Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics.

The quantitative prediction of the SYNTAX score for cardiovascular artery disease patients using the inverse problem algorithm (IPA) technique in artificial intelligence was explored in this study. A 29-term semi-empirical formula was defined according to seven risk factors: (1) age, (2) mean arterial pressure, (3) body surface area, (4) pre-prandial blood glucose, (5) low-density-lipoprotein cholesterol, (6) Troponin I, and (7) C-reactive protein. Then, the formula was computed via the STATISTICA 7.

Survival rate prediction of breast cancer patients of 0-IV stages with and without radiotherapy via a revised Taylor series expansion algorithm: A population-based study in Taiwan.

Background: The morbidity of breast cancer has continuously achieved a global topicality. In particular, during the last decade several ten thousand female adults in Taiwan have been confirmed as breast cancer patients.

Objective: To predict the survival rate of breast cancer patients at various (0-IV) stages and provide efficient assessment of proposed radiotherapy for patients.

Folic Acid in Stroke Prevention in Countries without Mandatory Folic Acid Food Fortification: A Meta-Analysis of Randomized Controlled Trials.

Background And Purpose: Additional folic acid (FA) treatment appears to have a neutral effect on reducing vascular risk in countries that mandate FA fortification of food (e.g., USA and Canada).

Revised inverse problem algorithm-based prediction of coronary artery stenosis readings from the clinical data of patients with coronary heart diseases.

Aim: Coronary artery stenosis readings were predicted in this study on the basis of clinical data for patients with coronary heart diseases using the inverse problem algorithm.

Method: Five factors, including age, BSA (body surface area), MAP (mean artery pressure), sugar AC (ante cibum), and LDL-C (low-density Lipoprotein-Cholesterol) were incorporated into a nonlinear first-order regression fit analysis to develop a prediction equation with sixteen terms derived via a revised inverse problem algorithm implemented through the STATISTICA default regression fit. The clinical data acquired from ninety-three coronary heart disease patients were first normalized to the same domain range of [-1 to +1], and then processed by the above algorithm to find the compromised solution of predicted coronary artery stenosis reading.

The role of ALDH2 and ADH1B polymorphism in alcohol consumption and stroke in Han Chinese.

The genes encoding the enzymes for metabolising alcohol dehydrogenase 1B (ADH1B) and aldehyde dehydrogenase 2 (ALDH2) - exhibit genetic polymorphism and ethnic variations. Although the ALDH2*2 variant allele has been widely accepted as protecting against the development of alcoholism in Asians, the association of the ADH1B*2 variant allele with drinking behaviour remains inconclusive. The goal of this study was to determine whether the polymorphic ADH1B and ALDH2 genes are associated with stroke in male Han Chinese with high alcohol consumption.

Increased risk of cerebral hemorrhage in Chinese male heavy drinkers with mild liver disorder.

Background: Epidemiological evidence suggests that heavy alcohol consumption increases the risk for either stroke or liver disease. The goal of this study was to determine whether heavy drinkers with mild liver disorder (MLD) are at risk of hemorrhagic stroke.

Methods: All of the 524 patients recruited were males with a first-ever acute stroke and were consecutively admitted to the Tri-Service General Hospital between January 2000 and December 2001.