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.

Acute ST-segment elevation myocardial infarction with multivessel disease is one of the high-risk types of coronary heart disease.Early opening of infarct-related artery and reperfusion of myocardium could significantly reduce the mortality in acute phase.However,the presence of non-culprit lesions in non-infarct-related arteries is still at risk and has an important impact on the long-term prognosis of patients.It remains controversial on how to precisely evaluate the clinical significance and revascularization value of non-culprit lesions.This article aims to review the research status and progress of guidance strategies of non-culprit lesion revascularization in patients with ST-segment elevation myocardial infarction and multivessel disease.

The gallium-based liquid metals were introduced in terms of the advantages when applied in medical field,application status in medical imaging,drug delivery,antibiosis and tumor therapy and cutting-edge application in flexible e-skin,wearable sensor and flexible medical device.The deficiencies of the gallium-based liquid metals in durability,potential toxicity,high cost of preparation and difficulty of process control were analyzed when applied in medical fields.The future development directions of the gallium-based liquid metals were pointed out.[Chinese Medical Equipment Journal,2024,45(11):97-102]

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective: To investigate the clinical features and long-term prognostic factors of diabetic patients with low or intermediate complexity coronary artery disease (CAD) post percutaneous coronary intervention (PCI). Methods: This was a prospective, single-centre observational study. Consecutive diabetic patients with SYNTAX score (SS)≤32 undergoing PCI between January and December 2013 in Fuwai hospital were included in this analysis. The patients were divided into two groups based on SS, namely SS≤22 group and SS 23-32 group. Multivariate Cox regression analysis was performed to identify independent factors related to poor 5-year prognosis. The primary outcomes were cardiac death and recurrent myocardial infarction, the secondary outcomes were all cause death and revascularization. Results: Of the 3 899 patients included in the study, 2 888 were men (74.1%); mean age was 59.4±9.8 years. There were 3 450 patients in the SS≤22 group and 449 patients in the SS 23-32 group. Compared with SS≤22 group, the incidence of revascularization was higher in SS 23-32 group (18.9% (85/449) vs. 15.2% (524/3450), log-rank P=0.019). There was no significant difference in all-cause death, cardiac death and recurrent myocardial infarction between the two groups (log-rank P>0.05). Multivariate Cox regression analysis showed that age (HR=1.05, 95%CI 1.02-1.08, P<0.001), chronic obstructive pulmonary disease (HR=3.12, 95%CI 1.37-7.07, P=0.007) and creatinine clearance rate (CCr)<60 ml/min (HR=3.67, 95%CI 2.05-6.58, P<0.001) were independent risk factors for 5-year cardiac death, while left ventricular ejection fraction (HR=0.94, 95%CI 0.91-0.96, P<0.001) was a protective factor. Previous PCI (HR=2.04, 95%CI 1.38-3.00, P<0.001), blood glucose level≥11.1 mmol/L on admission (HR=2.49, 95%CI 1.32-4.70, P=0.005) and CCr<60 ml/min (HR=1.85, 95%CI 1.14-2.99, P=0.012) were independent risk factors for 5-year recurrent myocardial infarction. The SS of 23-32 was independently associated with risk of revascularization (HR=1.54, 95%CI 1.09-2.16, P=0.014), after adjusting for residual SS. Residual SS was not a risk factor for 5-year prognosis. Conclusions: In diabetic patients with low-or intermediate complexity CAD, SS 23-32 is associated with increased risk of 5-year revascularization; the clinical characteristics of the patients are associated with the long-term mortality and recurrent myocardial infarction, but not related to revascularization.
Male ; Humans ; Middle Aged ; Aged ; Female ; Coronary Artery Disease/surgery* ; Stroke Volume ; Percutaneous Coronary Intervention ; Prospective Studies ; Treatment Outcome ; Ventricular Function, Left ; Prognosis ; Risk Factors ; Myocardial Infarction/etiology* ; Diabetes Mellitus

