Chinese herbal medicine for post-viral fatigue: A systematic review of randomized controlled trials.

BACKGROUND: Fatigue is a common symptom after viral infection. Chinese herbal medicine (CHM) is thought to be a potential effective intervention in relieving fatigue. PURPOSE: To assess the effectiveness and safety of CHM for the treatment of post-viral fatigue. STUDY DESIGN: Systematic review and meta-analysis of randomized controlled trials (RCTs). METHODS: The protocol of this systematic review was registered on PROSPERO (CRD42022380356). Trials reported changes of fatigue symptom, which compared CHM to no treatment, placebo or drugs, were included. Six electronic databases and three clinical trial registration platforms were searched from inception to November 2023. Literature screening, data extraction, and risk bias assessment were independently carried out by two reviewers. Quality of the included trials was evaluated using Cochrane risk of bias tool, and the certainty of the evidence was evaluated using GRADE. The meta-analysis was performed using Review Manager 5.4, mean difference (MD) and its 95% confidence interval (CI) was used for estimate effect of continuous data. Heterogeneity among trials was assessed through I2 value. RESULTS: Overall, nineteen studies with 1921 patients were included. Results of individual trial or meta-analysis showed that CHM was better than no treatment (MD = -0.80 scores, 95%CI -1.43 to -0.17 scores, P = 0.01, 60 participants, 1 trial), placebo (MD = -1.90 scores, 95%CI -2.38 to -1.42 scores, P<0.00001, 184 participants, 1 trial), placebo on basis of rehabilitation therapy (MD = -14.90 scores, 95%CI -24.53 to -5.27 scores, P = 0.02, 118 participants, 1 trial) or drugs (MD = -0.38 scores, 95%CI -0.48 to -0.27 scores, I2 = 0%, P<0.00001, 498 participants, 4 trials) on relieving fatigue symptoms assessing by Traditional Chinese Medicine fatigue scores. Trials compared CHM plus drugs to drugs alone also showed better effect of combination therapy (average MD = -0.56 scores). In addition, CHM may improve the percentage of CD4 T lymphocytes and reduce the level of serum IL-6 (MD = -14.64 scores, 95%CI 18.36 to -10.91 scores, I2 = 0%, P<0.00001, 146 participants, 2 trials). CONCLUSION: Current systematic review found that the participation of CHM can improve the symptoms of post-viral fatigue and some immune indicators. However, the safety of CHM remains unknown and large sample, high quality multicenter RCTs are still needed in the future.

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population.

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2-18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.