The purpose of this study was to investigate how aerobic exercise affects oxidative stress (OS) in patients with chronic kidney disease (CKD). Retrieval dates range from the date the database was established to 19 July 2023, without languages being restricted. A meta-analysis and sensitivity analysis were conducted using RevMan 5.3 and Stata 16.0. The meta-analysis showed that, compared to usual activity or no exercise, aerobic exercise significantly reduced the oxidative markers malondialdehyde (MDA) (mean differences (MD) - 0.96 (95% CI -1.33, - 0.59); p < 0.00001), advanced oxidation protein product (AOPP) (MD - 3.49 (95% CI - 5.05, - 1.93); p < 0.00001), F2-isoprostanes (F2-iso) (MD - 11.02 (95% CI - 17.79, - 4.25); p = 0.001). Aerobic exercise also increased the antioxidant marker superoxide dismutase (SOD) in CKD patients (standardized mean differences (SMD) 1.30 (95% CI 0.56, 2.04); p = 0.0005). Subgroup analysis showed a significant increase in glutathione peroxidase (GPX) in patients aged ≥60 years (SMD 2.11 (95% CI 1.69, 2.54); p < 0.00001). The change in total antioxidant capacity (TAC) after aerobic exercise was insignificant in patients with CKD. The trial sequential analysis supported aerobic exercise's effectiveness in improving MDA, SOD, AOPP, and F2-iso in patients with CKD. The results of this review suggest that aerobic exercise improves OS indicators (MDA, SOD, AOPP, and F2-iso) in CKD patients compared to conventional treatment or no exercise and that the effects on GPX and TAC indicators need further confirmation. For better validation of benefits and exploration of the best aerobic exercise regimen to improve OS status with CKD, further studies with high methodological quality and large sample sizes are needed.

Acute uncomplicated lower urinary tract infection (UTI) is one of the most common bacterial infections. Patients usually present with dysuria, urinary urgency, urinary frequency, and suprapubic pain or tenderness. Approximately 150 million people are diagnosed with UTI each year worldwide. The high recurrence rate of lower UTI is a common problem of clinical treatment. The misuse of antibiotics has led to the emergence of a number of resistant bacterial strains. Thus, traditional Chinese medicine is considered as an alternative option for treating acute uncomplicated lower UTI. Thus, this study aims to evaluate the efficacy and safety of Sanjin tablets (SJT) for the treatment of acute uncomplicated lower UTI, explore whether SJT can reduce or substitute the use of antibiotics, and reduce the recurrence rate in the treatment of acute uncomplicated lower UTI. In this study, a randomized, double-blind, double-dummy, parallel control of positive drug, multicenter clinical study will be established. A total of 252 patients with acute uncomplicated lower UTI (syndrome of dampness-heat in the lower jiao) will be randomly allocated in the ratio of 1:1:1 to three groups: experimental group; control group 1; and control group 2. The experimental group receives Sanjin tablets plus levofloxacin tablets (LT) placebo; the control group 1 receives LT plus SJT placebo; and the control group 2 receives SJT plus LT on the first five days, SJT plus LT placebo on the last two days. Each group will be treated for seven days and followed-up 1–2 times. The primary outcome measures of effective rate and recurrence rate are symptoms. Secondary outcome measures of effective rate and recurrence rate are the urine leukocytes, bacteriology examination, and safety assessment. Outcomes will be assessed at baseline and after treatment. This study protocol will provide the research data of efficacy and safety of SJT for the treatment of acute uncomplicated lower UTI. The first aim is to determine whether Sanjin tablets can reduce the use of antibiotics; the second aim is to determine whether Sanjin tablets can substitute the use of antibiotics. The recurrence rate will be assessed after cured to determine whether SJT can reduce the recurrence rate. The results of this study will improve the rational use of drugs, especially the rational application of antibiotics. It will also enable safety evaluation from laboratory indices and adverse events, which will provide reliable evidence for clinical treatment. ClinicalTrials.gov, NCT03658291 . Registered on 4 September 2018.

Introduction: The integration of traditional Chinese medicine (TCM) and Western medicine has demonstrated effectiveness in the primary prevention of stroke. Therefore, our study aims to utilize TCM syndromes alongside conventional risk factors as predictive variables to construct a machine learning model for assessing the risk of new-onset stroke.Methods: We conducted a ten-year follow-up study encompassing 4,511 participants from multiple Chinese community hospitals. The dependent variable was the occurrence of the new-onset stroke, while independent variables included age, gender, systolic blood pressure (SBP), diabetes, blood lipids, carotid atherosclerosis, smoking status, and TCM syndromes. We developed the models using XGBoost in conjunction with SHapley Additive exPlanations (SHAP) for interpretability, and logistic regression with a nomogram for clinical application.Results: A total of 1,783 individuals were included (1,248 in the training set and 535 in the validation set), with 110 patients diagnosed with new-onset stroke. The logistic model demonstrated an AUC of 0.746 (95% CI: 0.719–0.774) in the training set and 0.658 (95% CI: 0.572–0.745) in the validation set. The XGBoost model achieved a training set AUC of 0.811 (95% CI: 0.788–0.834) and a validation set AUC of 0.628 (95% CI: 0.537–0.719). SHAP analysis showed that elevated SBP, Fire syndrome in TCM, and carotid atherosclerosis were the three most important features for predicting the new-onset stroke.Conclusion: Under identical traditional risk factors, Chinese residents with Fire syndrome may have a higher risk of new-onset stroke. In high-risk populations for stroke, it is recommended to prioritize the screening and management of hypertension, Fire syndrome, and carotid atherosclerosis. However, future high-performance TCM predictive models require more objective and larger datasets for optimization.