Objective: To explore the association between sleep quality and dry eye symptoms in adolescents,so as to provide the evidence for reducing the prevalence of dry eye symptoms. Methods: The study population was adolescents aged 12-24 years from the Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, which was conducted from 20 June to 31 August 2022. A stratified random sampling and quota sampling method was used to select 6 456 adolescents within mainland China. The Ocular Surface Disease Index (OSDI) and Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) were used to assess dry eye symptoms and sleep quality. Multiple Logistic regression model was used to explore the relationship between sleep quality and dry eye symptoms in adolescents. The influence of gender on the association was explored by using interaction terms. Results: A total of 2 815 adolescents reported having dry eye symptoms, with a prevalence of 43.6%. Logistic regression analysis results showed an increased risk of exacerbation of dry eye symptoms in adolescents with poor sleep quality. The OR (95% CI ) for mild, moderate, and severe dry eye symptoms groups were 1.39(1.16-1.67), 1.52(1.28-1.81), and 2.35(2.02-2.72), respectively, compared with the ocularly normal group ( P <0.05). There was a significant interaction between sleep quality and gender on dry eye symptoms in adolescents ( P <0.01). Conclusions Sleep quality is associated with dry eye symptoms in adolescents, and those with poor sleep quality have a higher risk of dry eye symptoms. The effect of sleep quality on dry eye symptoms is greater in boys.

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.

ObjectiveTo investigate the contamination status of Staphylococcus aureus in food and the presence of enterotoxin genes in Jiading District， Shanghai， and to provide a basis for the prevention and treatment of foodborne Staphylococcus aureus disease. MethodsFrom 2021 to 2023， 15 types of food were sampled for S. aureus testing， and the presence of five enterotoxin genes， including sea⁃see， was tested in the strains. ResultsOut of 705 food samples， 88 （12.48%） were positive for S. aureus. S. aureus was detected in 12 of the 15 food types， with the three food types with the highest positive rates being cold noodles （45.00%）， raw poultry （26.25%）， and vegetable salads （20.00%）. The enterotoxin gene carriage rate was 32.95% in food strains. The carriage rates for sea， seb， and sec were 7.95%， 12.50%， and 14.77%， respectively. Neither sed nor see was detected. The detection rate of strains carrying two types of enterotoxin genes was 2.27%. The enterotoxin carriage rates in strains from vegetables， beverages， and raw meat were 57.14%， 40.00%， and 30.00%， respectively. ConclusionThe S. aureus detection rate in food in Jiading District is much higher than the national average. The enterotoxin gene carriage rates are high， with food strains carrying sea， seb， and sec， with sec being the most prevalent. There is a need to enhance monitoring of S. aureus and enterotoxins， especially in high-risk foods such as noodles， vegetables， and non-packaged beverages.

Objectives:To evaluate the valvular and cardiac function,cardiac reverse remodeling at 6-month after transcatheter edge-to-edge repair(TEER)for patients with functional and degenerative mitral valve regurgitation,and summarize the experience of echocardiography application. Methods:The clinical data of 93 patients with moderate to severe mitral regurgitation(MR)treated with TEER and completed 6-month follow-up in Yunnan Fuwai Cardiovascular Hospital from July 2022 to February 2023 were retrospectively analyzed.Patients were divided into functional mitral regurgitation(FMR)and degenerative mitral regurgitation(DMR)groups according to MR etiology.The valve characteristic parameters,as well as valvular function,chamber volume and cardiac functional parameters before and at 6 months after operation were compared.The key points of echocardiography application were summarized. Results:Among all patients,71 were FMR and 22 were DMR.There were differences in valve structure between the two groups.Mitral TEER were successfully accomplished and all patients completed 6-month follow-up.The key points of echocardiography application included:valve structure analysis,atrial septal puncture location,device delivery process monitoring and image optimization during clamping process.The mitral regurgitation grade and NYHA grade were significantly improved in all patients at 6 months after TEER(P<0.05),and the mean mitral valve pressure gradient was higher than that before operation(P<0.05).Left ventricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV)and left atrial volume index in FMR group were significantly decreased(P<0.05),while left ventricular and left atrial volume in DMR group remained unchanged(P>0.05).There were no significant changes in left ventricular ejection fraction and left ventricular global strain in both groups during the observation period(P>0.05).The changes of LVEDV and LVESV before and after operation were more significant in FMR group than those in DMR group(P<0.05). Conclusions:Mitral TEER can reduce the degree of regurgitation and improve cardiac function in the early postoperative period for moderate and severe MR patients with different etiologies.There are differences in preoperative valve structure and postoperative cardiac reverse remodeling between FMR and DMR patients.Echocardiography is an important imaging technique for the evaluation and monitoring process before,during and post mitral TEER.