Medicinal plant germplasm resources are the foundation of the modern development of traditional Chinese medicine. In-depth study of medicinal plant germplasm resources is a prerequisite for cultivating fine varieties and ensuring the output and standard quality of traditional Chinese medicine（TCM）. Traditional identification methods start with appearance and are greatly affected by natural environment and human factors，with a low efficiency and accuracy of identification are generally low molecularin general. Due to such advantages as easy operation，high sensitivity，accurate results， molecular biology technology has been widely used in the related research of relevant studies for medicinal plant germplasm resources due to its advantages of easy operation，high sensitivity，accurate results，etc. It mainly involving the distinction between wild and cultivated products，researchstudy on substitutes of TCM，identification of Chinese patent medicine，good variety marker breeding，genetic diversity researchstudy，genetic map establishment and omics research，etcstudy. Among them，omics researchstudy is divided into genomics，transcriptomics，metabolomics，and proteomics due toby different analysis purposes. Genomics is divided into three sub-fields namely structural genomics，functional genomics， and comparative genomics. Eukaryotes Because eukaryotes have nuclei and organelles，so omics researchstudy also includes chloroplast genomics，mitochondrial genomics，nuclear genomics，and plastid genomics. Among them，the chloroplast genome has a simple structure，small molecular weight，and good conservation，while the mitochondrial genome has a strong variability and complex structure，the nuclear genome data isfeatures complex， data and the nucleus contains no ribosomes in nucleus，resulting in spatiotemporal differences in the translation process，even if repeated repeatedly test， the result of and the test is alsoresults remained uncertain， even after repeated tests. The molecular biology technology and omics researchstudy involved in theby current medicinal plant researchstudy still hashave shortcomings，and there iswith a large room for development，which needs and need further improvement and supplementation. This articlepaper successively introduces the characteristics and applications of cytology，molecular markers，and omics researchstudy techniques in the identification of medicinal germplasm resources，providingin order to provide a reference for subsequent identification，development and utilization of medicinal plant germplasm resources.

Objective:To develop the specific molecular markers of Codonopsis plants and better identify their germplasm resources considering the significant difference in active ingredients of Codonopsis Radix from various origins and producing areas. Method:Such bioinformatics software as Primer 5.0， NTSYS-pc 2.10e， and PopGene 32 were used for searching the simple sequence repeat （SSR） markers of C. minima chloroplast genome， C. tsinlingensis chloroplast， and C. lanceolata mitochondrial sequences， and 120 pairs of SSR primers were designed by Primer 5.0. Then 16 pairs of cpSSR primers and 10 pairs of mtSSR primers with good screening effect and high polymorphism were selected for analyzing the interspecific versatility of 20 samples. Result:The results showed that 66 cpSSR primer sites and 26 mtSSR sites were identified from the genome sequences， with 86.20% of single nucleotide， 6.9% of dinucleotide， and 6.9% of trinucleotide for C. minima chloroplast， 83.78% of single nucleotide，13.51% of dinucleotide， and 2.71% of trinucleotide for C. tsinlingensis chloroplast， and 46.15% of single nucleotide and 53.85% of dinucleotide for C. lanceolata mitochondria. As demonstrated by polymerase chain reaction （PCR） identification results， the developed 26 pairs of SSR primers had good applicability in the genus Codonopsis. The analysis by NTSYS-pc 2.10e revealed that the genetic similarity coefficients of 20 samples were within the range of 0.38-1.00， and they were divided into two subgroups at a threshold of 0.69. Four pairs of polymorphic primers were screened out in the diversity analysis of 20 samples using PopGene 32. The number of observed alleles （Na） was 12， and the effective number of alleles （Ne） ranged from 1.362 9 to 2.605 9. The percentage of polymorphic loci （PPL） at each site was 100%， and the average values of genetic parameters Ho， He， and I at each site were 0.555 8， 0.444 2， and 0.753 2， respectively， indicating high polymorphism at each site. The screened four pairs of primers were utilized for DNA fingerprinting of the 20 samples， and it was found that the DNA fingerprints enabled the identification of these 20 samples. Conclusion:This study has provided a molecular basis for the study of the genetic relationship between plants in species Codonopsis and the intraspecific genetic differentiation.

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.