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

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Recent studies have shown that shorter periods of ejaculatory abstinence may enhance certain sperm parameters, but the molecular mechanisms underlying these improvements are still unclear. This study explored whether reduced abstinence periods could improve semen quality, particularly for use in assisted reproductive technologies (ART). We analyzed semen samples from men with normal sperm counts ( n = 101) and those with low sperm motility or concentration ( n = 53) after 3-7 days of abstinence and then after 1-3 h of abstinence, obtained from the Reproductive & Genetic Hospital of CITIC-Xiangya (Changsha, China). Physiological and biochemical sperm parameters were evaluated, and the dynamics of transfer RNA (tRNA)-derived fragments (tRFs) were analyzed using deep RNA sequencing in five consecutive samples from men with normal sperm counts. Our results revealed significant improvement in sperm motility and a decrease in the DNA fragmentation index after the 1- to 3-h abstinence period. Additionally, we identified 245 differentially expressed tRFs, and the mitogen-activated protein kinase (MAPK) signaling pathway was the most enriched. Further investigations showed significant changes in tRF-Lys-TTT and its target gene mitogen-activated protein kinase kinase 2 ( MAP2K2 ), which indicates a role of tRFs in improving sperm function. These findings provide new insights into how shorter abstinence periods influence sperm quality and suggest that tRFs may serve as biomarkers for male fertility. This research highlights the potential for optimizing ART protocols and improving reproductive outcomes through molecular approaches that target sperm function.
Male ; Humans ; Spermatozoa/metabolism* ; RNA, Transfer/genetics* ; Sperm Motility/genetics* ; Adult ; Semen Analysis ; Sexual Abstinence/physiology* ; Sperm Count ; DNA Fragmentation

Objective:To analyze the symptoms,diagnosis and treatment of upper urinary tract calculi patients combined with mild and moderate benign prostatic hyperplasia(BPH)after ureteral stent implantation.Methods:One hundred and six BPH pa-tients who were hospitalized for upper urinary tract calculi and had ureteral stents retained from January 2019 to December 2022 were selected and divided into 2 weeks group and 4 weeks group according to the time of removal of ureteral stents after surgery.Their gener-al clinical data were analyzed and compared.International Prostatic Symptom Scale(IPSS),postoperative ureteral Stent Symptom Questionnaire(USSQ),and incidence of adverse events after ureteral stent removal were recorded before and after removal.Results:The scores of IPSS were significantly increased in all patients,and symptoms in urinary tract had improved significantly after discharge(P＜0.05).Compared with the 2 weeks group,the USSQ score of the 4 weeks group was significantly increased(P＜0.05).And no significant adverse event was observed in the 2 weeks group after the removal of ureteral sten.Conclusion:IPSS score and USSQ score increased significantly during stent implantation in BPH patients with lithiasis.And complications increased sig-nificantly over time.Following thorough clinical assessment,early ureteral stent removal demonstrates both safety and efficacy,repre-senting an optimal therapeutic approach in selected cases.

The increasingly widespread application of artificial intelligence(AI)technology in the field of sports biomechanics has provided more effective technological support for competitive sports science to help athletes improve their performance.Using AI technologies and methods to obtain athletes' biomechanical data,analyze the biomechanical characteristics of movement techniques,design training plans,adjust tactical strategies,and prevent sports injuries has become an integral part of high-level competitive sports.This paper summarizes the current applications of AI technology in sports biomechanics through a literature review,including its applications in movement technique analysis for performance enhancement,tactical analysis,and sports injury prevention.The aim is to provide new ideas for further promoting the application of AI technology in sports biomechanics,offer new methods and means for competitive sports science and technology,and create more possibilities for the development of AI technology itself.

The increasingly widespread application of artificial intelligence(AI)technology in the field of sports biomechanics has provided more effective technological support for competitive sports science to help athletes improve their performance.Using AI technologies and methods to obtain athletes' biomechanical data,analyze the biomechanical characteristics of movement techniques,design training plans,adjust tactical strategies,and prevent sports injuries has become an integral part of high-level competitive sports.This paper summarizes the current applications of AI technology in sports biomechanics through a literature review,including its applications in movement technique analysis for performance enhancement,tactical analysis,and sports injury prevention.The aim is to provide new ideas for further promoting the application of AI technology in sports biomechanics,offer new methods and means for competitive sports science and technology,and create more possibilities for the development of AI technology itself.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Objective: To determine the inhibitory effects of pachymic acid on lung adenocarcinoma (LUAD) cells and elucidate its underlying mechanism. Methods: CCK-8, wound healing, Transwell, Western blot, tube formation, and immunofluorescence assays were carried out to measure the effects of various concentrations of pachymic acid on LUAD cell proliferation, metastasis, angiogenesis as well as autophagy. Subsequently, molecular docking technology was used to detect the potential targeted binding association between pachymic acid and protein tyrosine phosphatase 1B (PTP1B). Moreover, PTP1B was overexpressed in A549 cells to detect the specific mechanisms of pachymic acid. Results: Pachymic acid suppressed LUAD cell viability, metastasis as well as angiogenesis while inducing cell autophagy. It also targeted PTP1B and lowered PTP1B expression. However, PTP1B overexpression reversed the effects of pachymic acid on metastasis, angiogenesis, and autophagy as well as the expression of Wnt3a and β-catenin in LUAD cells. Conclusions: Pachymic acid inhibits metastasis and angiogenesis, and promotes autophagy in LUAD cells by modulating the Wnt/ β-catenin signaling pathway via targeting PTP1B.

italic>Anoectochilus roxburghii liquid (spray, a hospital preparation of Wu Mengchao Hepatobiliary Hospital of Fujian Medical University) has shown a good clinical treatment effect during the COVID-19 pandemic, but its material basis and mechanism of action are still unclear. In this study, network pharmacology and molecular docking methods were used to predict the molecular mechanism of A. roxburghii liquid against COVID-19, and pharmacodynamic experiments in vitro were conducted to study the interaction between the current targets with clear preventive and therapeutic effects and the key components of A. roxburghii liquid. UPLC-MS and database were used to compare and analyze the active ingredients in the liquid, and 17 potential active ingredients with good drug-like properties were screened by in vivo pharmacokinetics process in SwissADME database. SwissTargetPrediction and GeneCards were searched to find 93 common targets. Cytoscape 3.8.2 software was used to construct the "component-target" network map, and the Metascape platform was used for gene function annotation and pathway enrichment analysis. It was found that the extract could regulate the positive response to external stimuli, inflammatory response, cytokine production and other biological processes by binding the active ingredients such as isorhamnetin, kaempferol, luteolin, quercetin and apigenin to the common targets (NOS3, MPO, MMP3, etc.), and play an anti-COVID-19 role. In the angiotensin-converting enzyme 2 (ACE2) activity inhibition assay, it was found that the stock solution of A. roxburghii liquid (for spray), and the supernatant after removing polysaccharides (mainly containing flavonoids) could to some extent inhibit the activity of ACE2. Crucially, in the experiment of 2019-nCOV-S pseudovirus infecting HEK-293T-ACE2 cells, we found that A. roxburghii liquid may exert anti-COVID-19 effects by blocking the binding of SARS-CoV-S protein to ACE2.