Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain regions due to immune escape and waning vaccine efficacy. Identifying high-risk patients to mitigate transmission and avert complications promptly is imperative. Nevertheless, the current diagnostic methods, including PCR and bacterial culture, are time-consuming and expensive. Some studies have attempted to develop risk prediction models based on multivariate data, but their performance can be improved. Therefore, this study aims to further optimize and expand the risk assessment tool to more efficiently identify high-risk individuals and compensate for the shortcomings of existing diagnostic methods. The aim of this study was to develop a pertussis risk prediction model that is both efficient and has good generalization ability, applicable to different datasets. The model was constructed using machine learning techniques based on multicenter data and screened for key features. The performance and generalization ability of the model were evaluated by deploying it on an online platform. At the same time, this study aims to provide a rapid and accurate auxiliary diagnostic tool for clinical practice to help identify high-risk patients in a timely manner, optimize early intervention strategies, reduce the risk of complications and reduce transmission, thereby improving the efficiency of public health management. First, data from 1085 suspected pertussis patients from 7 centers were collected, and ten key features were analyzed using the lasso regression and Boruta algorithm: PDW-MPV-RATIO, SII, white blood cells, platelet distribution width, mean platelet volume, lymphocytes, cough duration, vaccination, fever, and lytic lymphocytes.Eight models were then trained and validated to assess their performance and to confirm their generalization ability with external datasets based on these features. Finally, an online platform was constructed for clinicians to use the models in real time. The random forest model demonstrated excellent discrimination ability in the validation set, with an AUC of 0.98, and an AUC of 0.97 in the external validation set. Calibration curve and decision curve analysis showed that the model had high accuracy in predicting low-to-medium risk patients, which could help clinicians avoid unnecessary interventions, especially in resource-limited settings. The application of this model can help optimize the early identification and management of high-risk patients and improve clinical decision-making. The pertussis prediction model devised in this study was validated using multicenter data, exhibited high prediction performance, and was successfully implemented online. Future research should broaden the data sources and incorporate dynamic data to enhance the model's accuracy and applicability.

Background: Depression is a significant global public health issue, affecting millions worldwide. Outdoor activities have shown potential mental health benefits, but the specific mechanisms and influencing factors remain unclear.Objectives: This study aimed to investigate the association between time spent outdoors and depression risk among U.S. adults, with a focus on variations across age and ethnic groups.Methods: Using data from the NHANES 2011–2018 survey, we analyzed 9,036 adults aged 20 years and older. Participants self-reported their outdoor activity time and depressive symptoms. Statistical analysis, accounting for various demographic and lifestyle factors, was employed to assess the relationship between outdoor activity and depression risk.Results: Spending more time outdoors was associated with a 51% lower risk of depression (odds ratio: 0.51, 95% CI: 0.40–0.64). Subgroup analysis revealed that this association was particularly pronounced among adults aged 40 and older, as well as non-Hispanic whites and non-Hispanic blacks.Conclusion: Encouraging outdoor activities may represent an effective public health strategy to reduce depression risk, particularly among middle-aged and older adults and specific ethnic populations. Public health policies should prioritize initiatives that encourage outdoor engagement, and future research is needed to explore the underlying mechanisms and population-specific responses to outdoor activity.

Vascular remodeling is a hallmark of various cardiovascular diseases and is increasingly recognized as an immune-mediated process. Recent evidence has iden

Background: The prevalence of chronic postsurgical pain (CPSP) ranges from 10% to 50%, varying by surgical procedure. With over 300 million surgeries performed annually worldwide, the prevention and management of CPSP represent a significant clinical challenge. Objective: This systematic review and meta-analysis aimed to evaluate the efficacy of the transversus abdominis plane (TAP) block in reducing the prevalence of CPSP among adults undergoing abdominal surgery. Methods: We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, ChinaInfo, and CNKI from their inception to July 2025. We included randomized controlled trials (RCTs) comparing the TAP block with no block or placebo in adults (≥18 years) undergoing abdominal surgery. The Cochrane risk of bias tool for randomized trials (RoB 2) was used to assess the risk of bias, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework was used to evaluate the certainty of the evidence. Results: Our systematic search and rigorous screening process identified eight RCTs involving 630 patients for this meta-analysis. During full-text review, 35 of 43 articles were excluded for specific ineligibility reasons. Low to moderate quality evidence showed that the TAP block significantly reduced the prevalence of CPSP at 3, 6, and 12 months postoperatively compared to no TAP block [risk ratio (RR) 0.62, 95% confidence interval (CI) 0.39–0.97, P = 0.04, I ² = 66% for 3 months, RR 0.44, 95% CI 0.25–0.77, P < 0.01, I ² = 63% for 6 months, RR 0.31, 95% CI 0.13–0.74, P < 0.01, I ² = 0% for 12 months]. Very low to low quality evidence demonstrated that the TAP block also decreased the pain scores of CPSP at 12 months postoperatively compared to no TAP block (mean difference −0.18, 95% CI −0.31 to 0.05, P < 0.01, I ² = 0%), but not at 3 and 6 months postoperatively. The reduction in pain scores observed with the TAP block failed to meet the threshold for the minimum clinically important difference at all assessed postoperative time points. Moderate quality evidence demonstrated that the TAP block significantly reduced the prevalence of postoperative nausea and vomiting at 24 h postoperatively compared to no TAP block (RR 0.56, 95% CI 0.34–0.93, P = 0.03, I ² = 0%). Subgroup analyses provided hypothesis generating evidence that the preoperative TAP block significantly reduced the prevalence of CPSP at 3 (RR 0.36, 95% CI 0.24–0.56, P < 0.01, I ² = 0%) and 6 (RR 0.20, 95% CI 0.11–0.38, P < 0.01, I ² = 0%) months postoperatively compared with no block, an effect not seen with postoperative administration. Other subgroup analyses based on local anesthetic type, primary anesthesia method, patient-controlled analgesia use, and gender also revealed potential differences. Conclusions: Based on low to moderate certainty evidence, which is limited by significant heterogeneity, the TAP block for abdominal surgery may reduce the prevalence of CPSP. This potential benefit appears more pronounced with preoperative administration. However, these findings warrant cautious interpretation. Future large-scale, multicenter RCTs designating CPSP as a primary outcome are necessary to provide more definitive evidence.