Objective: To investigate the factors influencing parents willingness to vaccinate their children against influenza in Guangzhou, and to provide a scientific basic for effectively improving the coverage rate of influenza vaccine in children. Methods: According to economic level, one secondary school and one elementary school in each of the central administrative and peripheral districts of Guangzhou were selected by stratified cluster sampling. A questionnaire survey was conducted among 5 133 parents of the school students. Questionnaire content included the basic characteristics of children and their parents, and parents knowledge of influenza vaccination for children. Results: A total of 14.57%(748/5 133) of parents were unwilling to have their children vaccinated against influenza. The results of the multivariate Logistic regression analysis found that, compared with parents aged ≤35 years old, parents aged 41-45 years and ≥46 years were 49% (adjusted OR=1.49, 95%CI =1.11-2.00) and 86% (adjusted OR= 1.86 , 95%CI =1.33-2.60), respectively, more likely to refuse vaccinating their children. Parents with an annual income ≥ 200 000 yuan were 52% more likely to be unwilling to vaccinate their children than those with annual income <100 000 yuan (adjusted OR=1.52, 95%CI =1.12-2.06). Parents living within a walking distance ≥30 minutes from the vaccination clinic were 52% more likely to be vaccinereluctant than those living within a walking distance of ≤10 minutes (adjusted OR=1.52, 95%CI = 1.16- 1.99). Compared with parents who regarded the vaccine as safe, parents who did not believe that it was safe or who were unsure of its safety were more likely to refuse vaccinating their children, with adjusted OR(95%CI ) of 12.75(9.44-17.23) and 3.37(2.73- 4.15 ), respectively( P <0.01). Conclusion Parents age, annual income, recognition of the safety of influenza vaccines, and walking distance to the vaccination clinic are associated with parents willingness to vaccinate children against influenza. Hospitals, communities and schools should cooperate to carry out vaccination and popular science propaganda, and arrange vaccination sites rationally to improve the coverage of influenza vaccines.

Objective:To investigate the clinical value of deep learning model based on contrast enhanced ultrasound (CEUS) video in the differential diagnosis of benign and malignant liver tumors.Methods:Between May 2010 and June 2022, 1 213 patients who underwent CEUS examination for liver masses in the Affiliated Hospital of Southwest Medical University were retrospectively collected, and the enrolled patients were divided into training and independent test cohorts with December 31, 2021 as the time cut-off. In the training cohort, the TimeSformer algorithm was used as the infrastructure, and multiple fixed-time segments were obtained from CEUS arterial videos by using the sliding window of the video, and the classification results of the entire video were obtained after fusing the features of multiple segments, so as to build a deep learning model based on CEUS videos. In the independent test cohort, ROC curves were used to verify the validity of the model and compared with three radiologists with different CEUS experience (R1, R2, and R3, with 3, 6, and 10 years of CEUS experience, respectively).Results:A total of 1 213 patients with liver masses were included in the study, including 1 066 patients in the training cohort (426 cases of malignancy) and 147 patients in the independent test cohort (50 cases of malignancy). The area under curve (AUC)value of deep learning model was 0.93±0.01 in the training cohort and 0.89±0.01 in the independent test cohort, and the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 80.42%, 74.19%, 92.00%, 94.52% and 65.71%, respectively. Among the three radiologists, R1 had the lowest diagnostic performance, with accuracy, sensitivity, specificity, PPV and NPV of 67.83%, 51.61%, 98.00%, 97.96% and 52.13%, respectively, while the above indicators of R3 were 82.52%, 76.36%, 94.00%, 95.95% and 68.12%, respectively. McNemar′s test showed that the difference between R1 and the deep learning model was statistically significant ( P<0.001), while the differences between R2 and R3 and the deep learning model were not statistically significant ( P=0.720, 0.868). In addition, the analysis time of the model for a single case was (340.24±16.32)ms, while the average analysis time of radiologists was 62.9 s. Conclusions:The deep learning model based on CEUS can better identify benign and malignant liver masses, and may reach the diagnostic level of experienced radiologists.

Objective To evaluate the accuracy of bioelectrical impedance analysis(BIA)in measurement of appendicular skeletal muscle mass(ASM)of adults.Methods A total of 836 adults aged 18-42 years were recruited in Guangzhou using a convenient sampling method from April 2021 to September 2022.ASM was measured using BIA and Dual-energy X-ray absorptiometry(DXA).Using DXA as the standard method,the consistency between the BIA and DXA measurements was evaluated by intra-class correlation coefficients(ICCs)and Bland-Altman analysis in logarithmically transformed data,in order to evaluate the accuracy of BIA in ASM measurement.Receiver operating characteristic curve was plotted to evaluate the diagnostic value of BIA for screening low muscle mass.Results A total of 774 individuals were included for analysis finally.ICCs for ASM measured by BIA and DXA were 0.774 and 0.667 in males and females,respectively.Mean ratios(limits of Agreement)of ASM were 0.94(0.80-1.10)and 0.91(0.78-1.05)in males and females,respectively.Area under curve of BIA for screening low muscle mass were 0.91 and 0.94 in males and females,respectively.The optimal cut-off values of Z-score by BIA for males and females were-0.57 and-0.66,respectively.Sensitivity and specificity for males were 82.5%and 86.0%,while being 86.8%and 93.8%,for females.Conclusion BIA shows a moderate consistency with DXA for measuring ASM in adults.Furthermore,BIA yields a good diagnostic value in identifying low muscle mass in adults aged 18-42 years.