BackgroundLow medication compliance among patients with severe mental disorders increases the disease burden on both the patients' families and the society. Medication adherence is influenced by numerous factors. Traditional methods such as Logistic regression struggle to quantify the importance of these factors. By introducing Extreme Gradient Boosting （XGBoost） combined with Shapley Additive Explanations （SHAP）， enables the quantification of the relative contribution weights of each factor， providing support for identifying the core influencing factors. ObjectiveTo explore the influencing factors of medication adherence among patients with severe mental disorders in Zhuhai， aiming to provide references for optimizing patient management strategies. MethodsExtract the data of patients with severe mental disorders who were registered on the mental health system platform in Zhuhai City from January 1， 2023 to March 31， 2025. A total of 9 329 patients were finally included for analysis. Influencing factors were screened using univariate analysis and multivariate logistic regression analysis， and an XGBoost model combined with the SHAP algorithm was constructed to quantify the importance of each influencing factor. ResultsAmong 9 329 patients， 8 446 demonstrated medication adherence， yielding an adherence rate of 90.53%. Multivariable analysis identified several risk factors significantly associated with medication non-adherence， being unmarried （OR=1.237， 95% CI： 1.019–1.502） or divorced （OR=1.389， 95% CI： 1.038–1.832）， a diagnosis of mental retardation with psychiatric disorders （OR=3.025， 95% CI： 2.402–3.796） or paranoid psychosis （OR=5.117， 95% CI： 3.086–8.299）， a disease duration of 2–4 years （OR=1.355， 95% CI： 1.085–1.696）， 4–6 years （OR=2.143， 95% CI： 1.671–2.747）， or >6 years （OR=1.681， 95% CI： 1.365–2.079）， lack of guardian subsidies （OR=1.412， 95% CI： 1.099–1.801）， absence of a disability certificate （OR=1.900， 95% CI： 1.588–2.282）， not being enrolled in care and support groups （OR=1.384， 95% CI： 1.183–1.617） or community services （OR=1.313， 95% CI： 1.042–1.645）， and not cohabiting with a guardian （OR=1.257， 95% CI： 1.048–1.501）. Conversely， the enrollment in special outpatient disease programs （OR=0.716， 95% CI： 0.609–0.842） and a family history of mental illness （OR=0.713， 95% CI： 0.503–0.982） were identified as protective factors. The XGBoost model exhibited robust predictive performance， with a sensitivity of 0.433， specificity of 0.944， accuracy of 0.891， Area Under the Curve （AUC） of 0.837， and F1 value of 0.449. Feature importance ranking indicated that the top three factors were disease duration， diagnosis， and the acquisition of disability certificates. ConclusionPolicy-based support （acquisition of disability certificates， special outpatient disease enrollment） and clinical disease characteristics （disease duration， diagnosis type） are key factors affecting medication adherence among patients with severe mental disorders in Zhuhai City. ［Funded by Zhuhai Medical Research Project （number， 2220009000281）］

