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.

AIM: To prepare a nanodrug MMC-ATS-@PLGA using polylactic acid hydroxyacetic acid copolymer(PLGA)as a carrier and mitomycin C(MMC)loaded on PLGA, and to analyse the biological safety and treatment effect of this nanodrug on inhibiting the proliferation of filtering bleb scarring after glaucoma surgery in vivo.METHODS: The thin-film dispersion hydration ultrasonic method was used to prepare the MMC-ATS-@PLGA, and its physical and chemical properties were detected. The effect of MMC-ATS@PLGA on rabbit corneas was analysed through corneal fluorescence staining and HE staining, and tear film rupture time(BUT), Schirmer test and intraocular pressure data were collected to analyse ocular surface biosafety. A slit lamp was used to observe and calculate the filtration bubble size, and the tissue morphological changes were analysed by conjunctival HE staining. In addition, immunohistochemistry and Elisa were used to compare the anti-inflammatory effects of Flumiolone Eye Drops(FML), MMC, and MMC-ATS-@PLGA nanoparticles on inhibiting the formation of filtering bleb scarring after glaucoma surgery from multiple perspectives via comparative proteomic analysis.RESULTS: The average particle size and zeta potential of MMC-ATS-@PLGA were 128.78±2.54 nm and 36.49±4.25 mV, respectively, with an encapsulation efficiency and a drug loading rate of(78.49±2.75)% and(30.86±1.84)%, respectively. At 33°C(the ocular surface temperature), the cumulative release rate of the MMC-ATS-@PLGA nanoparticles reached(76.58±2.68)% after 600 min. Moreover, corneal fluorescence staining, HE, BUT, Schirmer, and intraocular pressure results showed that MMC-ATS-@PLGA had good biocompatibility with the ocular surface of rabbits. At 3 wk after surgery, the area of filtering blebs in the MMC-ATS-@PLGA group was significantly larger than that in the FML group and MMC group, and the filtering blebs in the control group had basically disappeared. Pathological tissue analysis of the conjunctiva in the filtering blebs area of the eyes of the rabbits revealed that compared with that in the normal group, the morphology of the collagen fibres in the MMC-ATS-@PLGA group was relatively regular, the fibres were arranged neatly, and the tissue morphology was similar to that of the normal group. Immunohistochemistry and Elisa confirmed that compared with those in the normal group, the expression levels of α-SMA, CTGF, and type Ⅲ collagen fibre antibodies were significantly increased in the control group. After FML, MMC, or MMC-ATS-@PLGA treatment for 3 wk, the expression of inflammatory factors gradually decreased. Among the groups, the MMC-ATS-@PLGA group showed the most significant decrease(P<0.05).CONCLUSION: This study successfully synthesized a nanomedicine(MMC-ATS-@PLGA)that inhibits scar proliferation after glaucoma filtration surgery. The drug had stable physicochemical properties, good biocompatibility, and better anti-inflammatory effects by inhibiting the expression of α-SMA, CTGF, and type Ⅲ collagen fibres, which can prevent the formation of scarring in the filtering blebs area, thereby improving the success rate of glaucoma filtering surgery.

Traditional decoction pieces have low efficiency, poor batch-to-batch consistency, and irregular physical form, making it difficult to meet the demands of modern automated production and precise and rapid clinical blending. Therefore, this study aims to develop a new type of granular drinking tablet to meet the demand for high-quality development in the traditional Chinese medicine industry. In the current study, the differences and similarities between the new Lonicerae Japonicae Flos (LJF) granular drinking tablets and the traditional ones were evaluated based on the flowability, the paste rate of the standard soup, the characterization fingerprint, the degree of pasting, the content of active ingredients, the transfer rate, and its traditional antipyretic and anti-inflammatory efficacy, using the traditional LJF decoction piece as a reference. The flowability experiments showed that the flowability of medium-sized granules (10-24 mesh) was significantly better than that of traditional drinking tablets (P < 0.01); the results of the paste rate showed that there was no significant difference between the different particle sizes and the original decoction pieces (P > 0.05), but the small particle sizes (24-65 mesh) had poor decoction clarity and gelatinization; the transfer rate was calculated as chlorogenic acid and luteoloside, and there was no significant difference in the transfer rate between traditional slices and different particle sizes (P > 0.05); the pharmacological results showed that the contents of rat tumor necrosis factor-α (TNF-α), rat interleukin-1β (IL-1β) and rat prostaglandin E₂ (PGE₂), xylene ear swelling inhibition rate and granuloma inhibition rate showed that there was no significant difference between the traditional and new granule decoction pieces (P > 0.05). In this study, by examining the fluidity, paste rate, paste degree, and antipyretic and anti-inflammatory effects of the new granular decoction piece of LJF, it was initially revealed that the new granular drinking tablets of LJF were consistent with the basic properties and pharmacological effects of the commercially available traditional drinking tablets. Still, the new granular tablets were characterized by high utilization rate, homogeneous quality, and ease of clinical transfer, which had a good prospect for application. The animal experiment was approved by the Ethics Committee of the China Academy of Chinese Medical Sciences (approval number. 2022B214).