Vagus nerve stimulation (VNS) is a widely used neurostimulation technique for managing neurological diseases such as drug-resistant epilepsy and depression. Moreover, VNS has shown considerable potential for application in immune diseases. Herein, clinical studies on VNS application in immune disease treatments, such as for rheumatoid arthritis and Crohn’s disease, have been systematically reviewed, with a focus on stimulation methods, clinical efficacy, mechanisms of action, and safety. This review highlights the therapeutic effects of VNS in improving the clinical symptoms of patients with immune diseases through neuroimmune interactions and underscores the need for further large-scale, randomized controlled clinical trials to validate its long-term efficacy and safety.

Was not provided for this article.

Hypertension and depression have a well-recognized bidirectional association, with comorbidity reducing antihypertensive medication adherence and elevating adverse health outcomes; depression affects 30% to 40% of hypertensive patients, far higher than the general population. However, validated, hypertension-specific depression screening tools based on machine learning (ML) are currently lacking. This study aimed to develop, validate, and visualize an ML-based depression screening tool for adult hypertensive patients. A cross-sectional study was conducted using 2005–2020 National Health and Nutrition Examination Survey data of 6687 hypertensive patients aged ≥20 years (depression defined by Patient Health Questionnaire-9 score ≥ 10). Least absolute shrinkage and selection operator regression was used for feature selection, data were split into a 7:3 training/test set, and 7 ML models were constructed. Model performance was assessed by area under the receiver operating characteristic curve, calibration, and clinical utility (decision curve analysis); SHapley Additive exPlanations values ensured interpretability, and a web-based tool was built from the optimal model. The random forest model was identified as the optimal screening tool (area under the receiver operating characteristic curve = 0.761), with good calibration and favorable net clinical benefit (threshold probability: 0.07–0.39). Twelve key predictors were established, with sleep disturbances as the top factor, followed by poverty-to-income ratio and age. An interactive online screening tool (https://chunyuzhang28.shinyapps.io/Hypertension/) was developed, enabling rapid depression risk prediction for hypertensive patients via input of the 12 predictors. This random forest-based ML screening tool is a valid and practical tool for clinical depression risk assessment in adult hypertensive patients. Clinicians can use it to stratify depression risk in hypertensive patients, identify high-risk subgroups (e.g., those with sleep disturbances or low poverty-to-income ratio), and implement targeted early intervention and integrated management strategies for hypertension and depression, thus optimizing clinical decision-making for comorbidity care.