Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver disease worldwide, affecting approximately 32%-38% of the adult population and posing a growing public health burden. MASLD represents a continuous disease spectrum ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), progressive hepatic fibrosis, cirrhosis, and ultimately hepatocellular carcinoma (HCC). The pathological core of MASLD lies in disruption of hepatic lipid metabolic homeostasis, characterized by an imbalance among de novo lipogenesis, fatty acid β-oxidation, and very-low-density lipoprotein (VLDL)-mediated lipid export. This metabolic disequilibrium subsequently drives inflammatory injury and fibrotic progression. Among the multiple regulatory pathways involved, thyroid hormone (TH) signaling has emerged as a central regulator of hepatic metabolic homeostasis. The liver is a major peripheral target organ of TH action, where TH predominantly exerts its metabolic effects through thyroid hormone receptor β (TRβ). Large-scale epidemiological studies and meta-analyses have demonstrated that hypothyroidism is significantly associated with increased MASLD prevalence, more severe histological injury, and advanced hepatic fibrosis, suggesting that dysregulation of TH signaling may participate throughout the entire MASLD disease spectrum. At the molecular level, TH regulates hepatic lipid metabolism by coordinating suppression of lipogenesis, enhancement of mitochondrial fatty acid oxidation, and promotion of VLDL assembly and secretion through integrated genomic actions of the T3-TRβ axis and non-genomic signaling pathways. Across different stages of MASLD, TH signaling exerts stage-dependent protective effects. In the steatosis stage, TH improves metabolic flexibility by modulating insulin sensitivity, glucose metabolism, and lipid droplet clearance, thereby alleviating early lipotoxic stress. During progression to MASH, TH attenuates inflammatory amplification by improving mitochondrial homeostasis, suppressing activation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, and modulating the gut-liver axis microenvironment. In advanced stages, TH signaling influences hepatic stellate cell activation and extracellular matrix deposition, partly through interaction with the transforming growth factor-β (TGF-β)/SMAD pathway, while alterations in intrahepatic TH availability, mediated by dynamic changes in iodothyronine deiodinase 1 (DIO1), contribute to fibrosis progression and hepatocellular dedifferentiation. In hepatocellular carcinoma, coordinated downregulation of TRβ and DIO1 establishes a tumor-associated hypothyroid state that promotes metabolic reprogramming and tumor progression. The clinical relevance of TH signaling in MASLD has been underscored by the recent approval of Resmetirom, a liver-targeted TRβ‑selective agonist, for the treatment of non-cirrhotic MASH with moderate-to-severe fibrosis (F2-F3). This approval represents a landmark transition from mechanistic understanding to metabolism-centered precision therapy in MASLD. Clinical trials have demonstrated that Resmetirom not only improves key histological endpoints, including MASH resolution and fibrosis regression, but also favorably modulates atherogenic lipid profiles, highlighting the therapeutic potential of selectively targeting hepatic TH pathways. This review systematically summarizes the multidimensional regulatory roles of TH across the MASLD disease spectrum and discusses emerging diagnostic and therapeutic implications of TH-based interventions, aiming to inform future mechanistic research and optimize clinical management strategies.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.
Humans ; Artificial Intelligence ; Esophageal Squamous Cell Carcinoma/diagnosis* ; Esophageal Neoplasms/diagnosis* ; Precancerous Conditions/diagnosis*

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Congenital orofacial cleft, the most common birth defect in the maxillofacial region, exhibits a wide range of prognosis depending on the severity of deformity and underlying etiology. Non-syndromic congenital orofacial clefts typically present with milder deformities and more favorable treatment outcomes, whereas syndromic congenital orofacial clefts often manifest with concomitant organ abnormalities, which pose greater challenges for treatment and result in poorer prognosis. This consensus provides an elaborate classification system for varying degrees of orofacial clefts along with corresponding diagnostic and therapeutic guidelines. Results serve as a crucial resource for families to navigate prenatal screening results or make informed decisions regarding treatment options while also contributing significantly to preventing serious birth defects within the development of population.
Humans ; Cleft Lip/diagnosis* ; Cleft Palate/diagnosis* ; Consensus ; Prenatal Diagnosis ; Female

