Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Objective:To investigate the effects of different BMI classifications on rehabilitation outcomes and complications after total knee arthroplasty(TKA).Methods:Clinical data of 88 patients who underwent TKA in our hospital from January 2022 to June 2024 were retrospectively analyzed.According to the World Health Organization(WHO)BMI standards,patients were divided into normal weight group(18.5 ≤BMI＜24.9 kg/m2),overweight group(25.0≤BMI＜29.9 kg/m2),mild obesity group(30.0≤BMI＜34.9 kg/m2),and severe obesity group(BMI≥35.0 kg/m2).Baseline data,operation time,intraoperative blood loss,hospital stay,postoperative knee function scores,pain scores,range of motion(ROM),and incidence of complications were compared among groups.Results:With the increase of BMI,operation time was prolonged,intraoperative blood loss increased,and hospital stay extended,with statistically significant differences(P＜0.05).At 3-month and 6-month follow-ups,Knee Society Score(KSS)and Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)showed that functional recovery decreased with increasing BMI(P＜0.05).Regarding postoperative complications,the incidence of poor incision healing,infection,and deep vein thrombosis(DVT)in the obese groups was significantly higher than in the normal weight and overweight groups(P＜0.05).Conclusion:BMI is an important factor affecting rehabilitation outcomes and complications after TKA.Patients with high BMI have slower functional recovery and higher complication rates.For patients with high BMI,preoperative weight loss and individualized rehabilitation programs should be considered to improve TKA prognosis.

Objective:To investigate the effects of different BMI classifications on rehabilitation outcomes and complications after total knee arthroplasty(TKA).Methods:Clinical data of 88 patients who underwent TKA in our hospital from January 2022 to June 2024 were retrospectively analyzed.According to the World Health Organization(WHO)BMI standards,patients were divided into normal weight group(18.5 ≤BMI＜24.9 kg/m2),overweight group(25.0≤BMI＜29.9 kg/m2),mild obesity group(30.0≤BMI＜34.9 kg/m2),and severe obesity group(BMI≥35.0 kg/m2).Baseline data,operation time,intraoperative blood loss,hospital stay,postoperative knee function scores,pain scores,range of motion(ROM),and incidence of complications were compared among groups.Results:With the increase of BMI,operation time was prolonged,intraoperative blood loss increased,and hospital stay extended,with statistically significant differences(P＜0.05).At 3-month and 6-month follow-ups,Knee Society Score(KSS)and Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)showed that functional recovery decreased with increasing BMI(P＜0.05).Regarding postoperative complications,the incidence of poor incision healing,infection,and deep vein thrombosis(DVT)in the obese groups was significantly higher than in the normal weight and overweight groups(P＜0.05).Conclusion:BMI is an important factor affecting rehabilitation outcomes and complications after TKA.Patients with high BMI have slower functional recovery and higher complication rates.For patients with high BMI,preoperative weight loss and individualized rehabilitation programs should be considered to improve TKA prognosis.

This paper uses literature and network data to systematically sort out the theoretical and practical foundations of global public health cooperation,combines expert interviews to conduct empirical analyses,and further explores China's strategies for participating in global public health cooperation through quantitative statistics and text mining of interview data,and proposes a plan for China's participation in global public health cooperation under the current international situation.Under the countercurrents to globalization,China should take its own public health capacity building as the foundation,put global security and health equity at the core,with a philosophy of open cooperation and sustainable development,actively promote bilateral and multilateral cooperation,focus on cultivating global health talents,and enhance the effectiveness of disease prevention and control by making use of existing platforms,international mechanisms and digital health technologies,so as to help build a Global Community of Health for All.

