ObjectiveTo characterize the seasonal patterns of influenza in Kunming City, Yunnan Province before, during, and after the COVID-19 pandemic, and provide scientific evidence for optimizing influenza prevention and control strategies. MethodsInfluenza-like illness (ILI) and etiological surveillance data for influenza from the 14^th week of 2010 to the 13^th week of 2024 in Kunming City of Yunnan Province were collected. Harmonic regression models were constructed to analyze the epidemic characteristics and seasonal patterns of influenza before (2010/2011‒2019/2020 influenza seasons), during (2020/2021‒2022/2023 influenza seasons), and after (2023/2024 influenza season) the COVID-19 pandemic. ResultsBefore the COVID-19 pandemic, influenza in Kunming City mainly exhibited an annual cyclic pattern without a significant semi-annual periodicity, peaking from December to February of the next year, with an epidemic duration of 20‒30 weeks. During the pandemic, influenza seasonality shifted, with an increase in semi-annual periodicity and an approximate one month delay in annual peaks. However, after the pandemic, the annual amplitude of influenza increased compared with that before the pandemic, and the epidemic duration extended by about one month. Although the annual peak largely reverted to the pre-pandemic levels, the annual peaks for different influenza subtypes/lineages had not fully recovered. ConclusionInfluenza seasonality in Kunming City underwent substantial alterations following the COVID-19 pandemic and has not yet fully reverted to pre-pandemic levels. Continuous surveillance on different subtypes/lineages of influenza viruses remains essential, and prevention and control strategies should be adjusted and optimized in a timely manner based on current epidemic trends.

Objective:To develop and evaluate a rapid and sensitive point-of-care chemiluminescent assay(POC-CLIA)for β-human chorionic gonadotropin(β-HCG).Methods:POC-CLIA was constructed based on alkaline phosphatase(Alp)-AMPPD lumi-nescence system and magnetic particles(Mps)carrier.Performance of POC-CLIA,including sensitivity,precision,accuracy,linear dilution,specificity,stability,hook effect and clinical application were evaluated.Results:Detection limit of β-HCG was 0.71 mU/ml,linear detection range was 0.710～1.092×104 mU/ml,and was no hook effect up to 1.7×105 mU/ml.Intra and inter batch coefficients of variation were less than 10％,and could be stored stably at 37℃ for 10 days.Accuracy deviation was within±10％,so results were reliable.There was no cross-reactivity between interfering substances and anti-β-HCG antibdies.For detecting β-HCG in 100 clinical serum samples,results were highly correlated with those that were tested by clinical standard methods(R2=0.997 0).Turnaround time for single sample was less than 15 min and throughput could reach 200 T/h.Conclusion:This method is adequate that can be widely used in grassroots communities to help large-scale screening of pregnancy and related diseases.

The uniqueness of the location of large general hospitals in urban areas determines that the contradiction between space constraints and medical expansion has become a problem of management bottleneck.The situation that expert visits are too concentrated on a certain working day morning has been significantly improved by carrying out peak load shifting in the visiting arrangements and visiting units of large general hospitals through the Total Quality Management theory and continuous improvement of work mode,greatly alleviating public transportation congestion around the hospital.Through the internal construction of the quality of outpatient clinics,it can effectively improve the patients'sense of access to health care.

ObjectiveTo explore host factors interacting with influenza virus nucleoprotein （NP） and study their effects on influenza virus replication， as well as the mechanism of gardenia jasminoides iridoid glycoside （IGE） in inhibiting influenza virus. MethodA yeast two-hybrid system was utilized to screen host factors that interacted with influenza virus NP. Heterogeneous nuclear ribonucleoprotein D0 （HNRNPD）， glucosamine-6-phosphate deaminase 1 （GNPDA1）， poly（rC）-binding protein 1 （PCBP1）， and protein inhibitor of activated signal transducer and activator of transcription （STAT） protein 1 （PIAS1） were validated by immunoprecipitation assay. The effects of PIAS1 and HNRNPD on influenza virus replication were compared by a dual luciferase assay， and the effects of IGE on influenza virus replication were examined in the presence of transfected ribonucleoprotein （RNP） and knockdown of PIAS1. ICR mice were randomly divided into a normal group， model group， oseltamivir phosphate group， and high， medium， and low dose IGE groups， with 10 mice in each group. In addition to the normal group， each group was infected with the influenza A virus FM1 strain by nasal drip to establish a viral pneumonia model. The high， medium， and low dose IGE groups were given drugs of 50， 25， and 12.5 mg∙kg^-1 by gavage， and the oseltamivir phosphate group was given the drug of 27.5 mg∙kg^-1 by gavage. Equal amounts of distilled water were instilled in the normal and model groups for four consecutive days. Later， protein expression of PIAS1， NP， phosphorylated （p）-STAT3， STAT3， p-STAT1， and STAT1 were detected in the lung tissue by Western blot. ResultIn yeast two-hybrid assays， 16 potential host targets interacting with influenza virus NP were identified. Immunoprecipitation experiments revealed that HNRNPD and PIAS1 could interact with influenza virus NP. The dual luciferase reporter assays found that both PIAS1 knockdown and overexpression significantly affected IAV RNP activity （P<0.05， P<0.01）， and the effect of HNRNPD on IAV RNP was not significant. Both high and low dose IGE groups reduced influenza virus replication （P<0.05） and reversed the increase in influenza virus replication caused by the knockdown of PIAS1（P<0.05， P<0.01）. The expressions of PIAS1， NP， p-STAT3， p-STAT1， and STAT1 in the lung tissue of infected mice were reduced to different degrees in each IGE group （P<0.05， P<0.01）. ConclusionPIAS1 interacts with influenza virus NP and is able to inhibit influenza virus replication. IGE may exert antiviral effects by inhibiting the activity of IAV RNP through the PIAS1/STAT1 pathway.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

The uniqueness of the location of large general hospitals in urban areas determines that the contradiction between space constraints and medical expansion has become a problem of management bottleneck.The situation that expert visits are too concentrated on a certain working day morning has been significantly improved by carrying out peak load shifting in the visiting arrangements and visiting units of large general hospitals through the Total Quality Management theory and continuous improvement of work mode,greatly alleviating public transportation congestion around the hospital.Through the internal construction of the quality of outpatient clinics,it can effectively improve the patients'sense of access to health care.

The uniqueness of the location of large general hospitals in urban areas determines that the contradiction between space constraints and medical expansion has become a problem of management bottleneck.The situation that expert visits are too concentrated on a certain working day morning has been significantly improved by carrying out peak load shifting in the visiting arrangements and visiting units of large general hospitals through the Total Quality Management theory and continuous improvement of work mode,greatly alleviating public transportation congestion around the hospital.Through the internal construction of the quality of outpatient clinics,it can effectively improve the patients'sense of access to health care.

The uniqueness of the location of large general hospitals in urban areas determines that the contradiction between space constraints and medical expansion has become a problem of management bottleneck.The situation that expert visits are too concentrated on a certain working day morning has been significantly improved by carrying out peak load shifting in the visiting arrangements and visiting units of large general hospitals through the Total Quality Management theory and continuous improvement of work mode,greatly alleviating public transportation congestion around the hospital.Through the internal construction of the quality of outpatient clinics,it can effectively improve the patients'sense of access to health care.