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 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.

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 establish a multi-state Markov model of type 2 diabetes mellitus (T2DM) patients and explore the transition rule between the cumulative number of different chronic complications, estimate the transition probability and intensity between status, and explore the possible factors affecting the transition between status.Methods:A retrospective cohort study of 33 575 patients with T2DM was conducted. According to the baseline and the cumulative number of chronic complications during the follow-up period, the patients were classified based on five status: T2DM, one complication, two complications, three complications, four and above complication, indicated by S0, S1, S2, S3 and S4, respectively. A time-continuous and state-discrete multi-state irreversible Markov model was used for statistical analysis.Results:The study included 33 575 T2DM patients, and their average age was 60 years old, the median of follow-up length was 8 years. In these patients, 32 653 had no baseline complications. At the end of follow-up, the transition probabilities of S0→S1, S1→S2, S2→S3 and S3→S4 were 16.4%, 32.4%, 45.6% and 25.9%, respectively. The results of multivariate analysis showed that being female ( HR=0.919), less than 60 years old ( HR=0.929), higher fasting plasma glucose ( HR=1.601), lower high-density lipoprotein ( HR=1.087), higher total cholesterol ( HR=1.090),weekly exercise ( HR=0.897), vegetarian diet ( HR=0.852) and heavy diet ( HR=1.887) were the risk factors for S0 to S1. And being female ( HR=0.768), less than 60 years old ( HR=0.859) and lower high-density lipoprotein ( HR=1.160) were the risk factors for S1 to S2. Conclusions:The probability of multiple complications in T2DM patients increased over time, the transition intensity of S2→S3 was largest, followed by S1→S2. Therefore, we need to conduct both early and long-term indicators monitoring and disease prevention, strengthen the health education to improve patients' daily living habits at early stage of the illness, encourage patients to have moderate exercise and balanced diet, strengthen the monitoring of fasting blood- glucose, cholesterol and high-density lipoprotein levels to prevent the deterioration of the illness.

With the rapid development of Internet technology and the continuous advancement of medical informatization, big data in healthcare has gradually become an important resource to innovate health management and meet the growing health needs of people and the application of big data in healthcare has been one of the indispensable parts of national big data strategy in China. Based on the established healthcare big data platform and the application of big data technology, Yinzhou district has made innovative efforts to explore a new model driven by big data for the prevention and control of communicable and non-communicable diseases and the management of vaccination programs. It is expected that the "Internet plus healthcare" model will strengthen the disease prevention and control and public health management in local area, create a new business form and provide strong support for Healthy China 2030. This article introduces this new model driven by big data in Yinzhou and discusses the preliminary efficiency of this model in public health practice.

OBJECTIVE: To explore strategies of prenatal genetic testing for fetuses featuring abnormal skeletal development. METHODS: Clinical data of 17 fetuses with skeletal dysplasia was collected. The results of genetic testing and outcome of pregnancy were analyzed. RESULTS: For 12 fetuses, the femur-to-foot length ratio was less than 0.9. Thirteen fetuses had a positive finding by genetic testing. One fetus was diagnosed with chromosomal aneuploidy, three were diagnosed with microdeletion/microduplications, and nine were diagnosed with hereditary bone diseases due to pathological variants of FGFR3, COL1A2, GPX4 or ALPL genes. CONCLUSION For fetuses with skeletal dysplasia characterized by short femur, in addition to chromosomal karyotyping and microarray analysis, sequencing of FGFR3 and other bone disease-related genes can improve the diagnostic rate.
Bone Diseases, Developmental/genetics* ; Female ; Fetus/diagnostic imaging* ; Genetic Testing ; Humans ; Karyotyping ; Pregnancy ; Prenatal Diagnosis ; Receptor, Fibroblast Growth Factor, Type 3/genetics* ; Ultrasonography, Prenatal

The combination of preoperative chemoradi-otherapy and surgery has become the standard treatment for locally advanced rectal cancer. Up to 30% of patients received pathologic complete response(pCR) after neoadjuvant therapy, for whom low rates of local recurrence and improved outcome after surgery were achieved. Given that, some authors have recommended local resection for clinical extensive response or non operative "wait and see" policy for clinical complete response(cCR) respectively, in which radical surgery-associated complication and dysfunction can be avoided. Current imaging can provide excellent accuracy in primary staging of rectal cancer, however, when used for restaging, the ability is less satisfactory, especially for pCR prediction, as a result of modification on tumor and surrounding tissue induced by neoadjuvant therapy. The question on how to identify patients with pCR before surgery has received more attention recently. On the basis of pathological findings after surgery, in this article, we review the reliability and predictive ability of current imaging for restaging and pCR after preoperative chemoradiotherapy in rectal cancer.
Chemoradiotherapy ; Humans ; Neoadjuvant Therapy ; Rectal Neoplasms ; pathology ; therapy ; Treatment Outcome