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.

Objective:To construct a representative index system for evaluating pediatric orthopedic nursing quality, providing a basis for hospital pediatric orthopedic nursing quality assessment and monitoring.Methods:From April to July 2023, using the "structure-process-outcome" three-dimensional quality structure model as the theoretical framework, a literature review was conducted, and an item pool was formulated. Through two rounds of Delphi method expert consultations, the hierarchical analysis method was finally employed to determine the indicators and their weights at each level.Results:The effective recovery rates of the questionnaire of the two rounds of expert consultations were 100% (20/20), the authority coefficients of experts were 0.87 and 0.88, the coefficients of variation were 0.00 to 0.27 and 0.00 to 0.24. The Kendell harmony coefficients of the second and third indicators in the two rounds of inquiry were 0.140, 0.166 and 0.192, 0.161(all P<0.05). The final pediatric orthopedic nursing quality evaluation index system included 3 primary indicators, 21 secondary indicators and 83 tertiary indicators. Among the primary indicators, the weight of process quality was the highest at 0.493 4, followed by outcome quality at 0.310 8, and the lowest was structural quality at 0.195 8. In the secondary indicators, "assessment criteria of limb blood circulation" had the highest weight at 0.099 8. Conclusions:The constructed pediatric orthopedic nursing quality evaluation index system covers key aspects and is more operationally feasible. It provides better guidance for nursing interventions and quality control.

Objective To prepare a nano drug(PFOB@Lip-MMC)with liposome as the carrier,liquid perfluorooc-tyl bromide(PFOB)as core and mitomycin C(MMC)loading on the liposome shell and study its inhibitory effect on the proliferation of human pterygium fibroblasts(HPFs).Methods The thin film dispersion-hydration ultrasonic method was used to prepare PFOB@Lip-MMC and detect its physical and chemical properties.Cell Counting Kit-8,Cam-PI cell viability staining and flow cytometry were employed to detect the impact of different concentrations of PFOB@Lip-MMC on the via-bility of HPFs.DiI fluorescence labeled PFOB@Lip-MMC was used to observe the permeability of the nano drug to HPFs under a laser confocal microscope.After establishing HPF inflammatory cell models,they were divided into the control group(with sterile phosphate-buffered saline solution added),PFOB@Lip group(with PFOB@Lip added),MMC group(with MMC added),PFOB@Lip-MMC group(with PFOB@Lip-MMC added)and normal group(with fresh culture medi-um added)according to the experimental requirements.After co-incubation for 24 h,flow cytometer was used to detect the apoptosis rate of inflammatory cells,and the gene expression levels of interleukin(IL)-1β,prostaglandin E2(PGE2),tumor necrosis factor(TNF)-α and vascular endothelial growth factor(VEGF)in cells were analyzed by PCR.Results The average particle size and Zeta potential of PFOB@Lip-MMC were(103.45±2.17)nm and(27.34±1.03)mV,respec-tively,and its entrapped efficiency and drug loading rate were(72.85±3.28)％and(34.27±2.04)％,respectively.The sustained-release MMC of drug-loaded nanospheres reached(78.34±2.92)％in vitro in a 24-hour ocular surface environ-ment.The biological safety of PFOB@Lip-MMC significantly improved compared to MMC.In terms of the DiI fluorescence labeled PFOB@Lip-MMC,after co-incubation with inflammatory HPFs for 2 h,DiI fluorescence labeling was diffusely dis-tributed in the cytoplasm of inflammatory HPFs.The apoptosis rate of inflammatory HPFs in the PFOB@Lip-MMC group[(77.23±4.93)％]was significantly higher than that in the MMC group[(51.62±3.28)％].The PCR examination results showed that the gene transcription levels of IL-1 β,PGE2,TNF-α and VEGF in other groups were significantly reduced com-pared to the control group and PFOB@Lip group,with the most significant decrease in the PFOB@Lip-MMC group(all P＜0.05).Conclusion In this study,a novel nano drug(PFOB@LIP-MMC)that inhibited the proliferation of HPFs was successfully synthesized,and its cytotoxicity was significantly reduced compared to the original drugs.It has good bio-compatibility and anti-inflammatory effects,providing a new treatment approach for reducing the recurrence rate after pte-rygium surgery.

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.