scholarly journals Antimicrobial α-defensins as Multi-target Inhibitors Against Amyloid Formation and Microbial Infection

2021 ◽  
Author(s):  
Yanxian Zhang ◽  
Yonglan Liu ◽  
Yijing Tang ◽  
Dong Zhang ◽  
Huacheng He ◽  
...  
Keyword(s):  
Risk Factors ◽  
Thyroid Carcinoma ◽  
Type Ii Diabetes ◽  
Amyloid Formation ◽  
Type Ii ◽  
Amyloid Aggregation ◽  
Microbial Infection ◽  
Medullary Thyroid ◽  
Parkinson’S Diseases ◽  
Amyloid Diseases

Amyloid aggregation and microbial infection are considered as pathological risk factors for developing amyloid diseases, including Alzheimer’s (AD), type II diabetes (T2D), Parkinson’s diseases (PD), and medullary thyroid carcinoma (MTC)....

scholarly journals Diabetes in Post-Soviet Armenia: analysis of risk factors contributing to type II diabetes

2016 ◽  
Vol 82 (3) ◽  
pp. 423
Author(s):  
H. Wright ◽  
D. Beard ◽  
C. Ho ◽  
L.A. Stutz ◽  
M. Hovhannisyan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type Ii Diabetes ◽  
Type Ii

scholarly journals Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Mingyue Xue ◽  
Yinxia Su ◽  
Chen Li ◽  
Shuxia Wang ◽  
Hua Yao
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Tree ◽  
Type Ii Diabetes ◽  
Large Scale ◽  
Systolic Pressure ◽  
Gradient Boosting ◽  
Significant Feature ◽  
Type Ii ◽  
Extreme Gradient Boosting

Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.

scholarly journals Mechanistic Contributions of Biological Cofactors in Islet Amyloid Polypeptide Amyloidogenesis

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Phuong Trang Nguyen ◽  
Nagore Andraka ◽  
Carole Anne De Carufel ◽  
Steve Bourgault
Keyword(s):  
Type Ii Diabetes ◽  
Amyloid Fibrils ◽  
Driving Forces ◽  
Islet Amyloid Polypeptide ◽  
Type Ii Diabetes Mellitus ◽  
Major Protein ◽  
Local Concentration ◽  
Amyloid Formation ◽  
Type Ii ◽  
Islet Amyloid

Type II diabetes mellitus is associated with the deposition of fibrillar aggregates in pancreatic islets. The major protein component of islet amyloids is the glucomodulatory hormone islet amyloid polypeptide (IAPP). Islet amyloid fibrils are virtually always associated with several biomolecules, including apolipoprotein E, metals, glycosaminoglycans, and various lipids. IAPP amyloidogenesis has been originally perceived as a self-assembly homogeneous process in which the inherent aggregation propensity of the peptide and its local concentration constitute the major driving forces to fibrillization. However, over the last two decades, numerous studies have shown a prominent role of amyloid cofactors in IAPP fibrillogenesis associated with the etiology of type II diabetes. It is increasingly evident that the biochemical microenvironment in which IAPP amyloid formation occurs and the interactions of the polypeptide with various biomolecules not only modulate the rate and extent of aggregation, but could also remodel the amyloidogenesis process as well as the structure, toxicity, and stability of the resulting fibrils.

P.2.d.007 Are comorbid insulin resistance and type II diabetes risk factors for refractory bipolar illness?

2013 ◽  
Vol 23 ◽  
pp. S366-S367 ◽  
Author(s):  
C. Calkin ◽  
M. Ruzickova ◽  
C. Slaney ◽  
J. Garnham ◽  
T. Ransom ◽  
...  
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Type Ii Diabetes ◽  
Diabetes Risk ◽  
Type Ii ◽  
Bipolar Illness ◽  
Diabetes Risk Factors
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