The objective of this study is to assess the current status of vaccination against respiratory disease among the elderly aged ≥60 and analyze the factors influencing vaccination rates at both service provider and recipient levels in Zhejiang Province. Using a stratified random sampling method, a questionnaire survey was conducted from September 2022 to January 2023 among elderly people aged ≥60 in 30 townships/streets in Zhejiang Province, as well as immunization planning staff at the provincial, municipal, county/district, and township/street levels. Logistic regression models were used to analyze the factors related to vaccination among elderly people in Zhejiang Province. Based on the Zhejiang Provincial Comprehensive Management Information System for Vaccine and Vaccination, the systematic coverage rates of influenza vaccine and pneumonia vaccine for the elderly were 21.76% and 4.57%, respectively. Multivariate logistic regression analysis indicated that advanced age ( OR=1.74, 95% CI: 1.51-1.99), knowing that influenza is more severe than the common cold ( OR=1.67, 95% CI: 1.37-2.04) and having heard of the influenza vaccine ( OR=9.78, 95% CI: 7.03-13.59) were motivating factors for elderly to receive influenza vaccines. Advanced age ( OR=1.71, 95% CI: 1.43-2.06), knowing the serious consequences of pneumonia in the elderly ( OR=1.93, 95% CI: 1.47-2.55) and knowing that pneumonia vaccines can prevent pneumonia ( OR=6.36, 95% CI: 4.84-8.36) were motivating factors for elderly to receive pneumonia vaccines. Zhejiang Immunization Program staff believed that the main reasons why the elderly aged ≥60 would not be vaccinated against influenza or pneumonia were that they felt they would not get sick (55.52% and 56.35% respectively), it would not be serious if get sick (47.73% and 37.46% respectively), lacking trust in vaccine efficacy and safety (38.31% and 43.69% respectively). Vaccination rates for influenza and pneumonia vaccines among the elderly aged ≥60 in Zhejiang Province are suboptimal. Advanced age, awareness of the severity of respiratory diseases and awareness of vaccines against such diseases are related factors for elderly individuals to receive influenza and pneumonia vaccines.

The objective of this study is to assess the current status of vaccination against respiratory disease among the elderly aged ≥60 and analyze the factors influencing vaccination rates at both service provider and recipient levels in Zhejiang Province. Using a stratified random sampling method, a questionnaire survey was conducted from September 2022 to January 2023 among elderly people aged ≥60 in 30 townships/streets in Zhejiang Province, as well as immunization planning staff at the provincial, municipal, county/district, and township/street levels. Logistic regression models were used to analyze the factors related to vaccination among elderly people in Zhejiang Province. Based on the Zhejiang Provincial Comprehensive Management Information System for Vaccine and Vaccination, the systematic coverage rates of influenza vaccine and pneumonia vaccine for the elderly were 21.76% and 4.57%, respectively. Multivariate logistic regression analysis indicated that advanced age ( OR=1.74, 95% CI: 1.51-1.99), knowing that influenza is more severe than the common cold ( OR=1.67, 95% CI: 1.37-2.04) and having heard of the influenza vaccine ( OR=9.78, 95% CI: 7.03-13.59) were motivating factors for elderly to receive influenza vaccines. Advanced age ( OR=1.71, 95% CI: 1.43-2.06), knowing the serious consequences of pneumonia in the elderly ( OR=1.93, 95% CI: 1.47-2.55) and knowing that pneumonia vaccines can prevent pneumonia ( OR=6.36, 95% CI: 4.84-8.36) were motivating factors for elderly to receive pneumonia vaccines. Zhejiang Immunization Program staff believed that the main reasons why the elderly aged ≥60 would not be vaccinated against influenza or pneumonia were that they felt they would not get sick (55.52% and 56.35% respectively), it would not be serious if get sick (47.73% and 37.46% respectively), lacking trust in vaccine efficacy and safety (38.31% and 43.69% respectively). Vaccination rates for influenza and pneumonia vaccines among the elderly aged ≥60 in Zhejiang Province are suboptimal. Advanced age, awareness of the severity of respiratory diseases and awareness of vaccines against such diseases are related factors for elderly individuals to receive influenza and pneumonia vaccines.

Objective To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis(AV).Methods In this study,the vaginal secretion samples of 3 769 patients receiving treatment at the Department of Obstetrics and Gynecology,West China Second Hospital,Sichuan University between January 2020 and December 2021 were selected.Using the results of manual microscopy as the control,we developed the linear kernel SVM algorithm,an artificial intelligence(AI)automated analysis software,with Python Scikit-learn script.The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes(PBC).The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates.The receiver operating characteristic(ROC)curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control.Then,the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established.Results A total of 3 769 vaginal secretion samples were collected.The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels.We selected 1.5 μm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates.The automated identification parameter of Lactobacillus was the ratio of bacteria≥1.5 μm to those＜1.5 μm.The cut-off scores were 2.5 and 0.5,In the parameter of white blood cells(WBC),the cut-off value of the absolute number of WBC was 103 μL-1 and the cut-off value of WBC-to-epithelial cell ratio was 10.The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1%and 15%.The parameter of background flora was bacteria＜1.5 μm and the cut-off values were 5×103 μL-1 and 3×104 μL-1.The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1%and 10%.The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%.Out of 200 samples,automated microscopy and manual microscopy produced consistent scores for 185 samples,while the results for 15 samples were inconsistent.Conclusion We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV.There was good overall concordance between automated microscopy and manual microscopy.The AI identification software for AV can complete clinical lab examination with rather high objectivity,sensitivity,and efficiency,markedly reducing the workload of manual microscopy.

Objective:To evaluate the dose-response relationship of alfentanil inhibiting gag reflex when combined with propofol in elderly patients undergoing painless gastroscopy.Methods:Patients of American Society of Anesthesiologists physical statusⅠor Ⅱ, aged ≥60 yr, scheduled for elective painless gastroscopy, were selected. Propofol 1.5 mg/kg combined with alfentanil was given intravenously in all the patients. The dose of alfentanil was determined by the Dixon up-and-down method. The initial dose of alfentanil was set at 5 μg/kg. The dose of alfentanil in the next patient was determined according to the development of gag reflex, and the ratio between the two successive doses was 1.1. The median effective dose (ED 50) and 95% confidence interval of alfentanil-induced inhibition of gag reflex when combined with propofol in elderly patients undergoing painless gastroscopy were calculated using the by up-and-down sequential allocation. Results:The ED 50 (95% confidence interval) of alfentanil-induced inhibition of gag reflex when combined with propofol 1.5 mg/kg was 2.8 (2.4-3.2) μg/kg in elderly patients undergoing painless gastroscopy. Conclusion:When combined with propofol 1.5 mg/kg, the ED 50 of alfentanil inhibiting gag reflex is 2.8 μg/kg in elderly patients undergoing painless gastroscopy.