This article systematically analyzes the historical evolution of the name， medicinal parts， origin， harvesting， processing and other aspects of Houttuyniae Herba（HH） by referring to the medical books， prescription books and other documents of the past dynasties， combined with the research materials related to modern and contemporary times， in order to provide a basis for the development of famous classical formulas containing this herb. In ancient literature， HH was often referred to as "Ji" and "Jicai"， the name of "Ji" was first recorded in Mingyi Bielu during the Han and Wei dynasties， and the name of Yuxingcao was first seen in Lyuchanyan Bencao during the southern Song dynasty and has continued to this day. The origin of HH used throughout history is consistent， all of which are the whole herb or aboveground parts of Houttuynia cordata in Saururaceae family. HH recorded throughout history has a wide range of production areas， mostly self-produced self-marketing. In ancient times， fresh HH was often used as medicine by pounding its juice without involving any processing steps. Both fresh and dried products can be used as medicine， the fresh products uses the whole plant， while the dried products uses the aboveground parts， which are cleaned， selected and processed before use. Fresh products are harvested regardless of season， while dried products are harvested in both summer and autumn， with summer as the best. In ancient times， there were no specific requirements for the quality of HH， while in modern times， "intact stems and leaves with a strong fishy smell" are preferred. In addition， the medicinal properties of HH have undergone significant changes from ancient to modern times. In the early period， it was believed that its medicinal property was slightly warm， until the 1977 edition of Chinese Pharmacopoeia officially changed it to slightly cold. Both ancient and modern literature states that HH can be used for the treatment of carbuncle and malignant sores， Lyuchanyan Bencao for the first time introduced HH fresh juice can relieve summer heat， since Diannan Bencao recorded that it can be used for lung carbuncle， and gradually developed into the first choice for the treatment of lung carbuncle. Based on the research results， it is suggested that fresh herb or dried aboveground parts of H. cordata are used as medicine when developing famous classical formulas.

OBJECTIVE To investigate the potential mechanism of Sanqi danshen tablets in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Core targets of Sanqi danshen tablets in the treatment of NAFLD were explored by network pharmacological methods. Gene ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were also performed. Based on the results obtained from network pharmacological studies， using SD rats as subjects， the NAFLD model was induced by feeding them high-fat diet. The effects of Sanqi danshen tablets on pathological changes such as lipid droplet vacuoles and lipid accumulation in the liver tissue of NAFLD rats， as well as its impact on relative indicators of lipid metabolism， inflammatory responses and oxidative stress， were investigated. RESULTS A total of 20 core targets for the treatment of NAFLD with Sanqi danshen tablets were screened， primarily involved in GO functions such as biological regulation， cellular membrane and binding， and enriched in signaling pathways related to inflammatory responses， oxidative stress and lipid metabolism. Compared with the model group， lipid droplet vacuoles were reduced significantly in low-dose， medium-dose， high-dose groups of Sanqi danshen tablets and positive control （simvastatin） group， the number of lipid droplets decreased significantly and the color became lighter. The contents of total cholesterol， triglyceride （except for medium- dose group of Sanqi danshen tablets）， aspartate transaminase， alanine transaminase， tumor necrosis factor-α （except for low-dose group of Sanqi danshen tablets）， interleukin-17 （except for Sanqi danshen tablets groups） and malondialdehyde （except for low- dose group of Sanqi danshen tablets） in liver tissue were significantly decreased， while the content of superoxide dismutase was significantly increased （P＜0.01 or P＜0.05）. CONCLUSIONS Sanqi danshen tablets exert anti-inflammatory， antioxidant and lipid metabolism regulating effects by influencing the levels of inflammation， oxidative stress and lipids metabolism-related indicators， thereby improving NAFLD in rats.

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 describe the association of different sleep characteristics and cardiometabolic risk among college students, so as to provide reference for health promotion of college students. Methods: By random cluster sampling method, a questionnaire survey and physical examination including blood pressure, waist circumference and blood lipid indicators, which were conducted in April and May of 2019 among a total of 1 179 college students from the first grade in two universities in Hefei City of Anhui Province and Shangrao City of Jiangxi Province. A total of 729 college students with valid questionnaires were included into analysis. The Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) were used to investigate sleep behavior, and the Morning And Evening Questionnaire-5 (MEQ-5) was used to investigate sleep characteristics. The cardiometabolic risk score was derived using the sum of the standardized sex specific Z scores of waist circumference, mean arterial pressure, HDL cholesterol (multiplied by -1), triglycerides, and insulin resistance index. The rank sum tests were used to compare differences in cardiometabolic risk scores across demographic characteristics. Generalized linear models were used to compare the association of different sleep characteristics with cardiometabolic risk scores among college students. Results: The average cardiovascular metabolic risk score of college students was -0.32(-2.03, 1.58). There were statistically significant differences in cardiovascular metabolic risk scores among college students in variables such as smoking, health status, and physical activity levels ( t/F=-3.41, 12.88, 51.07, P <0.01). The results of the generalized linear model showed that nighttime preference ( B=1.89, 95%CI =1.02-3.49), insomnia symptoms ( B=3.25, 95%CI =1.79-5.90), and short or long sleep duration ( B=1.92, 95%CI =1.21-3.05) were positively correlated with the cardiovascular metabolic risk score of college students ( P <0.05). Conclusions Poor sleep patterns among college students are positively correlated with the risk of cardiovascular metabolism. The sleep behavior of college students should be actively changed to reduce the risk of cardiovascular disease.

