Efficacy and safety of acupuncture for vocal nodules: A systematic review and meta-analysis with trial sequential analysis.

In this study, we aim to evaluate the efficacy and safety of acupuncture for vocal nodules, concerning qualitive overall efficacy and quantitative improvement on quality of voice. Four English and four Chinese databases were searched up to December 10th, 2022. Risk of bias among the included trials were evaluated by the Cochrane ROB tool. Systematic reviews and meta-analyses were conducted based on the Cochrane systematic review method by using RevMan 5.4 Software, and trial sequential analyses were performed by TSA 0.9. Meta-influence analyses, subgroup-analyses, meta-regression, and evaluation of publication bias were performed for exploration of heterogeneity by Stata V.14. Quality of the results was accessed through the GRADE-pro GDT. Cluster analyses and correlation coefficient were performed by R 4.1.3. Finally, 15 trials involving 1,888 participants were included. Results showed that compared with western medicine alone or Chinese herbal medicine alone, acupuncture alone yielded significantly (p<0.05) higher clinical effective rate and more improvement on scores of voice analyses. However, reduction on scores of grade, roughness, and breathiness and voice handicap index during follow-ups, and results of clinical effective rate suggested that acupuncture was inferior to voice training. In addition, meta-regression and sub-group analyses firstly revealed advanced efficacies of acupuncture when performed with local and remote acupoints, compared with local acupoints only. Acupuncture specified adverse event was denied in six trials while it was not mentioned in other nine trials. Results of cluster analyses and correlation coefficient showed that Kai yin yi hao and He gu (LI-4) were the most frequently applied matching-acupoints in trials. In conclusion, compared with western medicine (level of evidence: low ⨁⨁◯◯, GRADE C) and Chinese herbal medicine (level of evidence: moderate ⨁⨁⨁◯, GRADE B), acupuncture is safe and of better efficacy for patients with vocal nodules, while there is also need for RCTs with improvements on designing and interventions in experimental and controls.

[Spatial Prediction Method of Farmland Soil Organic Matter in Weibei Dryland of Shaanxi Province].

Accurately predicting the spatial distribution of soil organic matter (SOM) content is of great significance for improving soil quality and improving the level of regional soil management. In order to explore the optimal model for predicting the SOM content of farmland in the Weibei Dryland of Shaanxi Province, the influence factors closely related to SOM content were selected as the modeling covariables, and a geographic detector, the ordinary kriging method (OK), geographic weighted regression model (GWR), partial least squares regression model (PLS), geographically weighted regression extended model (GWRPLS), and random forest model (RF) were used to predict the spatial distribution of SOM content in training samples. Additionally, the validation set samples were used to compare and analyze the prediction accuracy of the five methods. The results showed:① the main factors affecting the spatial variability of soil SOM were total nitrogen, fertilizer application, available potassium, available phosphorus, and altitude, and the interaction between any two factors was more explanatory for SOM than any single factor. ②ω(SOM) in farmland was between 2.25 and 30.23 g·kg-1, with an average value of 15.14 g·kg-1 and a coefficient of variation of 30.00. Although there were local differences in the prediction results of SOM by the five methods, the overall spatial distribution trend was basically the same. In the study area, the content of organic matter was low in the north and northeast and high in the west and southeast. ③ From the perspective of the prediction accuracy of the five methods, the root mean square error (RMSE) and mean absolute error (MAE) of RF were the smallest, and the prediction deviation (RPD) of GWRPLS was the largest. Compared with the OK method, the correlation coefficients (r) of GWR, PLS, RF, and GWRPLS increased to 0.907, 0.836, 0.968, and 0.972, respectively. Comprehensive analysis results showed that the random forest model had the highest prediction accuracy.