Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To investigate the epidemiological characteristics of cross-county imported dengue fever cases within Yunnan province in 2023, so as to provide insights into formulation of preventive and control measures for intra-provincial spread of dengue fever. Methods All data pertaining cross-county imported dengue fever cases within Yunnan Province in 2023 were collected, and the temporal, regional and population distributions of the cases were descriptively analyzed. Results A total of 1 664 intra-provincial cross-county imported dengue fever cases were reported in 95 counties (cities, districts) cross 16 profectures (cities) in Yunnan Province in 2023, accounting for 12.34% of total cases in the province. Cross-county imported dengue fever cases were predominantly reported during the period between August and October (1 516 cases, 91.11% of total cases), and peaked in September (659 cases), with a single-day peak on October 8 (36 cases). During the period from September 4 to 10, five counties (cities) with local dengue fever epidemics, including Jinghong City of Xishuangbanna Dai Autonomous Prefecture, Gengma Dai and Wa Autonomous County of Lincang City, Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, Mengla Coun ty of Xishuangbanna Dai Autonomous Prefecture, and Zhenkang County of Lincang City, exported 165 cross-county imported dengue fever cases to the rest of the province. Among the 1 644 intra-provincial cross-county imported dengue fever cases, the male to female ratio was 1.40∶1.00, and 1 329 cases were at ages of 15 to 55 years (79.87%), with farmers as the predominant occupation (886 cases, 53.25%). The top 5 counties (cities/districts) reporting the highest number of intra-provincial cross-county imported dengue fever cases included Simao District (266 cases) and Lancang Lahu Autonomous County (118 cases) of Pu’er City, Mengla County (91 cases) and Menghai County (91 cases) of Xishuangbanna Dai Autonomous Prefecture, and Mangshi City (73 cases) of Dehong Dai and Jingpo Autonomous Prefecture, which accounting for 38.40% of total imported cases. These intra-provincial cross-county imported dengue fever cases originated from 7 counties (cities/districts) in 4 prefectures (cities), including 1 261 cases (76.70%) from Jinghong City of Xishuangbanna Dai Autonomous Prefecture, 224 cases (13.63%) from Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, 103 cases (6.27%) from Gengma Dai and Wa Autonomous County of Lincang City, 31 cases (1.89%) from Mengla County of Xishuangbanna Dai Autonomous Prefecture, 30 cases (1.82%) from Zhenkang County of Lincang City, 10 cases (0.61%) from Cangyuan Wa Autonomous County of Lincang City, and 5 cases (0.30%) from Mohan-Boten Economic Cooperation Zone of Kunming City. In addition, local dengue fever epidemics following intra-provincial cross-county importation of dengue fevers cases in Simao District, Jinggu Dai and Yi Autonomous County, Mangshi City, Longchuan County, and Cangyuan Wa Autonomous County. Conclusions Farmers and students are high-risk populations for intra-provincial cross-county imported dengue fever cases in Yunnan Province, and health education pertaining personal protection against dengue fever should be strengthened among these high-risk populations by governments at all levels. There is a high risk of local out-break of dengue fever following continuous introduction of intra-provincial cross-county imported cases. Standardized management of intra-provincial cross-county imported dengue fever cases should be reinforced to reduce the risk of local epidemics.

The purpose of this study was to enrich the genomic information and provide a basis for further development and utilization of Polygonatum kingianum. The fresh rhizome of P. kingianum was used as the experimental material, and the full-length transcriptome was sequenced by PacBio Sequel platform. The final measured polymerase reads were 1 120 485, with a total of 77.73 GB of data. In NR database, 41 864 homologous sequence alignments were aligned to 5 species; 40 506 were annotated in the KOG database and classified into 26 categories based on their functionality; 69 060 GO annotated 32 functional groups divided into 3 categories: cellular components, molecular functions, and biological processes; 45 779 annotations were added to 145 metabolic pathways in the KEGG database. Among them, there are 127 identified transcripts related to polysaccharide synthesis in the glycolysis/gluconeogenesis metabolic pathway, 144 in the starch and sucrose metabolic pathway, 85 in the amino acid sugar and nucleotide sugar metabolic pathway, and 69 in the fructose and mannose metabolic pathway. In addition, 1 781 transcription factors were detected, distributed in 37 transcription factor families; 180 293 SSR loci were detected, among which single base repeats accounted for the most, accounting for 30.48% of all base repeats, and at least five base repeats, accounting for only 0.14% of single base repeats. The results of this study provide important data supporting for enriching the genomic information of P. kingianum and exploring its effective biosynthetic pathway.

AIM To investigate the influencing factors in scale-up of extraction process for Yunpi Xiaoshi Prescription.METHODS HPLC was adopted in the content determination of catechin,ferulic acid,taxifolin,isovitexin,narirutin,atractylenolideⅡ,naringin,morin,hesperidin,luteolin,hederagenin,atractylenolideⅠ,naringenin and hesperetin,the fingerprints were established,after which the effects of container volume,optimal fire and feeding quantity on the contents of various constituents were evaluated.RESULTS Fifteen batches of samples demonstrated the similarities of more than 0.995.Fourteen constituents showed good linear relationships within their own ranges(r>0.999 0),whose average recoveries were 96.4%-103.3%with the RSDs of 0.5%-2.7%.The influencing degree of optimal fire was greater than that of container volume and feeding quantity.CONCLUSION The combination of multi-component content determination and fingerprints can provide data basis and theoretical reference for the technology of consistency evaluation in scale-up of extraction process for Yunpi Xiaoshi Prescription.

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

Objective As primary Sj?gren's syndrome(pSS)primarily affects the salivary glands,saliva can serve as an indicator of the glands'pathophysiology and the disease's status.This study aims to illustrate the salivary proteomic profiles of pSS patients and identify potential candidate biomarkers for diagnosis. Methods The discovery set contained 49 samples(24 from pSS and 25 from age-and gender-matched healthy controls[HCs])and the validation set included 25 samples(12 from pSS and 13 from HCs).Totally 36 pSS patients and 38 HCs were centrally randomized into the discovery set or to the validation set at a 2:1 ratio.Unstimulated whole saliva samples from pSS patients and HCs were analyzed using a data-independent acquisition(DIA)strategy on a 2D LC-HRMS/MS platform to reveal differential proteins.The crucial proteins were verified using DIA analysis and annotated using gene ontology(GO)and International Pharmaceutical Abstracts(IPA)analysis.A prediction model for SS was established using random forests. Results A total of 1,963 proteins were discovered,and 136 proteins exhibited differential representation in pSS patients.The bioinformatic research indicated that these proteins were primarily linked to immunological functions,metabolism,and inflammation.A panel of 19 protein biomarkers was identified by ranking order based on P-value and random forest algorichm,and was validated as the predictive biomarkers exhibiting good performance with area under the curve(AUC)of 0.817 for discovery set and 0.882 for validation set. Conclusions The candidate protein panel discovered may aid in pSS diagnosis.Salivary proteomic analysis is a promising non-invasive method for prognostic evaluation and early and precise treatments for pSS patients.DIA offers the best time efficiency and data dependability and may be a suitable option for future research on the salivary proteome.