ObjectiveThe most prevalent mRNA modification, N6-methyladenosine (m⁶A) plays an important role in various RNA metabolism, including gene expression and translation. By recruiting different “reader” proteins and their cofactors, m⁶A modification can affect messenger RNA (mRNA) degradation, splicing, nuclear export and translation. However, the selective mechanism by which m⁶A sites regulate mRNA translation through m⁶A reader YTHDF1 binding remains poorly understood, due to a lack of computational methods for identifying context-specific m⁶A sites that regulate translation. To address this, we developed a novel computational framework named m⁶ATEpre, the first tool designed to predict cell-specific m⁶A sites that regulate translation efficiency. Methodsm⁶ATEpre integrates multi-omics data, introduces a novel feature representation strategy for m⁶A site sequences, and employs an autoencoder to effectively capture embedded feature representations. Specifically, m⁶ATEpre first integrated MeRIP-seq data and PAR-CLIP data through overlapping m⁶A sites with YTHDF1 binding sites and identified YTHDF1-mediated m⁶A sites. Then, m⁶ATEpre detected the translation gene by analyzing the Ribo-seq data under YTHDF1 knockdown vs control condition. Genes whose translation is mediated by YTHDF1 in an m⁶A-dependent manner were identified by a significant decrease in translation efficiency upon YTHDF1 knockdown. Next, we proposed a binary vector indicating the presence or absence of YTHDF1 binding motifs to characterize each m⁶A site sequence. This represents a novel feature representation strategy for m⁶A sites. m⁶ATEpre utilized the autoencoder to extract the potentially important feature representations and constructed a multilayer perceptron neural networks model to predict potential m⁶A sites that regulating translation efficiency. ResultsA comprehensive evaluation of m⁶ATEpre was conducted through a series of experiments. We compared its performance against that of a similar prediction task model, as well as other classifiers. The results indicate that m⁶ATEpre achieved the best prediction performance. In addition, we analyzed different feature representation strategies and performed ablation experiments to validate the rationality of the model design. The results demonstrate that our proposed feature representation strategy has a greater advantage in improving prediction performance. In the HeLa cell line, bioinformatic analysis of the metagene distribution and sequence minimum free energy of m⁶A sites regulating translation efficiency (m⁶A-reg-TE sites) revealed their specific properties in translation regulation. Functional enrichment analysis indicated that m⁶A-reg-TE genes are associated with specific biological processes and KEGG pathways. By integrating the binding sites of YTHDF1 co-factors with m⁶A-reg-TE sites, we revealed that YTHDF1-mediated and m⁶A-dependent translation efficiency regulation requires the cooperation of multiple translation-regulatory RNA-binding proteins among its co-factors in the HeLa cell line. Furthermore, we extended our predictions to the dataset of the HEK293T cell line. Similarly, bioinformatic analysis of the metagene distribution and functional enrichment revealed the cell-specific characteristic of these predicted m⁶A-reg-TE sites in HEK293T cells. Likewise, integrated analysis of multiple YTHDF1 co-factors and m⁶A-reg-TE sites predicted in the HEK293T cell line reveals their m⁶A-dependent cooperation in regulating translation efficiency. Conclusionm⁶ATEpre is a timely tool that will advance our understanding of the mechanisms of m⁶A regulation in translation efficiency. The source code and datasets used in this work can be downloaded from https://www.scidb.cn/s/bAZZFr.

Drawing on recent advances in modern fascial anatomy and biomechanics， this paper interprets the biomechanical implications of yin-yang balance at the physical level from the perspective of holistic force lines. It posits that holistic force lines represent the functional expression of mechanical load transmission and integration within the myofascial network； the channel sinews system and myofascial chains exhibit significant similarities in terms of their distribution， functional attributes， and principles of holistic regulation， and can thus be understood as structural-functional carriers of yin-yang balance. Accordingly， this paper provides a biomechanical interpretation of the yin-yang relationship from four aspects， including interdependence， mutual restriction， dynamic balance through waxing and waning， and mutual transformation， proposing that in a healthy state， the body relies on the continuous integration， distribution， and readjustment of the myofascial tension network to maintain overall coordination， whereas disease states are often associated with compensatory transmission of local tension abnormalities along holistic force lines， the accumulation of imbalances， and structural rigidity. The holistic force-line perspective provides a biomechanical explanatory framework for the theory of yin-yang balance and offers a new theoretical basis for clinical diagnosis and treatment to shift from empirical holistic balancing toward holistic force-line balancing based on posture， function， and tension transmission pathways.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Objective To explore the significance of the toxicity mechanism of butyl benzyl phthalate(BBP)in prevention and treatment of pulmonary fibrosis.Methods In this study,we used network toxicology combined with molecular docking technology to screen the targets of BBP and those related to pulmonary fibrosis through PubChem,GeneCards and other databases,and analyzed the intersecting genes by using a Wayne diagram.Protein-protein interaction networks were constructed to screen the core targets,and the pathway mechanisms were revealed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.Finally,molecular docking using AutoDock was performed to verify the binding patterns of core targets and BBP.Results A total of 91 potential targets of BBP-induced lung fibrosis were screened,among which PTGS2 and CYP3A4 were the core targets(binding energies of-1.84 kcal/mol and-1.68 kcal/mol,respectively).Enrichment analysis showed that BBP regulated the fibrosis process through G protein-coupled receptor signaling pathway,calcium signaling pathway and cyclic adenosine monophosphate signaling pathway.Molecular docking confirmed that BBP was stably bound to the core target through hydrogen bonding and hydrophobic interaction.Conclusion This study provides preliminary insights into the molecular mechanism of BBP-induced pulmonary fibrosis through network toxicology,and PTGS2 and CYP3A4 may play key roles in BBP-induced pulmonary fibrosis,which provides a novel reference for the exploration of the mechanism of toxicant-disease association.

