AIMC Topic: Uric Acid
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Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder.
Journal of affective disorders
Apr 16, 2022
BACKGROUND: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.
Frontiers in endocrinology
Jul 14, 2021
BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise ...
Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study.
Journal of diabetes research
Jun 12, 2020
BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible predi...
Humans
Machine Learning
Retrospective Studies
Blood Glucose
Cohort Studies
Logistic Models
Decision Trees
Glycated Hemoglobin
Pregnancy
Female
Reproducibility of Results
Young Adult
Adult
Maternal Age
Triglycerides
Clinical Decision Rules
Pregnancy in Obesity
Routinely Collected Health Data
Cholesterol
Uric Acid
Diabetes, Gestational
Lipoproteins, HDL
Deep learning computer vision algorithm for detecting kidney stone composition.
BJU international
Mar 3, 2020
OBJECTIVES: To assess the recall of a deep learning (DL) method to automatically detect kidney stones composition from digital photographs of stones.
Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.
European radiology
Nov 26, 2019
OBJECTIVES: To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.
Disability in multiple sclerosis is associated with age and inflammatory, metabolic and oxidative/nitrosative stress biomarkers: results of multivariate and machine learning procedures.
Metabolic brain disease
Jul 13, 2019
The aim of this study was to evaluate the immune-inflammatory, metabolic, and nitro-oxidative stress (IM&NO) biomarkers as predictors of disability in multiple sclerosis (MS) patients. A total of 122 patients with MS were included; their disability w...
Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain.
Food chemistry
Jan 19, 2019
Fuzzy controller artmap based algorithms via E-nose selective metal oxides sensor (MOS) data was applied for classification of S. oryzae infestation in rice grains. The screened defuzzified data of selective sensors was further applied to detect S. o...
The association between serum uric acid to creatinine ratio and renal disease progression in type 2 diabetic patients in Chinese communities.
Journal of diabetes and its complications
Nov 3, 2018
AIMS: Serum uric acid (UA) increases in patients with kidney disease due to the impaired UA clearance. The present study sought to evaluate the association between UA/creatinine ratio (UA/Cr) and renal disease progression in patients with type 2 diab...
Low serum uric acid concentration augments insulin effects on the prevalence of metabolic syndrome.
Diabetes & metabolic syndrome
Dec 22, 2017
AIM: Insulin and uric acid were shown affect the prevalence of Metabolic Syndrome (MetS), but no studies examine their interaction. Therefore, we conducted this study to determine their biological interaction in subjects from central Mexico.
Serum 1,5-Anhydroglucitol Concentrations Remain Valid as a Glycemic Control Marker In Diabetes with Earlier Chronic Kidney Disease Stages.
Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association
Dec 21, 2017
PURPOSE: To investigate the reliability of 1,5-anhydroglucitol (1,5-AG) in diabetes with mild or moderate renal dysfunction.
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