Objective:To describe the epidemiological characteristics of 22 common respiratory patho-gens in patients with pneumonia in Yinzhou,Ningbo,from January 1,2015 to December 21,2024.Methods:The test data of 22 common respiratory pathogens in patients diagnosed with pneumonia or lung infection in the Yinzhou Regional Health Information Platform from January 1,2015 to December 21,2024 were collected.The positive cases,positive rates,and positive proportions were calculated.The epidemiological characteristics were described by the year,sex,age group,season,and coronavirus disease 2019(COVID-19)pandemic period.Results:A total of 77 531 pneumonia patients were included,with 492 696 respiratory pathogen tests performed.The number of respiratory pathogen tests and positive cases of pneumonia patients in Yinzhou showed an upward trend.In the study,34.63％of the pneumo-nia patients tested positive for at least one pathogen,and the pathogen non-detection rate decreased from 79.44％in 2015 to 58.38％in 2024.The overall pathogen positive rate was 9.12％,which decreased during the COVID-19 pandemic and had not returned to the historical level after the COVID-19 pande-mic.The positive rate was highest in children aged 6-17 years(13.99％),and lowest in the elderly over 60 years(4.16％).The top 3 highest number of positive cases was Mycoplasma pneumoniae,influenza A virus,and influenza B virus;the top 3 highest positive rates of pathogen tests were Mycoplasma pneu-moniae(25.26％),rhinovirus(12.02％),and Bordetella pertussis(11.66％).The pathogen spectrum proportion in men was similar to that in women,only showing a higher ratio of Mycobacterium tuberculosis and a slightly lower ratio of Mycoplasma pneumoniae(P＜0.001).Mycoplasma pneumoniae,respiratory syncytial virus,and rhinovirus infections were more common in children,while influenza virus,Mycobac-terium tuberculosis,and Streptococcus pyogenes infections were more common in adults and the elderly(P＜0.001).Influenza virus and human metapneumovirus infections were more common in winter,rhi-novirus and Bordetella pertussis infections were more common in spring,and Mycoplasma pneumoniae in-fections were relatively more common in fall(P＜0.001).After the COVID-19 pandemic,the propor-tions of rhinovirus,respiratory syncytial virus,and human metapneumovirus infections in the pneumonia patients increased signi-ficantly,reaching 7.53％,4.26％,and 2.25％,respectively,while the propor-tions of influenza B virus and Mycobacterium tuberculosis infections decreased to 4.14％and 2.80％,re-spectively(P＜0.001).Conclusion:In the past decade,the scale of respiratory pathogen infection in the pneumonia population in Yinzhou had expanded significantly,and there were differences in distribu-tion by the year,gender,age group,and season.The respiratory pathogen spectrum in pneumonia pa-tients after the COVID-19 pandemic had a trend of diversification.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objective:To describe the epidemiological characteristics of 22 common respiratory patho-gens in patients with pneumonia in Yinzhou,Ningbo,from January 1,2015 to December 21,2024.Methods:The test data of 22 common respiratory pathogens in patients diagnosed with pneumonia or lung infection in the Yinzhou Regional Health Information Platform from January 1,2015 to December 21,2024 were collected.The positive cases,positive rates,and positive proportions were calculated.The epidemiological characteristics were described by the year,sex,age group,season,and coronavirus disease 2019(COVID-19)pandemic period.Results:A total of 77 531 pneumonia patients were included,with 492 696 respiratory pathogen tests performed.The number of respiratory pathogen tests and positive cases of pneumonia patients in Yinzhou showed an upward trend.In the study,34.63％of the pneumo-nia patients tested positive for at least one pathogen,and the pathogen non-detection rate decreased from 79.44％in 2015 to 58.38％in 2024.The overall pathogen positive rate was 9.12％,which decreased during the COVID-19 pandemic and had not returned to the historical level after the COVID-19 pande-mic.The positive rate was highest in children aged 6-17 years(13.99％),and lowest in the elderly over 60 years(4.16％).The top 3 highest number of positive cases was Mycoplasma pneumoniae,influenza A virus,and influenza B virus;the top 3 highest positive rates of pathogen tests were Mycoplasma pneu-moniae(25.26％),rhinovirus(12.02％),and Bordetella pertussis(11.66％).The pathogen spectrum proportion in men was similar to that in women,only showing a higher ratio of Mycobacterium tuberculosis and a slightly lower ratio of Mycoplasma pneumoniae(P＜0.001).Mycoplasma pneumoniae,respiratory syncytial virus,and rhinovirus infections were more common in children,while influenza virus,Mycobac-terium tuberculosis,and Streptococcus pyogenes infections were more common in adults and the elderly(P＜0.001).Influenza virus and human metapneumovirus infections were more common in winter,rhi-novirus and Bordetella pertussis infections were more common in spring,and Mycoplasma pneumoniae in-fections were relatively more common in fall(P＜0.001).After the COVID-19 pandemic,the propor-tions of rhinovirus,respiratory syncytial virus,and human metapneumovirus infections in the pneumonia patients increased signi-ficantly,reaching 7.53％,4.26％,and 2.25％,respectively,while the propor-tions of influenza B virus and Mycobacterium tuberculosis infections decreased to 4.14％and 2.80％,re-spectively(P＜0.001).Conclusion:In the past decade,the scale of respiratory pathogen infection in the pneumonia population in Yinzhou had expanded significantly,and there were differences in distribu-tion by the year,gender,age group,and season.The respiratory pathogen spectrum in pneumonia pa-tients after the COVID-19 pandemic had a trend of diversification.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