[Purpose]To analyze the epidemiology and disease burden of gastric cancer in China,Japan and Republic of Korea from 1990 to 2021 and to predict changing trends from 2022 to 2031.[Methods]Data were obtained from the Global Burden of Disease(GBD)database.Age-stan-dardized incidence rate(ASIR),age-standardized mortality rate(ASMR),crude incidence rate(CIR),crude mortality rate(CMR),and disability-adjusted life years(DALY)rate for China,Japan and Republic of Korea from 1990 to 2021 were analyzed.Joinpoint regression software was used to analyze trends and calculate annual percentage changes.The autoregressive integrated moving average(ARIMA)model was applied to predict incidence and mortality from 2022 to 2031.[Results]In 2021,China had 611 799 new gastric cancer cases and 445 013 deaths,with an ASIR of 29.05/105 and an ASMR of 21.51/105,both significantly higher than those in Japan and Republic of Korea.Among men in China,both the ASIR(44.48/105)and ASMR(32.61/105)were the highest among the three countries,exceeding those in Japan(38.77/105,20.26/105)and Re-public of Korea(38.98/105,20.50/105).Among women,China had the highest number of new cases,but its ASIR(15.23/105)was slightly lower than Republic of Korea's(15.57/105)and higher than Japan's(14.66/105).However,China's ASMR among women(12.02/105)remained significantly higher than Japan's(7.64/105)and Republic of Korea's(8.08/105).From 1990 to 2021,ASIR,ASMR and DALY rates for gastric cancer declined in all three countries,but the reduction in China was significantly smaller than that in Japan and Republic of Korea,with Republic of Korea showing the steepest declines across all indicators.ARIMA model predictions indicated significant differences in disease burden among the three countries from 2022 to 2031.ASIR is projected to continue declining in China and Republic of Korea,reaching 22.87/105 and 12.45/105,respectively by 2031,while in Japan it is predicted to rise to 26.55/105.ASMR is projected to decline in all three countries,reaching 13.71/105(China),10.44/105(Japan),and 9.08/105(Republic of Korea)in 2031.[Conclusion]Among China,Japan and Republic of Korea,China had the highest ASIR and ASMR of gastric cancer in 2021.Moreover,from 1990 to 2021,the reductions in ASIR,ASMR and DALY rates for gastric cancer were the smallest in China compared to Japan and Republic of Korea.These findings suggest that the disease burden of gastric cancer remains substantial in China,high-lighting the need for increased efforts in gastric cancer screening and early diagnosis and treatment.

Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

BACKGROUND:At present,the pathogenesis of osteoarthritis is still unclear,and there is a lack of effective means to control the disease.Research on osteoarthritis is mostly concentrated in the field of immunity,and there are few studies in the field of oxidative stress. OBJECTIVE:To explore the roles of oxidative stress and immune infiltration in osteoarthritis and to predict related miRNAs and therapeutic agents. METHODS:The GSE55235 dataset(10 samples of osteoarthritis and 10 healthy control samples)and the GSE55457 dataset(10 samples of osteoarthritis and 10 healthy control samples)were obtained from the GEO database for merging to obtain their differentially expressed genes that were combined with oxidative stress genes to get the differentially expressed genes of oxidative stress.The differentially expressed genes of oxidative stress were analyzed for KEGG and GO enrichment,and the osteoarthritis pathways and biological processes were evaluated using GSEA enrichment analysis.The protein-protein interaction network was constructed using the STRING online website and Cytoscape software,and the Degree algorithm was run to get the key genes.The GSE1919 dataset was obtained from the GEO database as a validation dataset,and the key genes were analyzed by variance analysis and receiver operating characteristic curve analysis to get the core genes.In addition,immune infiltration was evaluated by CIBERSORT and the correlation between core genes and immune cells was explored.miRNA prediction of core genes was performed using TargetScan and target drugs were predicted using the DSigDB database. RESULTS AND CONCLUSION:Sixty-five differentially expressed genes and five core genes(IL1B,CXCL8,MYC,NFKBIA,JUN)associated with oxidative stress were identified.Enrichment analysis showed that differentially expressed genes associated with oxidative stress were concentrated in the pathways of oxidative stress,interleukin-17,osteoclast differentiation,fluid shear stress and atherosclerosis.The area under the receiver operating characteristic curve for the five core genes exceeded 0.85,indicating their excellent specificity and sensitivity in diagnosing bone and joint conditions,as well as their close association with immune cells.The predicted miRNA was has-miR-3937,and the therapeutic small-molecule drugs were metformin,ionomycin and celecoxib.To conclude,oxidative stress and immune infiltration exist in osteoarthritis,and immune infiltration is involved in activating oxidative stress.The core genes and predicted miRNAs can be used as novel markers for the diagnosis of osteoarthritis,and small molecule drugs are predicted to treat osteoarthritis.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.