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.

Transcranial temporal interference stimulation (tTIS) is a novel non-invasive neuromodulation technique with the potential to precisely target deep brain structures. This study explores the neural and behavioral effects of tTIS on the superior colliculus (SC), a region involved in eye movement control, in mice. Computational modeling revealed that tTIS delivers more focused stimulation to the SC than traditional transcranial alternating current stimulation. In vivo experiments, including Ca²⁺ signal recordings and eye movement tracking, showed that tTIS effectively modulates SC neural activity and induces eye movements. A significant correlation was found between stimulation frequency and saccade frequency, suggesting direct tTIS-induced modulation of SC activity. These results demonstrate the precision of tTIS in targeting deep brain regions and regulating eye movements, highlighting its potential for neuroscientific research and therapeutic applications.
Animals ; Superior Colliculi/physiology* ; Transcranial Direct Current Stimulation/methods* ; Eye Movements/physiology* ; Male ; Mice ; Mice, Inbred C57BL

OBJECTIVE: The objective of our study was to evaluate the vaccine effectiveness (VE) of the pentavalent rotavirus vaccine (RV5) among < 5-year-old children in three provinces of China during 2020-2024 via a propensity score-matched test-negative case-control study. METHODS: Electronic health records and immunization information systems were used to obtain data on acute gastroenteritis (AGE) cases tested for rotavirus (RV) infection. RV-positive cases were propensity score matched with RV-negative controls for age, visit month, and province. RESULTS: The study included 27,472 children with AGE aged 8 weeks to 4 years at the time of AGE diagnosis; 7.98% (2,192) were RV-positive. The VE (95% confidence interval, CI) of 1-2 and 3 doses of RV5 against any medically attended RV infection (inpatient or outpatient) was 57.6% (39.8%, 70.2%) and 67.2% (60.3%, 72.9%), respectively. Among children who received the 3rd dose before turning 5 months of age, 3-dose VE decreased from 70.4% (53.9%, 81.1%) (< 5 months since the 3rd dose) to 63.0% (49.1%, 73.0%) (≥ 1 year since the 3rd dose). The three-dose VE rate was 69.4% (41.3%, 84.0%) for RVGE hospitalization and 57.5% (38.9%, 70.5%) for outpatient-only medically attended RVGE. CONCLUSION Three-dose RV5 VE against rotavirus gastroenteritis (RVGE) in children aged < 5 years was higher than 1-2-dose VE. Three-dose VE decreased with time since the 3rd dose in children who received the 3rd dose before turning five months of age, but remained above 60% for at least one year. VE was higher for RVGE hospitalizations than for medically attended outpatient visits.
Humans ; Rotavirus Vaccines/immunology* ; China/epidemiology* ; Case-Control Studies ; Child, Preschool ; Infant ; Rotavirus Infections/epidemiology* ; Male ; Propensity Score ; Female ; Vaccine Efficacy ; Gastroenteritis/virology* ; Vaccines, Attenuated ; Rotavirus

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective To study the traditional Chinese medicine(TCM)syndromes of patients with chronic heart failure(CHF)combined with diuretic resistance by using the Wenyang Huayin Yangxin Prescription,and to observe its therapeutic efficacy.Methods A total of 68 CHF patients complicated with diuretic resistance and who had Yangqi deficiency and presenting blood stasis syndrome combined with Tanyin were randomly assigned to a control group and an observation group.The control group was given intravenous furosemide(≥ 80 mg/d)via infusion pump in addition to standard Western medical treatment,while the observation group was given intravenous furosemide(＜80 mg/d)via infusion pump along with the Wenyang Huayin Yangxin Prescription(30 g Astragalus,15 g Poria,15 g Baizhu,15 g Chuanxiong,10 g Danfu tablet,10 g Cassia,10 g Alisma,and 10 g Zhimu).The quantitative index of diuretic resistance was used as the primary outcome measure.In addition,the differences between the two groups in TCM syndromes,cardiac function-related indicators,incidence of endpoint events,and readmission rate were compared.Results After 2 weeks of treatment,the filtration sodium excretion fraction(FENa)in the observation group was(0.18±0.04)％,while that of the control group was(0.16±0.03)％,showing a statistically significant difference(P=0.037).The 24-hour urine volume and urine Na+/K+ratio in the observation group increased significantly from baseline levels and were higher than those in the control group(P＜0.05).The differences in the changes of 24-hour urine volume,urine sodium,FENa,and urine Na+/K+ratio between the two groups were statistically significant(P＜0.05).The TCM syndrome scores decreased in both groups after 2 weeks of treatment,with the observation group showing a significantly greater reduction compared with the control group(P＜0.001).The differences in the changes of TCM syndrome scores between the two groups were statistically significant(P＜0.001).After 2 weeks of treatment,the observation group showed significant improvements in palpitations,shortness of breath,facial and limb edema,spontaneous sweating,chest tightness(pain),asthma,and oliguria compared with the baseline data(P＜0.05),while the control group showed improvements only in facial and limb edema,asthma,and oliguria(P＜0.05).Except for the asthma syndrome after 2 weeks of treatment,the observation group showed better outcomes in spontaneous sweating,chest tightness(pain),asthma,and oliguria at various time points after treatment compared with the control group(P＜0.05).After 2 weeks of treatment,the observation group had significantly better cardiac output(CO)and stroke volume(SV)compared with those of the control group(P＜0.05).The differences in the changes in N-terminal pro-brain natriuretic peptide(NT-proBNP),left ventricular ejection fraction(LVEF),SV,and CO between the two groups were statistically significant(P＜0.05).After 24 weeks of follow-up,no significant differences in the incidence of end-point events or readmission rates between the two groups were observed.Conclusion The Wenyang Huayin Yangxin Prescription,combined with low-dose intravenous furosemide administered through an infusion pump,can improve the TCM syndromes of patients with Yangqi deficiency and blood stasis syndrome combined with Tanyin in addition to CHF complicated by diuretic resistance.This treatment improves the patients'heart function and diuretic resistance,reduces the intravenous dosage of diuretic,and enhances clinical efficacy.This approach should be more widely applied in clinical settings.

Objective To explore the application and effectiveness of the apprenticeship education model in Chinese herbal medicine teaching at Western medical college.Methods By comparing classic lecture-based teaching with a combined approach integrating apprenticeship education,the study assesses the impact on student learning outcomes.Participants included students from the 2018 cohort of the eight-year clinical medicine program and the 2022 cohort of the"4+4"pilot program at Peking Union Medical College,who received classic teaching methods and apprenticeship case-based teaching methods,respectively.Upon course completion,students completed a 14-item multiple-choice questionnaire covering essential theory of Chinese medicine,as well as specific categories such as qi-regulating,blood-activating herbs,among others.Results The overall accuracy rate in the apprenticeship case-based teaching group was significantly higher than that in the classic teaching group(P＜0.01).Conclusions The apprenticeship education model of Traditional Chinese Medicine has a positive effect on teaching of Chinese herbal medicine at West-ern medical college and warrants further promotion and application.