OBJECTIVE To construct a classification prediction model for vancomycin blood concentration， and to optimize its precision dosing strategies. METHODS Patient records meeting inclusion criteria were extracted from the Medical Information Mart for Intensive Care database. Following data cleaning and preprocessing， a final cohort of 9 902 patient was analyzed. Feature selection was performed through correlation analysis and the Boruta feature selection algorithm. Vancomycin blood concentrations were discretized into three categories based on clinical therapeutic windows： low （＜10 μg/mL）， intermediate （10-20 μg/mL）， and high （≥20 μg/mL）. Six machine learning algorithms were employed to construct classification models： tabular prior-data fitted network （TabPFN）， logistic regression （LR）， random forest （RF）， extreme gradient boosting （XGBoost）， support vector machine （SVM）， K-nearest neighbors （KNN）. Model performance was evaluated using 10-fold cross-validation （10-CV）， with primary metrics including： accuracy， balanced accuracy， precision macro， recall macro， macro F1， area under the receiver operating characteristic curve （OvR-AUC）. Shapley Additive Explanations （SHAP） was adopted to analyze the direction and magnitude of the impact that different features had on the model’s predictive outcomes. RESULTS The results showed that the RF and TabPFN models performed the best （with accuracy of 0.741 4 and 0.737 7， and OvR-AUC of 0.907 0 and 0.895 8， respectively）. XGBoost model exhibited moderate performance， while LR， SVM， and KNN models demonstrated relatively poor performance. Confusion matrix heatmap analysis revealed that both RF and TabPFN achieved higher accuracy in predicting high- concentration cases but exhibited slightly lower performance in the low and medium concentration categories. Bootstrap with 10-CV revealed that the RF model demonstrated stable performance across various evaluation metrics （accuracy： 0.741 4； balanced accuracy： 0.740 3； precision macro： 0.732 1； recall macro： 0.736 0； macro F1： 0.736 0； OvR-AUC： 0.907 0）， indicating good classification performance and generalization ability. SHAP analysis revealed that creatinine， urea nitrogen， daily cumulative dose and administration frequency of vancomycin， which were key predictors， had a significant impact on the prediction results. CONCLUSIONS RF and TabPFN models demonstrate certain advantages in the classification prediction of vancomycin trough blood concentrations； however， their performance in the low to moderate concentration categories still requires improvement.

Objective: To investigate the current status and influencing factors for cognitive frailty among the elderly in community, so as to provide the evidence for early identification and prevention of cognitive frailty among the elderly. Methods: Residents aged 60 years and above with local household registration from 11 counties (cities, districts) in Zhejiang Province from 2021 to 2023 were selected as study participants using a multistage random sampling method. Demographic information, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were assessed using the Patient Health Questionnaire. Cognitive frailty was evaluated using the FRAIL Scale and the Mini-Mental State Examination. Factors affecting cognitive frailty among the elderly in community were identified using a multivariable logistic regression model. Results: A total of 16 613 individuals were surveyed, including 7 465 males (44.93%) and 9 148 females (55.07%). The average age was (70.97±7.29) years. A total of 784 individuals were detected with depressive symptoms, with a detection rate of 4.72%. A total of 724 individuals were detected with cognitive frailty, with a detection rate of 4.36%. Multivariable logistic regression analysis showed that females (OR=1.419, 95%CI: 1.179-1.708), aged ≥70 years (70-<80 years old, OR=1.869, 95%CI: 1.490-2.345; ≥80 years old, OR=5.017, 95%CI: 3.935-6.398), without a spouse (OR=1.495, 95%CI: 1.234-1.810), sedentary (OR=2.420, 95%CI: 1.829-3.202), chronic diseases (1 type, OR=1.456, 95%CI: 1.175-1.804; ≥2 types, OR=1.639, 95%CI: 1.314-2.045), and depressive symptoms (OR=4.191, 95%CI: 3.361-5.225) were associated with a higher risk of cognitive frailty among the elderly in community. Conversely, a lower risk of cognitive frailty was seen among the elderly in community who had primary school or above (primary school, OR=0.512, 95%CI: 0.389-0.676; junior high school or above, OR=0.464, 95%CI: 0.354-0.608), engaged in physical exercise (OR=0.396, 95%CI: 0.291-0.539), and were reported average or good self-rated health status (average, OR=0.641, 95%CI: 0.475-0.866; good, OR=0.150, 95%CI: 0.109-0.208). Conclusions The detection rate of cognitive frailty among the elderly in community is relatively low and is influenced by demographic factors such as gender, age, education level, as well as lifestyle like sedentary and physical exercise, and health status. It is recommended to reduce the risk of cognitive frailty among the elderly through multidimensional interventions, including health education, promotion of healthy lifestyles, and enhanced mental health support.