Objective:To analyze the clinical characteristics of the Chikungunya fever outbreak in Shunde District, Foshan City, Guangdong Province in July 2025 and the risk factors associated with delayed viral RNA clearance.Methods:A total of 562 patients with Chikungunya fever admitted to three designated hospitals in Shunde District from July 10 to 30, 2025 were enrolled. Demographic data, clinical manifestations, and laboratory findings were collected. Patients were categorized into four age groups including minors (<18 years), young adults (18 to 39 years), middle-aged adults (40 to 64 years) and elderly adults (≥65 years). The differences of clinical characteristics among these age groups were analyzed. Intergroup comparisons were performed using chi-square test, one-way analysis of variance, or Kruskal-Wallis H test. Pairwise comparisons between groups were conducted using the Bonferroni or Games-Howell or Dunn method. Binary logistic regression was employed to analyze risk factors associated with delayed viral RNA clearance (>7 days). Results:The mean age of the 562 enrolled Chikungunya fever patients was (44.8±21.3) years. Fever, arthralgia and rash were the three core symptoms, with incidence rates of 87.5% (492/562), 88.4%(497/562) and 69.6%(391/562), respectively. At discharge, only 54.1%(304/562) of patients achieved complete symptom resolution, while 26.5%(149/562) still had arthralgia and 36.1%(203/562) had residual rash. Significant differences were observed among age groups in the incidence of fever ( χ2=9.43, P=0.024), peak body temperature ( F=6.54, P<0.001), incidence of arthralgia ( χ2=26.89, P<0.001), duration of arthralgia ( F=12.68, P=0.001), incidence of rash ( χ2=68.99, P<0.001), rate of residual rash at discharge ( χ2=32.37, P<0.001), lymphocyte count ( F=12.94, P<0.001), platelet count ( F=14.95, P<0.001), and C-reactive protein levels (CRP) ( H=94.18, P<0.001). Further pairwise comparisons revealed that compared to the middle-aged and elderly groups, the minor group had a higher incidence of fever and a lower incidence of arthralgia, and the duration of arthralgia was shorter than the elderly group (all P<0.008 3). Compared with the other three groups, the elderly group had lower incidence and residual rate of rash, and lower platelet counts (all P<0.008 3), and higher levels of CRP (all P<0.05). The elderly group had lower lymphocyte counts compared to the minor and young adult groups (both P<0.05). Significant differences were found among age groups in the time to viral RNA clearance ( F=5.77, P=0.003) and length of hospital stay ( F=11.64, P<0.001), with the elderly group having significantly longer duration for both compared to the other three groups (all P<0.05). Multivariate analysis showed that advanced age (odds ratio ( OR)=1.049, 95% confidence interval ( CI) 1.015 to 1.083), longer duration of fever ( OR=1.529, 95% CI 1.086 to 2.155) and longer duration of arthralgia ( OR=1.927, 95% CI 1.318 to 2.817) were independent risk factors for delayed viral RNA clearance (all P<0.05). Conclusions:Patients with Chikungunya fever in Shunde District primarily present with fever, arthralgia and rash. The incidence and characteristics of these three core symptoms show age-related variations. Elderly patients and those with longer durations of fever or arthralgia are more likely to experience delayed viral clearance.

This paper uses literature and network data to systematically sort out the theoretical and practical foundations of global public health cooperation,combines expert interviews to conduct empirical analyses,and further explores China's strategies for participating in global public health cooperation through quantitative statistics and text mining of interview data,and proposes a plan for China's participation in global public health cooperation under the current international situation.Under the countercurrents to globalization,China should take its own public health capacity building as the foundation,put global security and health equity at the core,with a philosophy of open cooperation and sustainable development,actively promote bilateral and multilateral cooperation,focus on cultivating global health talents,and enhance the effectiveness of disease prevention and control by making use of existing platforms,international mechanisms and digital health technologies,so as to help build a Global Community of Health for All.

Stellate ganglion block (SGB) is a specific type of peripheral nerve block in which local anesthetics and/or steroids are injected around the stellate ganglia.In the past,SGB was mainly used to alleviate pain-related syndromes.With the development of ultrasound technology,SGB has been widely used in non-analgesic fields,demonstrating significant therapeutic effects on arrhythmias,hot flashes,psychiatric disorders,cerebrovascular diseases,insomnia,and post coronavirus disease-2019 conditions in recent years.This study reviews the progress in the application of SGB in the non-analgesic fields.
Stellate Ganglion ; Humans ; Autonomic Nerve Block/methods* ; Anesthetics, Local/administration & dosage* ; COVID-19

Objective : To investigate the effects of cinnamaldehyde(CA) on the growth, metastasis and apoptosis of human endometriosis(EMs) cells and to explore whether the mechanism is related to ribosomal protein S7(RPS7) expression. Methods : Endometriosis cells were divided into control group, CA group, sh-NC group, CA+sh-RPS7 group. Effects of CA on cell growth in human endometriotic cells were determined using Cell Counting Kit-8(CCK-8) and colony formation assay. Effects of CA on cell metastasis were performed by motility assay and Transwell assay. Effects of CA on cell apoptosis were evaluated by Hoechst 33258 staining and flow cytometry. Meanwhile, the levels of PCNA, E-cadherin, Vimentin, Bax and Bcl-2 were evaluated using Western blot in human endometriotic cells with treatment CA. The expression of RPS7 was detected by qRT-PCR and Western blot assay. The RPS7 overexpression of human endometriotic cells was established by cell transfection. CA-mediated effects on cell proliferation and apoptosis were determined by CCK-8 assay and flow cytometry in human endometriotic cells with RPS7 overexpression. Results : CA repressed cell growth as well as down-regulated PCNA. The half inhibitory concentration(IC50) value was 53.60 μmol/L after 24 h treatment, and colony formation rate was 25.32%. Additionally, CA inhibited metastasis which was associated with downregulated Vimentin and upregulated E-cadherin. The relative migration rates were 35% and 29% as well as invasion rate was 40%. Further, CA induced apoptosis by cell cycle G2/M phase arrest and cell apoptosis rate was 25.1%, which related to the up-regulation of of Bax and the down-regulation of Bcl-2. CA inhibited the expression of RPS7 and overexpression of RPS7 promoted cell proliferation and suppressed apoptosis in CA-mediated cells. Conclusion CA inhibits cell growth, metastasis, and induces cell apoptosis by downregulating the expression of RPS7.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.