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.

Background The affinity between placental transporters and perfluoroalkyl substances (PFAS) could affect the placental transport and toxicity of PFAS, while the study on the interaction between PFAS and placental transporters is limited. Objective To explore interactions between PFAS and placental transporters using molecular docking, and to provide a theoretical basis for PFAS toxicity prediction and fetal health risk assessment. Methods Fifteen PFAS compounds, each conformationally sampled and energy-minimized, and 16 placental transporters, represented by their 3D structures, were imported into a molecular docking software (MOE 20140901). For each PFAS, 30 distinct conformations were generated and docked into the active pockets of the transporters using a semi-flexible docking mode. Docking poses were primarily scored and ranked based on their calculated binding free energy (ΔG, kcal·mol⁻¹), with additional consideration given to hydrogen bonding interactions and the ligand's root mean square deviation (RMSD) at the binding site; the top 20 poses for each complex were subsequently output. Optimal binding configurations were identified as those exhibiting a relatively low binding free energy (ΔG ranging from −3 to −10 kcal·mol⁻¹), well-defined hydrogen bonds, and an RMSD ≤ 2.0 Å. The binding capabilities of the PFAS to the placental transporters were then evaluated based on these optimal docking results. Results The PFAS could bind to the placental transporters, with structural specificity. For example, the binding capabilities increased as the carbon chain length of PFAS increased, and it was also higher for PFOS alternatives than for PFOS. Besides, the binding capabilities of sulfonic PFAS with the same carbon chain length was also stronger than that of carboxylic PFAS. For example, the binding capabilities of PFOS (C8) to 15 placental transporters was higher than that of PFOA (C8), except for glucose transporter 1 (PFOS vs. PFOA: −4.14 vs. −4.14). Further, PFAS might be bound to the placental transporter through hydrogen, ionic, and hydrophobic interactions. Conclusion PFAS are able to bind the placental transporters, and its toxicity and exposure risk can’t be ignored.

Objective To explore the effects of oral voriconazole(VRC)on the pharmacokinetics of tacrolimus(TAC)in renal transplant patients.Methods Renal transplant patients who had taken TAC orally for more than 2 days and achieved steady-state plasma concentration before taking VRC.The trough concentration of TAC was measured on the 3rd,5th and 10th days after VRC 200 or 400 mg·d-1 administration.The trough concentration(C0)of TAC was determined by high performance liquid chromatography.The genotypes of TAC were determined by polymerase chain reaction and the pharmacokinetics of TAC after combined use of VRC were compared.Results After the use of VRC,the TAC C0 of 11 renal transplant patients was 3-8 μg·L-1,and the concentration of TAC ranged from 50.00％to 87.50％of the original dose.Additionally,the impact of VRC on TAC varied significantly among individuals.The mean TAC C0 value after VRC administration was significantly higher than the value before VRC[(12.14±3.89)vss(5.20±2.79)μg·L-1].Eleven renal transplant patients were grouped according to cytochrome P450(CYP)2C19-CYP3A5 gene polymorphism,under the condition of combined administration,the C0/dose of TAC in the slow metabolizer group was higher than that in the fast metabolizer group on the 3rd,5th and 10th days[(582.10±252.30)vs(439.03±166.08),(873.71±449.22)vs(666.60±168.00),(852.10±505.73)vs(261.50±81.98)μg·L-1·mg-1·kg;all P＜0.01].Conclusion TAC pharmacokinetics was significantly affected by the VRC in renal transplant recipients,and the principle that TAC dose needed to be reduced by one-third of the original dose was no longer applicable,which may be related to the pharmacokinetics of the VRC itself and the gene polymorphism of CYP2C19/CYP3A5 enzyme.It is recommended to regularly monitor the concentration of TAC when VRC and TAC are used in combination.

Objective: To evaluate the effectiveness and safety of modified Xiaoyao powder for postpartum depression (PPD) by conducting a systematic review of randomized controlled trials (RCTs). Methods: The Chinese National Knowledge Infrastructure Databases (CNKI), the Chinese Scientific Journals Database (VIP), Wanfang, Google Scholar, the SinoMed, Embase, Cochrane Library, and PubMed databases were searched from their inception to April 25, 2023. The Cochrane Risk of Bias tool was used to assess the quality of the trials. We applied the risk ratio to present dichotomous data and the mean difference to present continuous data. Data with similar characteristics were pooled for meta-analysis and heterogeneity was assessed using I2. Results: This review included 35 trials involving 2848 participants. The quality of the included studies was low (unclear randomization processes and insufficient reporting of blinding). Participants treated with modified Xiaoyao powder plus Western medicine showed lower Hamilton Depression Scale (HAMD) depression score than those who used Western medicine alone (mean difference = −2.15; 95% confidence interval:−2.52 to 1.78; P < .00001), and higher effective rate (relative risk = 1.19; 95% confidence interval: 1.15 to 1.24; P < .00001), When comparing modified Xiaoyao alone with Western medicine, the HAMD depression score remained low, however, the efficacy rate was higher in the modified Xiaoyao group. Regarding adverse events, the modified Xiaoyao group reported weight gain, nausea, and diarrhea, but no severe adverse events were reported. Conclusion Modified Xiaoyao may help relieve depression in PPD when used alone or in combination with Western medicine, with minor side effects. Therefore, future high-quality, large-sample size RCTs are warranted.

In order to solve the problem of resistance of Pseudomonas aeruginosa to multiple antibiotics, it is an effective way to find inhibitors of P. aeruginosa efflux pump. In this study, 15 new ornithine peptidomimetic derivatives were designed and synthesized by changing the side chain structure of natural amino acids with PAβN, a dipeptide efflux pump inhibitor, and their synergic activity with aztreonam, a monocyclic β-lactam antibiotic, against P. aeruginosa was evaluated. Among them, the representative compound 12b not only enhanced the antibacterial activity of β-lactam antibiotics aztreonam, ceftazidime and meropenem, but also significantly enhanced the antibacterial action of macrolide antibiotics clarithromycin, showing a broad-spectrum synergic sensitization effect. In addition, compound 12b also has a good safety. Preliminary mechanisms suggest that 12b works by directly targeting the efflux transporter MexB. These results provide a new lead compound for the development of a new class of efflux pump inhibitors against P. aeruginosa.