Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

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.

Nucleic acid-based molecular diagnostic methods are considered the gold standard for detecting infectious pathogens.However,when applied to portable or on-site rapid diagnostics,they still face various limitations and challenges,such as poor specificity,cumbersome operation,and portability difficulties.The CRISPR(Clustered regularly interspaced short palindromic repeats)/CRISPR-associated protein(Cas)-fluorescence detection method holds the potential to significantly enhance the specificity and signal-to-noise ratio of nucleic acid detection.In this study,we developed a portable grayscale reader detection system based on loop-mediated isothermal amplification(LAMP)-CRISPR/Cas.On one hand,in the presence of CRISPR RNA(crRNA),the CRISPR/Cas12a system was employed to achieve precise fluorescent detection of self-designed LAMP amplification reactions for influenza A and influenza B viruses.This further validated the high selectivity and versatility of the CRISPR/Cas system.On the other hand,the accompanying independently developed portable grayscale reader allowed for low-cost collection of fluorescence signals and high-reliability visual interpretation.At the end of the detection process,it directly provided positive or negative results.Practical sample analyses using this detection system have verified its reliability and utility,demonstrating that this system can achieve highly sensitive and highly specific portable analysis of influenza viruses.

The apprenticeship education of Traditional Chinese medicine(TCM)is an important pathway for the cultivation of talents in TCM education.The combination of institutional education and apprenticeship education is considered to be the most suitable educational model that aligns with the inherent characteristics of TCM education.The current status of TCM education in western medical institutions and the main challenges include the difficulty in transitioning between western and Chinese medical reasoning and limited clinical internship hours for TCM.The strengths and features of TCM apprenticeship education lie in cultural heritage,classical teachings,mentorship,practice orientation and personalized education.Therefore,integration of TCM apprenticeship education and clinical internships for western medical students represents a new educational model for medical undergraduates.

Osteosarcopenia (OS) is a multifactorial, multiaetiologic degenerative metabolic syndrome in which sarcopenia coexists with osteoporosis, and its influences are related to aging-induced mechanics, genetics, inflammatory factors, endocrine disorders, and irregular lifestyles. With the accelerated aging process in our country, osteosarcopenia has become a public health problem that cannot be ignored, with a higher risk of falls, fractures, impaired mobility and death. In recent years, scholars at home and abroad have conducted a lot of research on osteosarcopenia, but their pathogenesis is still unclear. Understanding the signaling pathways associated with osteosarcopenia is of great significance for further research on the pathogenesis of these disorders and for finding new targets for treatment. Studies have shown that activation of the PI3K/Akt signaling pathway promotes osteoblast differentiation as well as skeletal muscle regeneration, indicating that inhibition of thePI3K/Akt signaling pathway is closely related to the development of osteosarcopenia. Muscle factor-mechanical stress interactions can maintain osteoblast viability by activating the Wnt/β-catenin signaling pathway, suggesting that Wnt signaling is important in muscle and bone crosstalk. The Notch signaling pathway also plays an important role in improving bone and muscle mass and function, but different researchers hold different views, which need to be further validated and refined in subsequent studies. Exercise, as an existing non-pharmacological treatment with strong and sustained effects on physical function and muscle strength, also significantly increases bone density in osteoporosis patients, which may be mainly due to the fact that exercise induces changes in the form and function of bones, in the form of muscular pulling and indirectly improves the bone mass, and changes in the bone strength can also change the number, shape as well as the function of the muscles. At the same time, the mechanism of different exercise modalities focuses on different aspects, and there are differences in exercise time, exercise intensity, and therapeutic effects in the implementation of interventions. Aerobic exercise can improve the quality of skeletal muscle and increase the expression of osteogenesis-related genes by stimulating mitochondrial biosynthesis, as well as improve the quality and strength of bones and muscles through the Wnt/β- catenin and PI3K/Akt signaling pathways, effectively preventing and controlling the occurrence of musculoskeletal disorders. High-intensity resistance exercise has a significant effect on improving the quality of muscles and bone mineral density, but older people with osteosarcopenia suffer from a decline in muscle quality and strength, and a decline in bone mineral density, which makes them very susceptible to fracture, so they should select the intensity of the training in a gradual and orderly manner, from small to large. What kind of exercise intensity and exercise modalities are most effective in improving the occurrence and development of osteosarcopenia needs to be further investigated. Therefore, this paper mainly reviews the epidemiology of osteosarcopenia, diagnostic criteria, the related signaling pathways (PI3K/Akt pathway, Wnt/β-catenin pathway, Notch pathway, NF-κB pathway) that jointly regulate the metabolic process of myocytes and skeletal cells, as well as the interventional effects of different exercise modes on osteosarcopenia, with the aim of providing theoretical bases for the clinical treatment of osteosarcopenia, as well as enhancing the preventive capacity of the disease in old age.

ObjectiveExosomes are microvesicles which could be secreted by all cell types with diameters between 30 and 150 nm. It was widely distributed in body fluids including blood, urine, and breast milk. Exosomes are considered as potential biomarkers and drug carriers by reason of containing nucleic acids, lipids, proteins and other bioactive molecules. Milk-derived exosomes have been widely used as drug delivery carriers to treat targeted diseases with a lower cost, higher biocompatibility and lower immunogenicity. Until now, there is no research about the milk-derived exosomes phosphorylation to reveal the difference of protein phosphorylation in different species of milk. To investigate the pathways and proteins with specific functions, phosphorylated proteomic analysis of milk-derived exosomes from different species is performed, and provide new ideas for exploring diversified treatments of disease. MethodsWhey and exosomes derived from bovine, porcine and caprine milk were performed for proteomics and phosphoproteomics analysis. The relationship between milk exosome proteins from different species and signaling pathways were analyzed using bioinformatics tools. ResultsA total of 4 191 global proteins, 1 640 phosphoproteins and 4 064 phosphosites were identified from 3 species of milk-derived exosomes, and the exosome proteins and phosphoproteins from different species were significantly higher than those of whey. Meanwhile, some special pathways were enriched like Fcγ-mediated phagocytosis from bovine exosomes, pathways related with neural and immune system from caprine exosomes, positive and negative regulation of multiple activities from porcine exosomes. ConclusionIn this study, the proteomic and phosphoproteomic analyses of exosomes and whey from bovine, porcine and caprine milk were carried out to reveal the difference of composition and related signaling pathways of milk exosome from different species. These results provided powerful support for the application of exosomes from different milk sources in the field of disease treatment.