Objective To prepare mouse monoclonal antibodies against the ectodomain of E2 (E2ecto) glycoprotein of Western equine encephalitis virus (WEEV). Methods A prokaryotic expression plasmid pET-28a-WEEV E2ecto was constructed and transformed into BL21 (DE3) competent cells. E2ecto protein was expressed by IPTG induction and presented mainly as inclusion bodies. Then the purified E2ecto protein was prepared by denaturation, renaturation and ultrafiltration. BALB/c mice were immunized with the formulated E2ecto protein using QuickAntibody-Mouse5W as an adjuvant via intramuscular route, boosted once at an interval of 21 days. At 35 days post-immunization, mice with antibody titer above 1×10⁴ were inoculated with E2ecto intraperitoneally, and spleen cells were fused with SP2/0 cells three days later. Hybridoma cells secreting specific monoclonal antibodies were screened by the limited dilution method, and ascites were prepared after intraperitoneal inoculation of hybridoma cells. The subtypes and titers of the antibodies in ascites were assayed by ELISA. The biological activity of the mAb was identified by immunofluorescence assay(IFA) on BHK-21 cells which were transfected with eukaryotic expression plasmid pCAGGS-WEEV-CE3E2E1. The specificity of the antibodies were evaluated with E2ecto proteins from EEEV and VEEV. Results Purified WEEV E2ecto protein was successfully expressed and obtained. Four monoclonal antibodies, 3G6G10, 3D7G2, 3B9E8 and 3D5B7, were prepared, and their subtypes were IgG2c(κ), IgM(κ), IgM(κ) and IgG1(κ), respectively. The titers of ascites antibodies 3G6G10, 3B9E8 and 3D7G2 were 10⁵, and 3D5B7 reached 10⁷. None of the four antibody strains cross-reacted with other encephalitis alphavirus such as VEEV and EEEV. Conclusion Four strains of mouse mAb specifically binding WEEV E2ecto are successfully prepared.
Horses ; Animals ; Mice ; Encephalitis Virus, Western Equine ; Ascites ; Immunosuppressive Agents ; Antibodies, Monoclonal ; Immunoglobulin M

Objective To assess the efficacy and safety of ascending aorta banding technique combined with typeⅠhybrid aortic arch repair for the aortic arch diseases. Methods The clinical data of patients undergoing ascending aorta banding technique combined with type Ⅰ hybrid arch repair for aortic arch diseases from March 2019 to March 2022 in Beijing Anzhen Hospital were retrospectively analyzed. The technical success, perioperative complications and follow-up results were evaluated. Results A total of 44 patients were collected, including 35 males and 9 females, with a median age of 63.0 (57.5, 64.6) years. The average EuroSCORE Ⅱ score was 8.4%±0.7%. The technical success rate was 100.0%. All patients did not have retrograde type A aortic dissection and endoleaks. One patient died of multiple organ failure 5 days after operation, the in-hospital mortality rate was 2.3%, and the remaining 43 patients survived and were discharged from hospital. The median follow-up period was 14.5 (6-42) months with a follow-up rate of 100.0%. One patient with spinal cord injury died 2 years after hospital discharge. One patient underwent thoracic endovascular aortic repair at postoperative 3 months due to new entry tears near to the distal end of the stent. Conclusion Ascending aorta banding combined with typeⅠhybrid arch repair for the aortic arch diseases does not need cardio-pulmonary bypass. Ascending aorta banding technique strengthens the proximal anchoring area of the stent to avoid risks such as retrograde type A dissection, endoleak and migration. The operation owns small trauma, rapid recovery, low mortality and a low rate of reintervention, which may be considered as a safe and effective choice in the treatment of the elderly, high-risk patients with complex complications.