Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Objective To study the effect and duration of point-of-use filters on the improvement of endoscopic fi-nal rinse water quality.Methods The final rinse water end at the gastroscope manual cleaning workstation in the Endoscopy Centre of the First Affiliated Hospital with Nanjing Medical University was selected to install a tap ter-minal filter;five specimens of final rinse water were collected consecutively before the installation,immediately after the installation,and 1-11 weeks after the installation.At each sampling time,the staff responsible for clea-ning and disinfecting were asked whether the flow rate of discharged water could satisfy the working demand;the final rinse water was inoculated on R2A culture medium with membrane filter method,bacterial colony forming unit(CFU)was calculated after 30℃ incubation for 5 days.Results The qualified rates of endoscopic final rinse water before point-of-use filter installation was 0,immediately after and 1-9 weeks after installation were both 100％,10 and 11 weeks after installation were 80.0％and 20.0％,respectively.The mean CFU of endoscopic final rinse wa-ter before point-of-use filter installation was 102 CFU/100 mL,immediately after and 1-9 weeks after installation were both ≤2 CFU/100 mL,10 and 11 weeks after installation were 8 and 18 CFU/100 mL,respectively.The feedback from the cleaning and disinfection staff before installation,immediately after installation,and 1-11 weeks after installation indicated that the flow rate of discharged water gradually slowed down over time,but could still meet the work requirements.Conclusion The point-of-use filter can quickly and effectively improve the quality of endoscopic final rinse water,with use duration of up to 9 weeks after installation;Its biggest advantage is that it can serve as the final barrier to all integrated measures,playing a supplementary role in case of any problems occu-rring in the front-end process,and ensuring the microbial quality of the final rinse water to the greatest extent possible.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

Plant-derived bioactive peptides have become a research hotspot in the fields of food and medicine due to their high source safety,easy absorption and utilization by the human body,and potential edible and medicinal value.Bioactive peptides can be classified into antihypertensive,hypoglycemic,hypolipidemic,anticancer,antioxidant,antimicrobial,and anti-inflammatory peptides according to their functions.Among these,antihypertensive,hypoglycemic,and hypolipidemic peptides are collectively referred to as cardiovascular active peptides,which can be used for the treatment and prevention of cardiovascular diseases and have an important role in the development of modem biomedicine.This review focused on the preparation methods,separation,purification,and identification techniques of bioactive peptides,as well as their mechanisms of action and applications in regulating cardiovascular diseases,aiming to provide a reference for further development and application of plant-derived cardiovascular peptides.

Objective To explore the clinical application value of multimodal radiomics in differentiating parotid pleomorphic adenoma(PA)from adenolymphoma(AL).Methods The clinical and imaging data of 68 cases of PA and 52 cases of AL were retrospectively analyzed.All patients underwent ultrasound examination,enhanced CT scan and enhanced MRI scan of the neck before the operation.All patients were randomly divided into training group(n=84)and validation group(n=36)according to the ratio of 7∶3.The 3D Slicer software was used to manually draw the lesion area of the preoperative images and perform radiomics feature extraction.The best feature subset was selected to establish the radiomics model,and the diagnostic efficacy of different models was evaluated by the receiver operating characteristic(ROC)curve.Results The study found that age,gender,and smoking history were effective in differentiating parotid PA from AL,and were used to construct a clinical diagnostic model.Eighteen features were selected through dimensionality reduction to establish the radiomics model.A multimodal combined diagnostic model was then constructed by integrating the radiomics model with gender,age,and smoking history.Compared to the clinical model and the radiomics model,the multimodal combined diagnostic model demonstrated the highest area under the curve(AUC)for distinguishing PA from AL in both the training group and the validation group.Conclusion The combination of radiomics based on neck ultrasound,CT,and MRI with clinical characteristics shows significant clinical application value in the preoperative differentiation of PA and AL.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

