed that CSNK1D participated in circadian and metabolic regulation， YWHAQ modulated the Hippo pathway and lipid signal transduction， and MSN was associated with cytoskeletal remodeling and fibrogenic responses. ConclusionThe integrative proteogenomic analysis in this study shows that CSNK1D， YWHAQ， and MSN may be key mediators in the pathogenesis of MAFLD. These proteins have the potential to serve as novel biomarkers and therapeutic targets， providing new directions for research on disease mechanisms and the development of intervention strategies.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective:To explore the role and mechanism of intestinal endotoxemia in the development of hepatopulmonary syndrome in rats.Methods:Rats were randomly divided into Experimental Group(N=10)and Control Group(N=10),and the rat model of Hepatopulmonary Syndrome was established by common bile duct ligation(CBD),and the contents of endotoxin,TNF-α and IL-6 were measured.Rat lung microvascular endothelial cells were isolated and cultured in vitro.Endotoxin induced the activation and proliferation of these cells,and the levels of TNF-α and IL-6 were detected.Results:The level of endotoxin in rats with hepatopulmonary syndrome was significantly elevated,which induced the production of TNF-α and IL-6,causing pulmonary vasodilation and hypoxemia.Conclusions:Intestinal endotoxemia plays an important role in the development of Hepatopulmonary Syndrome,and its possible mechanism is to induce pulmonary microvascular endothelial cells to produce a variety of inflammatory cytokines and to promote the development of pulmonary vasodilation and Hepatopulmonary Syndrome.It provides key experimental evidence and potential targets for the prevention and treatment strategies of hepatopulmonary syndrome.

The scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

Pulpotomy, which belongs to vital pulp therapy, has become a strategy for managing pulpitis in recent decades. This minimally invasive treatment reflects the recognition of preserving healthy dental pulp and optimizing long-term patient-centered outcomes. Pulpotomy is categorized into partial pulpotomy (PP), the removal of a partial segment of the coronal pulp tissue, and full pulpotomy (FP), the removal of whole coronal pulp, which is followed by applying the biomaterials onto the remaining pulp tissue and ultimately restoring the tooth. Procedural decisions for the amount of pulp tissue removal or retention depend on the diagnostic of pulp vitality, the overall treatment plan, the patient's general health status, and pulp inflammation reassessment during operation. This statement represents the consensus of an expert committee convened by the Society of Cariology and Endodontics, Chinese Stomatological Association. It addresses the current evidence to support the application of pulpotomy as a potential alternative to root canal treatment (RCT) on mature permanent teeth with pulpitis from a biological basis, the development of capping biomaterial, and the diagnostic considerations to evidence-based medicine. This expert statement intends to provide a clinical protocol of pulpotomy, which facilitates practitioners in choosing the optimal procedure and increasing their confidence in this rapidly evolving field.
Humans ; Calcium Compounds/therapeutic use* ; Consensus ; Dental Pulp ; Dentition, Permanent ; Oxides/therapeutic use* ; Pulpitis/therapy* ; Pulpotomy/standards*

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective To investigate the types and mechanisms of microglial cell death induced by interaction between palmitic acid(PA)and lipopolysaccharide(LPS).Methods BV-2 microglial cells were divided into three groups for apoptosis research,BSA group,PA treatment group,and staurosporine(STA)group.They were further divided into four groups for necrosis research,BSA group,BSA+inhibitor group,PA group,and PA+inhibitor group.Western blotting was conducted to assess the expression levels of key proteins involved in apoptosis and necrosis pathways.The effect of PA on microglial cells was validated through feeding a high-fat diet to Institute of Cancer Research(ICR)mice.Results Apoptotic microglia were observed in both BSA group and PA group,PA significantly induced the activation of caspase-3,caspase-7,and poly ADP-ribose polymerase(PARP).However,compared to the BSA group,the level of activated Caspase-7 in the STA group did not change significantly.Inhibition of ferroptosis,necroptosis,or autophagy did not protect against PA-induced cell damage,while the Caspase-11 inhibitor,wedelolactone(WE),significantly improved PA induced cell damage.This study also found that PA could promote LPS entry into microglial cells and induce pyroptosis.This phenomenon and the protective effect of WE were further confirmed in a high-fat diet mouse model through immunofluorescent staining and Western blotting.Conclusion PA induces apoptosis and pyroptosis in microglial cells,while simultaneously promoting LPS entry into microglial cells and inducing pyroptosis.

OBJECTIVE: To describe survival trends and global patterns of esophageal cancer (EC) using survival data from population-based cancer registries. METHODS: We systematically searched PubMed, EMBASE, Web of Science, SEER, and SinoMed databases for articles published up to 31 December 2023. Eligible EC survival estimates were evaluated according to country or region, period, sex, age group, pathology, and disease stage. RESULTS: After 2010, Jordan exhibited the highest age-standardized 5-year relative survival rates (RSRs)/net survival rates (NSRs) at 41.1% between 2010 and 2014, while India had the lowest, at 4.1%. Survival rates generally improved with diagnostic age across most countries, with significant increases in South Korea and China, of 12.7% and 10.5% between 2000 and 2017, respectively. Survival was higher among women compared to men, ranging from 0.4%-10.9%. Survival rates for adenocarcinoma and squamous cell carcinoma were similar, differing by about 4%. In China, the highest age-standardized RSRs/NSRs was 33.4% between 2015 and 2017. Meanwhile, the lowest was 5.3%, in Qidong (Jiangsu province) between 1992-1996. CONCLUSION Global EC survival rates have improved significantly in recent decades, but substantial geographical, sex, and age disparities still exist. In Asia, squamous cell carcinoma demonstrated superior survival rates compared to adenocarcinoma, while the opposite trend was observed in Western countries. Future research should clarify the prognostic factors influencing EC survival and tailor prevention and screening strategies to the changing EC survival patterns.
Humans ; Esophageal Neoplasms/mortality* ; Registries ; Male ; Female ; Survival Analysis ; Middle Aged ; Survival Rate ; Aged ; Global Health