Objective To observe the clinical efficacy of acupuncture at Neiyingxiang(EX-HN09)points combined with western medicine in the treatment of allergic rhinitis of deficiency-cold of lung qi type.Methods Sixty patients with deficiency-cold of lung qi type of allergic rhinitis were randomly divided into observation group and control group,with 30 patients in each group.The control group was treated with Desloratadine Tablets combined with Mometasone Furoate Aqueous Nasal Spray,and the observation group was treated with acupuncture at Neiyingxiang points combined with the self-made rhinitis recipe on the basis of the control group,and the clinical efficacy of the two groups was evaluated after 14 days.The changes of nasal symptom scores,Visual Analogue Scale(VAS)and rhinoconjunctivitis quality of life scores of the patients of the two groups were observed before and after the treatment.After 14 days of treatment,the clinical efficacy of the two groups was evaluated.The changes in nasal symptom scores,as well as VAS and rhinoconjunctivitis quality of life questionnaire(RQLQ)scores were observed before and after treatment.The changes in traditional Chinese medicine(TCM)sydnrome scores and serum immunoglobulin E(IgE)were compared before and after treatment in the two groups,and the safety of the two groups was evaluated.Results(1)The total effective rate of the observation group was 93.33%(28/30),and the control group was 73.33%(22/30).The efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(2)After treatment,the symptoms of nasal congestion,sneezing,runny nose and nasal itching were significantly improved in the two groups(P＜0.01),and the observation group was significantly superior to the control group in improving nasal symptoms,and the differences were statistically significant(P＜0.05).(3)After treatment,the VAS scores of patients in the two groups were significantly improved(P＜0.01),and the observation group was superior to the control group in improving VAS scores,with statistically significant differences(P＜0.05).(4)After treatment,the PQLQ scores of patients in the two groups improved significantly(P＜0.01),and the observation group was significantly superior to the control group in improving the PQLQ scores,and the difference was statistically significant(P＜0.05).(5)After treatment,the TCM syndrome scores of the patients in the two groups were significantly improved(P＜0.01),and the observation group was significantly superior to the control group in improving TCM syndrome scores,with statistically significant differences(P＜0.05).(6)After treatment,the serum IgE levels of patients in the two groups were significantly improved(P＜0.01),and the observation group was significantly superior to the control group in improving serum IgE levels(P＜0.05),with a statistically significant difference.(7)There was no significant difference in the incidence of adverse reactions between the observation group and the control group(P＞0.05).Conclusion Acupuncture at Neiyingxiang points plus self-made rhinitis recipe combined with western medicine in the treatment of deficiency-cold of lung qi type of allergic rhinitis can significantly improve the clinical symptoms of the patients,thus improving the quality of life of the patients,and the therapeutic efficacy is remarkable.

Objective To screen the potential pharmacological targets of Ningzhi capsule,a lipid-lowering tradi-tional Chinese medicine,and explore its mechanism of effect.Methods The components and predicted targets of Ningzhi capsule′s constituent drugs were obtained from BATMAN-TCM database.Hyperlipidemia-related targets were obtained from DisGeNET and GeneCards databases.The Venny2.1.0 tool was used to map drug targets and disease targets to obtain common targets as potential pharmacological targets.Protein-protein interaction analysis(STRING),gene ontology and pathway enrichment analysis(DAVID)were performed for the common targets.Finally,Swiss dock was used for molecular docking verification.Results A total of 1 432 predicted targets of Ning-zhi capsule and 87 targets related to hyperlipidemia were found and 32 common targets were screened which covered 64 potential pharmacological ingredients of Ningzhi capsule.Potential pharmacological targets were most abundant for turmeric root-tuber,turmeric and cattail pollen,and potential pharmacological ingredients were most abundant for sickle senna seed,turmeric and turmeric root-tuber.Apolipoprotein E(APOE),nitric oxide synthase 3(NOS3)and peroxisome proliferator activated receptor alpha(PPARA)had the highest hyperlipidemia correlation scores and more protein interactions,which were potential core targets.The biological processes related to DNA transcription were significantly enriched.Cholesterol metabolism,cGMP-PKG and PPAR signaling pathways were involved with APOE,NOS3 and PPARA,respectively.Molecular docking showed good binding activity.Conclusions There are many potential pharmacological ingredients of Ningzhi capsule and the key components for lowering lipids include turmeric root-tuber,turmeric,cattail pollen and sickle senna seed.APOE,NOS3 and PPARA are believed to be the key targets for lowering lipids with potential mechanism related to cholesterol metabo-lism,cGMP-PKG and PPAR signaling pathways.