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.

Objective To assess the association between Metformin use and the risk of dementia in patients with type 2 diabetes mellitus(T2DM).Methods The research data came from the big medical data platform of Yinzhou District,and we constructed a cohort of T2DM patients who had initiated treatment of Metformin or sulfonylurea since January 1,2009.The inverse probability of treatment weighting(IPTW)was used to control the baseline confounding factors,and the Cox regression model was used to estimate the HR(95%CI)of the association between Metformin use and dementia risk.Results The incidence rate of dementia in new users of Metformin(41181 persons)and sulfonylureas(38092 persons)was 128.4 per 100000 person years and 142.3 per 100000 person years respectively.Compared with sulfonylureas,the crude analysis with no adjustment for confounding factors showed that there was a negative association between the use of Metformin and the incidence of dementia,with an HR(95%CI)0.930(0.800～1.090).After adjusting for potential confounders with IPTW,Metformin was not significantly associated with the risk of dementia HR(95%CI)1.040(0.890～1.220).The subgroup analysis results for different baseline characteristics were consistent with the primary analysis results,and there were no statistically significant associations between Metformin and dementia incidence risk in all subgroups.Conclusions There is no significant association between the use of Metformin and the risk of dementia in T2DM patients in the Yinzhou District.

Objective To investigate the effect of IGF1R β subunit mutants sb-IGF1R and ma-IGF1R on the biological behavior of osteosarcoma 143B cells. Methods We designed and constructed sb-IGF1R and ma-IGF1R fragments. They were cloned into adenovirus AdEasy shuttle plasmid, to obtain Ad-sbIGF1R and Ad-maIGF1R. We observed the proliferation, migration and apoptosis of the osteosarcoma cells transfected with Ad-sbIGF1R, Ad-maIGF1R and Ad-IGF1R. The Ad-sbIGF1R, Ad-maIGF1R and Ad-GFP nude mouse models were constructed to evaluate the tumor growth in vitro. Results By plasmid PCR, IGF-1R β subunit mutant was overexpressed in osteosarcoma cells. Ad-sbIGF1R and Ad-maIGF1R significantly inhibited the proliferation and migration of osteosarcoma cells, and promoted cell apoptosis, and inhibited tumor growth in subcutaneous tumor-bearing nude mouse models. Conclusion IGF1R β subunit mutants inhibit the proliferation and migration of osteosarcoma cells and induce cell apoptosis.

Objective:To analyze the clinical phynotypes of fetuses with 22q11.2 microduplications.Method:Eleven fetuses were diagnosed with 22q11.2 microduplications among 2 969 cases who underwent prenatal chromosomal microarray analysis from January 2016 to February 2020. The phenotypes, indications for invasive prenatal diagnosis, genetic results, pregnancy outcomes and postnatal clinical presentation were analyzed.Results:There were 6 cases diagnosed with classic 3.0 Mb microduplication (DiGeorge and velocardiofacial syndromes, DGS/VCFS) in the 22q11.2, 1 case with 1.5 Mb proximal microduplication and 4 cases with distal small segment microduplication (E-H). Out of 11 fetuses with 22q11.2 microduplications,7 cases were inherited, 2 cases was de novo and data were not available for 2 cases. Vicular septal defect and anencephalu were diagnosed by ultrasonography in 2 cases,fetal growth restriction was diagnosed in 2 cases,no any abnormalities were found in remaining 7 cases. Seven cases(3 cases of classic 3.0 Mb microduplication, 1 case of proximal microduplication and 3 cases of distal small segment microduplication) were delivered at full-term;and pregnancy was terminated in 4 cases. Seven infants were followed up after birth, 4 infants were normal, 3 showed abnormal phenotypes.Conclusion:The clinical phenotypes after birth of fetuses with 22q11.2 microduplication are diverse. Prenatal genetic counseling is necessary,so that pregnant women and their families can fully understand the possible clinical phenotypes and make informed choices.