Antibody-drug conjugates(ADCs), as an emerging therapy for cancer treatment, have made significant progress in the past few decades. However, due to the heterogeneity of ADCs, they still face various issues and challenges in clinical therapy. Therefore, site-specific conjugation techniques have become a crucial area of research in ADCs, and in recent years, this field has witnessed numerous breakthrough advancements, empowering ADCs with enhanced performance. The review provides a comprehensive overview of the frontiers in site-specific conjugation technologies for ADCs. Categorized into seven major classes including lysine-based, cysteine-based, low-abundance amino acid-based and glycosylation site-based conjugation techniques, ribosomal incorporation of unnatural and noncanonical amino acids and enzyme-mediated conjugation techniques, it meticulously describes 21 classical and emerging techniques such as the THIOMAB technology and linchpin-directed modification, in order to offer valuable insights for the development of next-generation ADCs.

Objective:To explore the current status of work-family behavioral role conflict among Operating Room nurses from the resource perspective and analyze its influencing factors using Logistic regression and decision tree models.Methods:A convenience sampling method was used to survey 1 231 Operating Room nurses from 20 hospitals in Henan Province from September to November 2023, utilizing a general information questionnaire, Survey of Nurse Perceived Organizational Support (SNPOS), Family APGAR Index (APGAR), and Work-Family Behavioral Role Conflict Scale (WFBRCS). Univariate analysis, Logistic regression, and decision tree model analyses were applied to identify factors affecting work-family behavioral role conflict among the Operating Room nurses.Results:A total of 1 231 questionnaires were retrieved, and 1 182 were validly questionnaires, resulting in a retrieving rate of 96.02%. Both models identified gender, having children, hospital type, organizational support perception, and family care as influencing factors of work-family behavioral role conflict among the Operating Room nurses ( P<0.05). The areas under the curve ( AUC) for the receiver operating characteristic curves of the Logistic regression and decision tree models were 0.782 and 0.735, respectively, with sensitivities of 76.1% and 65.9%, and specificities of 67.2% and 74.1%, respectively. Conclusions:The work-family behavioral role conflict among Operating Room nurses is at a moderate level and influenced by multiple factors. Both Logistic regression and decision tree models have predictive value for classification, with the Logistic regression model showing higher sensitivity and the decision tree model showing higher specificity. The complementary use of both models has more clinical significance.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Metabolic reprogramming is a hallmark of cancer, including lung cancer. However, the exact underlying mechanism and therapeutic potential are largely unknown. Here we report that protein arginine methyltransferase 6 (PRMT6) is highly expressed in lung cancer and is required for cell metabolism, tumorigenicity, and cisplatin response of lung cancer. PRMT6 regulated the oxidative pentose phosphate pathway (PPP) flux and glycolysis pathway in human lung cancer by increasing the activity of 6-phospho-gluconate dehydrogenase (6PGD) and α-enolase (ENO1). Furthermore, PRMT6 methylated R324 of 6PGD to enhancing its activity; while methylation at R9 and R372 of ENO1 promotes formation of active ENO1 dimers and 2-phosphoglycerate (2-PG) binding to ENO1, respectively. Lastly, targeting PRMT6 blocked the oxidative PPP flux, glycolysis pathway, and tumor growth, as well as enhanced the anti-tumor effects of cisplatin in lung cancer. Together, this study demonstrates that PRMT6 acts as a post-translational modification (PTM) regulator of glucose metabolism, which leads to the pathogenesis of lung cancer. It was proven that the PRMT6-6PGD/ENO1 regulatory axis is an important determinant of carcinogenesis and may become a promising cancer therapeutic strategy.