Objective:To analyze the association of sedentary time and physical activity with the risk of developing chronic obstructive pulmonary disease (COPD) in non-smoking women aged 40 years and above in Songjiang District, Shanghai.Methods:Based on a natural population-based cohort in Songjiang, a total of 18 707 non-smoking women who were aged 40 years and above and without COPD at baseline survey were enrolled in the study. Cox proportional risk regression model was used to analyze the associations of the duration of sedentary behavior, physical activity with the risk for COPD at baseline survey, and the hazard ratio ( HR) of risk for COPD and its 95% CI were calculated. Stratified analyses were performed based on age, BMI, history of respiratory diseases and so on. Sensitivity analyses were conducted by excluding the cases diagnosed with COPD within one year after the baseline survey. Results:As of March 31, 2024, a total of 691 new COPD cases had been recorded after a median follow-up time of 6.96 years with an incidence density of 53.22 per 10 000 person-years. After adjusting for relevant confounders, in the tertile subgroups of sedentary time, the risk for COPD reduced by 17% in the short sedentary time group compared with the long sedentary time group ( HR=0.83,95% CI:0.70-0.99). Compared with the low physical activity level and long sedentary time group, the risk for COPD reduced by 24% in the high physical activity level and short sedentary time group ( HR=0.76, 95% CI: 0.61-0.95) and by 23% in the low physical activity level and short sedentary time group ( HR=0.77, 95% CI: 0.60-0.97). Compared with the non-physical exercise and long sedentary time group, the risk for COPD reduced by 28% in the non-physical exercise and short sedentary time group ( HR=0.72, 95% CI: 0.60-0.87). These associations remained when the cases diagnosed with COPD within one year of the baseline survey were excluded. Conclusions:Increasing physical activity and reducing sedentary time have beneficial effects to prevent COPD in non-smoking women, and reducing sedentary time alone may also reduce the risk for COPD if increasing physical exercise or other physical activity is not possible.

Plant-derived bioactive peptides have become a research hotspot in the fields of food and medicine due to their high source safety,easy absorption and utilization by the human body,and potential edible and medicinal value.Bioactive peptides can be classified into antihypertensive,hypoglycemic,hypolipidemic,anticancer,antioxidant,antimicrobial,and anti-inflammatory peptides according to their functions.Among these,antihypertensive,hypoglycemic,and hypolipidemic peptides are collectively referred to as cardiovascular active peptides,which can be used for the treatment and prevention of cardiovascular diseases and have an important role in the development of modem biomedicine.This review focused on the preparation methods,separation,purification,and identification techniques of bioactive peptides,as well as their mechanisms of action and applications in regulating cardiovascular diseases,aiming to provide a reference for further development and application of plant-derived cardiovascular peptides.

Objective To explore the clinical application value of multimodal radiomics in differentiating parotid pleomorphic adenoma(PA)from adenolymphoma(AL).Methods The clinical and imaging data of 68 cases of PA and 52 cases of AL were retrospectively analyzed.All patients underwent ultrasound examination,enhanced CT scan and enhanced MRI scan of the neck before the operation.All patients were randomly divided into training group(n=84)and validation group(n=36)according to the ratio of 7∶3.The 3D Slicer software was used to manually draw the lesion area of the preoperative images and perform radiomics feature extraction.The best feature subset was selected to establish the radiomics model,and the diagnostic efficacy of different models was evaluated by the receiver operating characteristic(ROC)curve.Results The study found that age,gender,and smoking history were effective in differentiating parotid PA from AL,and were used to construct a clinical diagnostic model.Eighteen features were selected through dimensionality reduction to establish the radiomics model.A multimodal combined diagnostic model was then constructed by integrating the radiomics model with gender,age,and smoking history.Compared to the clinical model and the radiomics model,the multimodal combined diagnostic model demonstrated the highest area under the curve(AUC)for distinguishing PA from AL in both the training group and the validation group.Conclusion The combination of radiomics based on neck ultrasound,CT,and MRI with clinical characteristics shows significant clinical application value in the preoperative differentiation of PA and AL.

Hypoglycemic drugs are an important means of treatment for patients with type 2 diabetes.However,poor,ineffective and even adverse drug reactions often occur in clinical treatment.With the develop-ment of pharmacogenomics,the link between individual genetic variation and drug dose and treatment re-sponse is becoming clearer,resulting in better efficacy and fewer adverse reactions for patients.This article fo-cuses on hypoglycemic drugs and reviews the latest advances in pharmacogenomics,so as to provide references for individualized treatment of type 2 diabetes and prevention of corresponding complications until the realiza-tion of precision medicine