A5005447973- Diabetes, Cardiovascular Risks, and Lipoproteins
- Artificial Intelligence in Healthcare
- Cardiovascular Function and Risk Factors
- Machine Learning in Healthcare
- Diabetes Treatment and Management
- Diet, Metabolism, and Disease
- Advanced Glycation End Products research
- Imbalanced Data Classification Techniques
- Genetic Syndromes and Imprinting
- Neurological Disorders and Treatments
- Diabetes Management and Research
- Diabetes and associated disorders
- Hormonal Regulation and Hypertension
- Cardiovascular Disease and Adiposity
- Phytoestrogen effects and research
- Coronary Interventions and Diagnostics
- Thyroid Disorders and Treatments
- Dermatological and Skeletal Disorders
- Neurological Disease Mechanisms and Treatments
- Data-Driven Disease Surveillance
- Radiomics and Machine Learning in Medical Imaging
- Natural Antidiabetic Agents Studies
- Atrial Fibrillation Management and Outcomes
- Cardiovascular Health and Risk Factors
- Acute Ischemic Stroke Management
Tongji University
2017-2024
Southeast University
2021-2024
Zhongda Hospital Southeast University
2009-2024
Tongji Hospital
2017
Several studies suggest that the inflammation plays a role in pathogenesis of some glucose disorders adults. Exposure pancreatic β-cells to cytokines, such as interleukin-1β (IL-1β), is thought contribute β-cell apoptosis. One important event triggered by IL-1β induction nitric oxide synthase (iNOS), an enzyme catalyzes intracellular generation cytotoxic free radical NO. Recent work have suggested formononetin, O-methylated isoflavone found number plants and herbs like Astragalus...
In this paper, we propose a diabetes data anomaly detection approach based on hierarchical clustering and support vector machine (SVM), named (HCSVM). the HCSVM approach, sets with same characteristics are classified by algorithm, divided into significant abnormal parts potential parts. Additionally, convolutional neural network (CNN) is utilized to detect analyze of each part. The feature output from CNN full connection layer applied as input SVM classifier, optimal classification...
We aim to determine the role of free triiodothyronine (FT3), main active ingredient thyroid hormones (THs), in type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI).A total 255 T2DM without diagnosed diseases were recruited and divided into MCI group healthy cognition group. Neuropsychological functions observed by multidimensional function scales including MoCA, Digit Span Test (DST), Verbal Fluency (VFT), Clock drawing test (CDT), Trail Making (TMT) A B,...
Purpose: In addition to hyperglycemia and hyperlipidemia, obesity hypertension are important cardiovascular risk factors for coronary heart disease (CHD) in patients with type 2 diabetes mellitus (T2DM). This study aims explore the interaction of these factors. Patients Methods: Data hospitalized T2DM from 2013 2018 were collected. A multivariate logistic regression model was established. normal weight blood pressure recruited as controls. The on CHD evaluated by relative excess due (RERI)....
The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability medical expertise limits its clinical application. This paper combines knowledge representation methods, a model is constructed. initially constructs relationship graph between physical indicator test value based on normal range examination index. And index testing by encoded. Then, patient data represented as vector input into built...
Background: The conventional approaches to diabetes screening are potentially limited by poor compliance and laboratory demand. This study aimed evaluate the performance of fasting plasma glucose (FPG) postprandial urine (PUG) in for Chinese high-risk population. Methods: Nine hundred nine subjects with factors underwent oral tolerance test after an overnight fast. FPG, hemoglobin A1c, 2-h (2 h-PG), 2 h-PUG were evaluated. Diabetes prediabetes defined American Association criteria. area...
Objective Coronary heart disease (CHD) is the most common cause of death in patients with type 2 diabetes (T2DM). We aim to estimate prevalence CHD and cardiovascular risk factors Chinese T2DM.Methods A total 66,536 inpatients treated from 2013 2018 were investigated, demographic clinical data collected 30,693 T2DM. Age-standardized was calculated on basis population census 2010. Logistic regression analysis used analyze factors.Results The crude estimated be 23.5% a standardized 13.9%...
Dipeptidyl peptidase-4 inhibitors (DPP-4i) have become firmly established in treatment algorithms and national guidelines for improving glycemic control type 2 diabetes mellitus (T2DM).To report the findings from a multicenter, randomized, double-blind, placebo-controlled phase 3 clinical trial, which was designed to assess efficacy safety of novel DPP-4 inhibitor fotagliptin treatment-naive patients with T2DM.Patients T2DM were randomized receive (n = 230), alogliptin 113) or placebo 115)...
Type 2 diabetes may impair cardiac structure and function at very early stage, other factors, for example, obesity hypertension, can induce aforementioned abnormalities individually. This study aimed to explore precise prevention treatment of diabetic cardiomyopathy (DCM) by using cluster analysis echocardiographic variables.A total 66 536 inpatients with from 2013 2018 were investigated, 7112 patients available after nadir. The was performed on variables assess the clinical profiles risk...
患者女,13岁,因体检发现血压高1个月就诊.1个月前体检时测血压为150/100 mm Hg(1 Hg=0.133 kPa),后多次测血压均高于正常,无月经初潮.家族中无高血压史,父母系近亲结婚。
Abstract Background: Coronary heart disease (CHD) is the most common cause of death in patients with type 2 diabetes (T2DM). We aim to estimate prevalence CHD and cardiovascular risk factors Chinese diabetic inpatients. Methods: A total 66536 inpatients from 2013 2018 were investigated, demographic clinical data gathered 30693 T2DM. The age-standardized was calculated on basis population census 2010. Multiple imputation used impute missing values logistic regression analysis analyze factors....