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] To evaluate the feasibility of a novel outpatient infusion model for blinatumomab in two acute lymphoblastic leukemia (ALL) patients, aiming to address challenges of poor treatment tolerance, high healthcare costs, and compromised quality of life, thereby providing clinical insights for broader adoption of this approach. [Methods] Two post-allogeneic hematopoietic stem cell transplantation (allo-HSCT) patients undergoing blinatumomab maintenance therapy were selected to evaluate the efficacy of the outpatient infusion model. Patient selection criteria, nursing protocols, standardized workflows, and advancements in infusion practices were systematically analyzed combined with a review of global developments in this field. [Results] Both patients completed outpatient blinatumomab infusion without severe adverse events, demonstrating preliminary feasibility and safety of this model. The novel approach enhanced treatment convenience, reduced hospitalization costs, and improved quality of life. [Conclusion] Despite the limited sample size, this pilot study highlights the potential of outpatient blinatumomab administration as a viable alternative to traditional inpatient regimens.

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.

In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

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 investigate the protective effect of dexmedetomidine(Dex)pretreatment on myocardial ischemia-reperfusion mice and the effect of Nod-like receptor protein 3(NLRP3)inflammatory signaling pathway.Methods C57BL/6J mice were randomly divided into sham group(only threading without ligation),model group(recovery after ligation of left anterior descending coronary artery),positive group(modeling after intraperitoneal injection of 1 mg·kg-1 trimetazidine),Dex group(modeling after intraperitoneal injection of 20 μg·kg-1 dexmedetomidine),MCC950 group(modeling after intraperitoneal injection of 10 mg·kg-1 NLRP3 inhibitor MCC950),12 mice in each group.Cardiac function indexes were detected at 24 h after reperfusion,the expression level of related proteins in myocardial tissue was detected by Western blot,enzyme-linked immunosorbent assay(ELISA)was used to detect the expression level of serum factor,myocardial antioxidant index was detected by kit method,and apoptosis was detected by Tunel method.Results The NLRP3 protein expression levels of sham group,model group,positive group,Dex group and MCC950 group were 0.31±0.05,1.06±0.07,0.52±0.05,0.65±0.07 and 0.39±0.04,respectively;the expression levels of apoptosis-associated granuloid protein(ASC)were 0.27±0.08,0.88±0.09,0.46±0.05,0.59±0.07 and 0.34±0.04,respectively;CK-MB levels were(25.64±2.94),(102.08±7.04),(49.61±7.70),(60.86±5.24)and(63.24±5.38)U·L-1,respectively;IL-6 levels were(104.78±10.73),(231.54±15.56),(158.20±16.54),(165.10±14.77)and(141.17±14.08)pg·mL-1,respectively;Tunel positive cell rates were(4.34±0.16)％,(25.98±1.58)％,(8.74±0.93)％,(11.06±1.07)％and(9.19±0.88)％,respectively.Sham group were compared with model group;Dex group and MCC950 group were compared with model group,the above indexes were statistically significant(all P＜0.05).Conclusion Dexmedetomidine preconditioning may prevent ischemia-reperfusion myocardial injury by inhibiting NLRP3/ASC/caspase-1 inflammatory pathway,inflammatory response and myocardial cell apoptosis.