Objective: To examine the effect of dietary behaviors on handgrip strength loss among the elderly, so as to provide insights into the prevention of handgrip strength loss. Methods : Based on the health surveillance cohort among the elderly in Zhejiang Province, two villages or communities were randomly sampled from each of Shaoxing and Zhoushan cities using a multi-stage cluster sampling method, and all residents that had lived in local areas for one year and longer and had an age of 60 years and older were enrolled. Participants' demographics, dietary behaviors, smoking, drinking, and exercise were collected through questionnaire surveys, and the height, body weight and handgrip strength were measured. The handgrip strength loss was diagnosed according the 2019 Consensus Update on Sarcopenia Diagnosis and Treatment proposed by Asian Working Group for Sarcopenia, and the effect of dietary behaviors on handgrip strength loss was examined using a multivariable logistic regression model. Results: A total of 1 265 residents were enrolled, with a mean age of (70.67±7.30) years, and including 565 men (44.66%) and 700 women (55.34%). The overall prevalence of handgrip strength loss was 42.85% among the participants, and the prevalence was 40.35% in men and 44.86% in women, respectively. Multivariable logistic regression analysis showed that nut intake for 1 to 3 times a week (OR=0.180, 95%CI: 0.088-0.367) and for 4 to 6 times a week (OR=0.241, 95%CI: 0.113-0.514) led to a reduced risk of handgrip strength loss among the elderly, and intake of sugary drinks for 4 to 6 times a week led to an increased risk of handgrip strength loss among the elderly (OR=2.298, 95%CI: 1.120-4.714) after adjustment for age, body mass index, educational level and exercise. Conclusion Intake of nuts and sugary drinks may affect the development of handgrip strength loss among the elderly.

Objective:To explore the value of clinical data and MRI image features in predicting and analyzing the degree of differentiation of hepatocellular carcinoma (HCC).Methods:The clinical and imaging data of 180 patients with surgical outcomes of HCC from March 2015 to June 2019 in the General Hospital of Ningxia Medical University were retrospectively analyzed. Alpha-fetoprotein (AFP)、aspartate aminotransferase (AST)、D-dimer、clinical stage、tumor length、apparent diffusion coefficient(ADC)、enhancement types and so on the clinical and imaging data of the poorly differentiated and non-differentiated HCC were compared and analyzed. Multivariate logistic regression was used to predict independent risk factors for poorly differentiated HCC.Results:Of the 180 HCC patients, 121 were moderately and highly differentiated, and 59 were poorly differentiated. Univariate analysis showed that the patient’s age, gender, AFP, AST, D-dimer level, clinical stage, Child-Pugh score, tumor length, whether the capsule was complete, tumor apparent diffusion coefficient, the maximum level ADC value, enhancement type with HCC differentiation degree were correlated(all P<0.05). Multivariate logistic regression analysis showed that the patients' gender ( OR=4.524, P<0.05), clinical stage ( OR=5.598, P<0.05), D-dimer ( OR=8.576, P<0.05), HCC diameter ( OR=0.498, P<0.05), enhancement types ( OR=2.988, P<0.05), tumour ADC value ( OR=0.059, P<0.05) were independent of poorly differentiated HCC risk factor. Conclusion:MRI image features can be used as an effective indicator to predict the degree of HCC differentiation before surgery. It is more valuable to accurately predict the degree of HCC combined with D-dimer and AFP value.