Objective. To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. Methods. Related compounds were obtained from TCMSP and BATMAN‐TCM database. Oral bioavailability and drug‐likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM‐RA, compounds‐targets, and pathways‐targets‐compounds were visualized through Cytoscape 3.7.1. The protein‐protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE). Results. A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds‐targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta‐sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K‐Akt, TNF, and IL‐17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets. Conclusion. The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

Background: Extranodal NK/T-cell lymphoma, nasal type (ENKTCL-NT), is a rare, aggressive subtype of non-Hodgkin lymphoma, and it usually presents as a destructive sinus mass that may attend epistaxis. However, ENKTCL-NT with the manifestation of peripheral neuropathy is pretty unusual. Case presentation: A 17-year-old Chinese patient presented with peripheral neuropathy and positive auto-antibodies. However, she failed methylprednisolone pulse therapy and developed hemophagocytic syndrome. The diagnosis of CD30-positive primary cutaneous ENKTCL-NT was confirmed by pathological biopsy. Her disease was brought under control after five cycles of chemotherapy. Conclusions: The report findings are helpful in the differential diagnosis of peripheral neuropathy and autoimmune disease. We should be alert for the development of ENKTCL-NT when the rash and peripheral neuropathy are the first symptoms and are accompanied by Epstein-Barr virus (EBV) infection.

Background and objectives: Osteoporosis (OP) presents a significant health issue in rheumatoid arthritis (RA) patients, yet existing machine learning (ML) studies on OP prediction in this population are limited by low accuracy, a narrow range of considered risk factors, and a lack of interpretability. This study aims to develop an interpretable machine learning model using the CNN-SVM algorithm, integrated with interpretability techniques, for individualized osteoporosis risk assessment in RA patients. The model specifically focuses on the osteopenia stage, which has been overlooked in previous research, to better capture the different risk factors involved in the progression of osteoporosis in RA patients. Methods: We recruited 314 RA patients from the Department of Rheumatology and Immunology. Participants were categorized into osteoporosis, osteopenia, and normal groups based on lumbar spine or hip bone mineral density (BMD) T-scores. We constructed ML model to assess osteoporosis using a novel classification algorithm, CNN-SVM, and employed SHapley Additive exPlanations (SHAP) and Sankey diagram to investigate significant risk factors, rank risk factor contributions, and provide individualized feature contribution explanations. Results: A total of 16 candidate variables were included, and three classification models were constructed to predict osteoporosis versus osteopenia, osteoporosis versus normal, and osteopenia versus normal. The AUC values for the models were 0.83, 0.93, and 0.74, respectively. Feature importance analysis using SHAP identified several key predictors. Factors such as Vitamin D supplements, Synovitis in Both Knees, and gender were crucial for distinguishing normal from osteopenia. For differentiating osteoporosis, Alendronate Sodium, weight, and age consistently ranked as highly influential features across different comparisons. Feature importance analysis was performed, ranking risk factors and providing individualized explanations of feature contributions. Conclusions: The developed interpretable ML model shows promise for screening osteoporosis risk in patients with RA. Its ability to identify individual risk factors highlights its potential to facilitate personalized prevention and management strategies, pending further validation.

This study investigates the effectiveness of Azvudine and nirmatrelvir-ritonavir (Paxlovid) in treating COVID-19 pneumonia through an analysis of real-world clinical data. We retrospectively collected data from COVID-19 patients hospitalized at the Second Xiangya Hospital of Central South University between December 21, 2022, and January 18, 2023. Using kernel density estimation, box-and-whisker plots, and Schoenfeld residual plots, we evaluated the transition of patients to negative status and assessed factors such as age, disease severity, and treatment effects. The findings revealed that both Azvudine and Paxlovid significantly reduced recovery times, with Azvudine showing notable benefits for patients aged 50–80. Our analysis indicated that these drugs improved lung CT values and reduced disease severity in moderate cases. The Cox model demonstrated robustness in predicting outcomes, and a nomogram was developed for individualized recovery probability assessment. These results provide important insights into optimizing COVID-19 treatment and the potential of predictive models in clinical decision-making.

To investigate the association between weekend catch-up sleep (WCS) duration and depressive symptoms among young adults in China and the United States, and to explore potential associated factors. We conducted a binational cross-sectional study comprising 1,440 Chinese young adults (collected in 2024) and 892 US young adults (from NHANES 2021–2023). Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9 ≥ 10). WCS duration was categorized. Logistic regression, restricted cubic splines (RCS), and threshold effect models were employed to examine the relationship. Subgroup analyses were conducted by sex, education, and lifestyle factors. A U-shaped association was observed in both populations. Compared to no catch-up sleep (0 h), a WCS duration of 0–1 h was associated with significantly lower odds of depressive symptoms (China: Odds Ratios (OR) = 0.495, 95% Confidence Intervals (CI) 0.339–0.724; US: OR = 0.412, 95% CI 0.209–0.812). Restricted Cubic Spline (RCS) and threshold analyses confirmed this non-linear pattern, with inflection points at approximately 0.58 h (China) and 1.01 h (US). Subgroup analyses indicated the associations were modified by sex and alcohol drinking status. This study reveals a consistent U-shaped association between WCS and depressive symptoms among young adults in two distinct national populations. Moderate weekend catch-up sleep (around 0–1 h) may be associated with lower odds of depressive symptoms. These findings highlight the importance of balanced sleep patterns for mental health in young adults.