To summarize the clinical characteristics, antibody spectrum and neuroelectrophysiological features of autoimmune nodopathy(AN), and to explore the phenotypic differences among different antibody-positive subgroups.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective To explore the clinical and genetic characteristics of children with holocarboxylase synthetase(HLCS)deficiency.Methods A retrospective analysis was conducted on the clinical data of 3 children with HLCS deficiency who were admitted to Children's Hospital Affiliated to Zhengzhou University from December 2014 to January 2024.Relevant literature indexed in CNKI,Wanfang Data,PubMed and other databases was reviewed to summarize the clinical characteristics and HLCS gene mutations of children with HLCS deficiency.Results All 3 children were male,with onset age of 4-6 months.The main clinical manifestations included shortness of breath,vomiting,diarrhea,and poor mental state,and partial cases were complicated by growth retardation and neurological symptoms.Laboratory tests showed metabolic acidosis in all cases,blood amino acid and acylcarnitine profiles as well as urinary organic acid analysis suggested multiple carboxylase deficiency.Genetic testing revealed compound heterozygous mutation in the HLCS gene of all 3 children,among which the c.1892delT(p.L631X)mutation was previously unreported.According to the guidelines of the American College of Medical Genetics and Genomics(ACMG),the c.1892delT(p.L631X)mutation was rated as pathogenic mutation(PVS1+PM2_supporting+PM3).Biotin supplementation was effective in all cases.Literature review included 27 English literatures and 29 Chinese literatures,reporting a total of 133 children with HLCS deficiency caused by HLCS gene mutation.Common clinical manifestations included metabolic acidosis,skin lesions,vomiting,feeding difficulties,dyspnea,diarrhea,and neurological symptoms,etc.Conclusions Blood amino acid and acylcarnitine profiles,urine organic acid analysis,and gene testing are helpful for the diagnosis of HLCS deficiency.Timely biotin supplementation leads to a good prognosis.The mutation of HLCS gene is considered as the genetic etiology of HLCS deficiency in 3 children,among which the c.1892delT(p.L631X)mutation is a newly discovered mutation.

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.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To evaluate the association between the preoperative aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio and postoperative delirium (POD) in patients undergoing total knee/hip arthroplasty.Methods:In this nested case-control study, medical records from patients, aged ≥55 yr, of American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ, with Mini-Mental State Examination (MMSE) scale score >24 on preoperative day 1, scheduled for elective total knee/hip arthroplasty under combined spinal-epidural anesthesia at Qingdao Municipal Hospital between November 2021 and December 2023, were collected. POD was assessed using the Confusion Assessment Method. Patients were categorized into POD and non-POD groups based on the occurrence of POD. Logistic regression was employed to identify protective and risk factors for POD. The predictive performance of preoperative AST/ALT ratio alone and its combination with cerebrospinal fluid (CSF) biomarker concentrations for POD was evaluated using receiver operating characteristic curves, and the clinical utility of the prediction models was assessed using decision curve analysis. Mediation analysis was conducted to examine the mediation role of CSF biomarkers in the relationship between the preoperative AST/ALT ratio and POD. Restricted cubic splines were used to assess the nonlinear relationship between the preoperative AST/ALT ratio and POD.Results:A total of 460 patients were finally included, with 49 in POD group and 411 in non-POD group. After adjustment for multiple confounding factors including age, sex, educational level, MMSE score, history of hypertension, history of diabetes, history of smoking and history of alcohol use, multivariable logistic regression identified that the higher preoperative AST/ALT ratio, CSF total tau protein (t-tau) and phosphorylated tau protein (p-tau) concentrations were independent risk factors for POD, whereas lower CSF β-amyloid 42 (Aβ 42) concentration, Aβ 42/t-tau ratio and Aβ 42/p-tau ratio served as protective factors ( P<0.05). The area under the receiver operating characteristic curve of the preoperative AST/ALT ratio combined with CSF biomarkers in predicting POD was 0.939 ( P<0.001), demonstrating high clinical efficacy. After adjusting for age, educational level, sex, MMSE score, history of hypertension, history of diabetes, history of smoking, history of alcohol use and body mass index, restricted cubic splines revealed a nonlinear relationship between preoperative AST/ALT levels and the probability of POD, and the probability of POD increased with rising ratios when the preoperative AST/ALT ratio ranged from 1.09 to 1.40 ( Poverall < 0.05, Pnonlinear <0.05). After stratification by sex, the preoperative AST/ALT ratio demonstrated a linear relationship with the probability of POD ( Poverall <0.05, Pnonlinear>0.05). Mediation analysis indicated that the relationship between the preoperative AST/ALT ratio and POD was partially mediated by CSF p-tau concentration (proportion mediated 18.1%), CSF t-tau concentration (proportion mediated 12.0%), and the Aβ 42/t-tau ratio (proportion mediated 15.4%). Conclusions:A higher preoperative AST/ALT ratio is an independent risk factor for POD in total knee/hip arthroplasty patients. The concentrations of CSF t-tau and p-tau and Aβ 42/t-tau ratio have a mediating role in the relationship between the preoperative AST/ALT ratio and POD.