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.

With the completion of the "Human Genome Project" and the smooth progress of the "Herbal Genome Project", the research wave of RNAomics is gradually advancing, opening the research gateway for the modernization of traditional Chinese medicine (TCM) and initiating the post-genome era of medicinal plant RNA research. Therefore, this article proposes for the first time the concept of HerbRNomes, which involves constructing databases of medicinal plant, medicinal fungus, and medicinal animal RNA at different stages, from different origins, and in different organs. This research aims to explore the role of HerbRNA in self-genetic information transmission, functional regulation, as well as cross-species regulation functional mechanisms and key technologies. It also investigates application scenarios, providing a theoretical basis and research ideas for the resistance of TCM or medicinal plants to adversity and stress, molecular assistant breeding, and the development of small nucleic acid drugs. This article reviews recent research progress in elucidating the molecular mechanisms of the transmission and expression of genetic information, self-regulation and cross-species regulation of herbs at the RNA level, along with key technologies. It proposes a development strategy for small nucleic acid drugs based on HerbRNomes, providing theoretical support and guidance for the modernization of TCM based on HerbRNomes research.

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.

Neuroscience is a significant frontier discipline within the natural sciences and has become an important interdisciplinary frontier scientific field. Brain is one of the most complex organs in the human body, and its structural and functional analysis is considered the “ultimate frontier” of human self-awareness and exploration of nature. Driven by the strategic layout of “China Brain Project”, Chinese scientists have conducted systematic research focusing on “understanding the brain, simulating the brain, and protecting the brain”. They have made breakthrough progress in areas such as the principles of brain cognition, mechanisms and interventions for brain diseases, brain-like computation, and applications of brain-machine intelligence technology, aiming to enhance brain health through biomedical technology and improve the quality of human life. Due to limited understanding and comprehension of neuroscience, there are still many important unresolved issues in the field of neuroscience, resulting in a lack of effective measures to prevent and protect brain health. Therefore, in addition to actively developing new generation drugs, exploring non pharmacological treatment strategies with better health benefits and higher safety is particularly important. Epidemiological data shows that, exercise is not only an indispensable part of daily life but also an important non-pharmacological approach for protecting brain health and preventing neurodegenerative diseases, forming an emerging research field known as motor neuroscience. Basic research in motor neuroscience primarily focuses on analyzing the dynamic coding mechanisms of neural circuits involved in motor control, breakthroughs in motor neuroscience research depend on the construction of dynamic monitoring systems across temporal and spatial scales. Therefore, high spatiotemporal resolution detection of movement processes and movement-induced changes in brain structure and neural activity signals is an important technical foundation for conducting motor neuroscience research and has developed a set of tools based on traditional neuroscience methods combined with novel motor behavior decoding technologies, providing an innovative technical platform for motor neuroscience research. The protective effect of exercise in neurodegenerative diseases provides broad application prospects for its clinical translation. Applied research in motor neuroscience centers on deciphering the regulatory networks of neuroprotective molecules mediated by exercise. From the perspectives of exercise promoting neurogenesis and regeneration, enhancing synaptic plasticity, modulating neuronal functional activity, and remodeling the molecular homeostasis of the neuronal microenvironment, it aims to improve cognitive function and reduce the incidence of Parkinson’s disease and Alzheimer’s disease. This has also advanced research into the molecular regulatory networks mediating exercise-induced neuroprotection and facilitated the clinical application and promotion of exercise rehabilitation strategies. Multidimensional analysis of exercise-regulated neural plasticity is the theoretical basis for elucidating the brain-protective mechanisms mediated by exercise and developing intervention strategies for neurological diseases. Thus,real-time analysis of different neural signals during active exercise is needed to study the health effects of exercise throughout the entire life cycle and enhance lifelong sports awareness. Therefore, this article will systematically summarize the innovative technological developments in motor neuroscience research, review the mechanisms of neural plasticity that exercise utilizes to protect the brain, and explore the role of exercise in the prevention and treatment of major neurodegenerative diseases. This aims to provide new ideas for future theoretical innovations and clinical applications in the field of exercise-induced brain protection.

ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.

ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.

Objective To evaluate the economic value of using glucagon-like peptide-1 receptor agonist(GLP-1RA)in combination with metformin for the treatment of type 2 diabetes mellitus(T2DM).Methods Based on 7 randomized controlled clinical trials(RCTs),Markov model was built to simulate the dynamic changes of metformin alone or combined with GLP-1RA in the treatment of T2DM patients without or with complications and death from the perspective of China's health system.Quality-adjusted life years(QALYs)was used as a health output indicator and 3 times China's per capita gross domestic product(GDP)in 2023 was set as the willingness-to-pay(WTP)threshold.The cycle was at the rate of 1 year and a total of 20 years cohort simulation in Markov model was applied to obtain long-term cost and effect of each treatment strategy.The incremental cost-utility ratio(ICUR)was analyzed as the primary evaluation indicator and the sensitivity of cost,utility and discount was performed to test the stability of the results.Results Compared with metformin alone,the ICUR of GLP-1RA including liraglutide,dulaglutide,exenatide,loxenatide,semaglutide combined with metformin were all below the WTP threshold,and the increased cost was acceptable.Extending the simulation time to 30 years or 50 years had no effect on results.The results of probability sensitivity analysis showed that the cost effect of semaglutide 0.5 mg combined with metformin had the highest probability of a cost-utility advantage of 99.7％among all the treatment strategies when WTP threshold was 3 times China's per capita GDP in 2023(268 074 yuan).Conclusion GLP-1RA,including liraglutide,dulaglutide,exenatide,lixisenatide,and semaglutide,at the regular recommended dose combined with metformin,would present higher cost-utility compared to metformin monotherapy.