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.

Objective:To establish a general method for the simultaneous determination of hydroxyphenyl esters and quaternary ammonium bacteriostatic agents in eye drops.Methods:The chromatographic analysis was per-formed on an Agilent C18 column(4.6 mm ×250 mm,5 μm)with 1％triethylamine solution(pH adjusted to 5.0 with phosphoric acid)as mobile phase A and methanol as mobile phase B.Gradient elution was performed at col-umn temperature of 40 ℃.The detection wavelength was 214 nm,the flow rate was 1 mL·min-1,and the injec-tion volume was 20 μL.Results:Methylparaben,ethylparaben,propylparaben,butylparaben,benzalkonium chlo-ride and benzalkonium bromide were 0.11-559.0,0.10-513.0,0.10-258.8,0.11-270.5,1.07-537.0 and 1.03-512.8 μg·mL-1,respectively.The linear range was good(r＞0.999).The average recoveries of meth-ylparaben,benzalkonium bromide and benzalkonium chloride were 104.7％(RSD=1.3％),102.6％(RSD=1.1％)and 100.9％(RSD=1.1％),respectively.The contents of bacteriostatic agent in 100 batches of eye drops from 36 varieties of 12 enterprises were determined,and the accurate results were obtained.Conclusion:This meth-od provides a reference for the content quality control and safety evaluation of bacteriostatic agents in eye drops.

Objective To compare the cost and utility of aflibercept and conbercept for the treatment of wet age-related macular degeneration(wetAMD),in order to provide a reference for the selection of treatment regimens from the perspective of pharmacoeconomics.Methods The incremental cost-utility ratio(ICUR)was obtained by using Markov model to simulate the survival of the two treatment regimens over the 5-year period,calculating costs and health outputs separately.Univariate sensitivity analysis was used to determine the impact of the parameter on ICUR,and probability sensitivity analysis was used to determine the influence of the uncertainty of each model parameter on the research results.One times the 2022 gross domestic product(GDP)per capita of China was used as the willingness-to-pay threshold(WTP)to judge its economy.Results Over the simulation period,the compazine regimen was significantly economical against the aflibercept regimen,with an ICUR of-1 528 840 per quality-adjusted life year(QALY),which was lower than the WTP.Univariate sensitivity analysis showed that the transition probability between mild and moderate visual status between the two regimens and the number of aflibercept injections per year were significant influencing factors of ICUR.Probabilistic sensitivity analysis pointed to a significant cost-utility advantage for conbercept at a WTP of one times GDP(probability of 65.9％),which was a more robust result.Conclusion For the treatment of wetAMD,conbercept has a cost-utility advantage compared with aflibercept.

Objective To explore the molecular mechanism of chronic osteomyelitis and to clarify the role of MAPK signal pathway in the pathogenesis of chronic osteomyelitis,by collecting and analyzing the transcriptional information of bone tissue in patients with chronic osteomyelitis.Methods Four cases of traumatic osteomyelitis in limbs from June 2019 to June 2020 were selected,and the samples of necrotic osteonecrosis from chronic osteomyelitis(necrotic group),and normal bone tissue(control group)were collected.Transcriptome information was collected by Illumina Hiseq Xten high throughput sequencing platform,and the gene expression in bone tissue was calculated by FPKM.The differentially expressed genes were screened by comparing the transcripts of the Necrotic group and control group.Genes were enriched by GO and KEGG.MAP3K7 and NFATC1 were selected as differential targets in the verification experiments,by using rat osteomyelitis animal model and im-munohistochemical analysis.Results A total of 5548 differentially expressed genes were obtained by high throughput sequenc-ing by comparing the necrotic group and control group,including 2701 up-regulated and 2847 down-regulated genes.The genes enriched in MAPK pathway and osteoclast differentiation pathway were screened,the common genes expressed in both MAPK and osteoclast differentiation pathway were(inhibitor of nuclear factor κ subunit Beta,IκBKβ),(mitogen-activated protein ki-nase 7,MAP3K7),(nuclear factor of activated t cells 1,NFATC1)and(nuclear factor Kappa B subunit 2,NFκB2).In rat os-teomyelitis model,MAP3K7 and NFATC1 were highly expressed in bone marrow and injured bone tissue.Conclusion Based on the transcriptome analysis,the MAPK signaling and osteoclast differentiation pathways were closely related to chronic os-teomyelitis,and the key genes IκBKβ,MAP3K7,NFATC1,NFκB2 might be new targets for clinical diagnosis and therapy of chronic osteomyelitis.

Objective To investigate the expression and prognostic value of spindle and kinetochore-associated complex subunit 2(SKA2)in cervical cancer tissues,as well as its impact on the proliferation,migration and invasion of cervical cancer cells.Methods The expression of SKA2 in cervical cancer tissues was analyzed by bioinformatics database and immunohistochemical SP method,and the relationship between SKA2 expression level and clinicopathological features of cervical cancer patients and its prognostic value was analyzed.The mRNA expression of SKA2 in human normal cervical cells(HcerEpic)and cervical cancer cells(HeLa,SiHa,CaSki,C-33A)was detected by RT-qPCR.Cervical cancer cells SiHa with higher SKA2 expression level was selected for further study.SiHa cell model with down-regulated SKA2 expression was constructed,and its knockdown effect was verified.Cell proliferation capacity was detected by CCK-8 method,cell migration capacity was detected by cell scratch wound healing assay,and cell migration and invasion capacity was detected by Transwell assay.Results Compared with normal cervical tissues and cells,the expression levels of SKA2 mRNA and protein were higher in cervical cancer tissues and cells,and the differences were statistically significant(P<0.05).High SKA2 expression was associated with FIGO staging in patients with cervical cancer.Furthermore,SKA2 knockdown could inhibit the proliferation,migration and invasion of SiHa cells in cervical cancer(P<0.05).Conclusion SKA2 is up-regulated in cervical cancer tissues and cells,and can promote the proliferation,migration and invasion of cervical cancer cells.The expression level of SKA2 is associated with the progression of cervical cancer,and the prognosis of cervical cancer patients with high SKA2 expression is worse.