Objective Investigate the prevalence and epidemiological characteristics of flatfoot among a sample of adolescents(grade 3,5 and 7)in the Northeastern Chongqing,analyze the influencing factors,to provide a scientific basis for selecting intervention strategies.Methods Utilizing plantar optical observation mirrors and questionnaires,a flatfoot screening was conducted among grade 3,5 and 7 school-aged adolescents from 10 urban and rural schools in Wanzhou District,Chongqing.Relevant parameters were recorded,and uni-variate logistic regression analysis was performed considering gender,genetics,BMI,exercise duration,foot-wear,history of ankle trauma,awareness of risks,medical consultation history,and knowledge of preventive measures for flatfoot prevalence.Variables with statistically significant differences in the univariate logistic a-nalysis were included in the multivariate logistic regression analysis.Results In a sample of 5 058 school-age adolescents,2 163 were diagnosed with flatfoot,yielding a prevalence rate of 42.76%(2 163/5 058).Urban and rural areas difference:urban areas had a rate of 43.70%(1 444/3 304),while rural areas had a rate of 40.99%(719/1 754),no statistically significant difference was found between the two(χ2=3.334,P=0.068).Gender difference:males had a rate of 45.56%(1 212/2 660),and females had a rate of 39.66%(951/2 398),the prev-alence rate was higher in males than in females,with a statistically significant difference(χ2=17.730,P<0.001).Grade difference:the prevalence rate in grade 3 was 49.45%(822/1 662),in grade 5 was 42.78%(727/1 699),and in grade 7 was 36.18%(614/1 697),there was a statistically significant among different grades(χ2=60.473,P<0.001),while the prevalence rate in grade 3 was significantly higher than that in grade 5 and 7,and it showed a decreasing trend with the increase of the students'grade level(χ2 trend=24.223,P<0.001).Genetic differences:the proportion of individuals with a positive genetic predisposition to flat feet was 15.53%(336/2 163),the influence of genetics on the occurrence of flat feet was statistically significant(χ2=70.232,P<0.001);furthermore,there was a higher genetic tendency towards severe flatfoot,with statistically significant difference(χ2trend=44.976,P<0.001).BMI difference:comparison of flatfoot severity across differ-ent BMI strata showed a significant difference(χ2trend=21.118,P=0.002),indicating that varying BMI levels affect the prevalence of flatfoot.Multivariate logistic regression analysis showed that gender,genetics,and BMI(18.5-<25.0 kg/m2)were the influencing factors of flatfoot among adolescents in Northeastern Chongqing(P<0.05).Conclusion The incidence of flatfoot among school-age adolescents decreases with in-creasing grade,with lower rates among girls than boys,and there is a genetic predisposition.Most flatfoot are mild,and BMI is a high-risk factor.Early intervention should be implemented for flatfoot in adolescents.

OBJECTIVE: To explore the predictive value of age, D-Dimer and fibrinogen (FIB) for non-thrombotic. METHODS: A retrospective analysis was conducted on a total of 1 384 coagulation test cases from January to August 2024 at Nanning No. 8 People's Hospital. Among them, the control group comprised 400 non-thrombotic cases with D-Dimer test results within the reference range. The thrombotic group comprised 57 clinically diagnosed thrombotic patients. The research group comprised 927 non-thrombotic cases with D-Dimer levels exceeding the reference range. The diagnosis treatment records, age information, plasma D-Dimer, and FIB test results of each group were collected. The changes and correlations of age, D-Dimer, and FIB indicators were compared and analyzed among the three groups. A new combination factor was generated by fitting a Logistic binary regression model. ROC curves were used to evaluate the predictive value of each index for non-thrombotic disease in both the research group and the thrombotic group. RESULTS: Compared with the control group, the thrombotic group and the research group had significantly higher age, D-Dimer, and FIB levels (P < 0.001). Further comparative analysis showed that the research group had significantly lower age and D-Dimer levels than the thrombotic group, the FIB level was significantly higher than that of the thrombotic group (P < 0.001). Spearman correlation analysis showed that the correlation coefficient between age and D-Dimer in the research group was higher than that in the control group and thrombotic group (P < 0.01), the thrombotic group had the highest negative correlation coefficient between FIB and D-Dimer (P < 0.01). The ROC curve analysis results showed that the AUC values of age, plasma D-dimer, and FIB independently predicted non-thromb diseases were 0.726, 0.735, and 0.611, respectively. A new combined factor was generated by fitting age, D-dimer, and FIB with a logistic binary regression model. The AUC value of the combined prediction of non-thrombotic diseases was the maximum at 0.832, which had high diagnostic value, and its sensitivity and specificity were 0.572 and 0.070. CONCLUSION Elevated D-dimer levels were associated with age, increased FIB, and a variety of non-thrombotic diseases, and combination of age, D-dimer, and FIB had a certain predictive value for non-thrombotic diseases, but the combined model had a low specificity, other information needs to be combined in the clinic to improve diagnostic accuracy.
Humans ; Fibrin Fibrinogen Degradation Products ; Retrospective Studies ; Fibrinogen ; Predictive Value of Tests ; Thrombosis ; Age Factors ; ROC Curve ; Male ; Female ; Middle Aged ; Adult