AIMC Topic: Uric Acid

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Noninvasive CT radiomics-clinical model accurately classifies anhydrous uric acid stones: a multicenter study.

World journal of urology
BACKGROUND: Urolithiasis, particularly anhydrous uric acid stones (AUAs), imposes significant clinical and economic burdens. Accurate preoperative differentiation of AUAs from other stone types remains challenging, yet essential for personalized pati...

AI-Assisted Microfluidic Paper-Based Analytical Device with Au-Pt Nanoparticles for Multiplex, Interference-Resistant Quantification of Urinary Biomarkers.

Analytical chemistry
Urinary glucose, creatinine, and uric acid are vital biomarkers for diabetes and kidney disease management. However, multiplex point-of-care detection faces challenges due to insufficient sensitivity in complex urine matrices and signal cross-talk fr...

Machine learning models for predicting renal injury in patients with gout.

Renal failure
BACKGROUND: Renal injury is a severe complication among individuals diagnosed with gout. This research constructed a machine learning predictive model to assess renal injury risk in gout patients.

Label-Free SERS Platform Assisted by Machine Learning for Multi-Target Detection and Physiological State Classification in Sweat.

Analytical chemistry
The detection of sweat metabolites is crucial for health monitoring, disease screening, and personalized medicine. Traditional methods encounter challenges like low metabolite concentrations, complex biological matrices, and difficulty in achieving m...

All-flexible chronoepifluidic nanoplasmonic patch for label-free metabolite profiling in sweat.

Nature communications
Wearable sensors allow non-invasive monitoring of sweat metabolites, but their reliance on molecular recognition elements limits both physiological coverage and temporal resolution. Here we report an all-flexible chronoepifluidic surface-enhanced Ram...

Construction and validation of a urinary stone composition prediction model based on machine learning.

Urolithiasis
The composition of urinary calculi serves as a critical determinant for personalized surgical strategies; however, such compositional data are often unavailable preoperatively. This study aims to develop a machine learning-based preoperative predicti...

Wearable Double Network Plasmonic Hydrogel for SERS Detection of Urea and Uric Acid in Sweat.

ACS sensors
Traditional wearable devices for sweat detection often face limitations such as low detection sensitivity, insufficient mechanical properties, and discomfort during use. To address these challenges, hydrogels are utilized as sensor patches to improve...

Multi-component metabolite electrochemical detection and analysis based on machine learning.

Analytical methods : advancing methods and applications
Metabolic molecules are highly correlated with various physiological indicators and diseases, so it is particularly important to monitor the levels of multiple metabolites in the body. Due to the similar electrochemical properties of uric acid (UA), ...

Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome.

BMC microbiology
Hyperuricemia (HUA) and gout result from imbalances in uric acid metabolism and are closely associated with the gut microbiota. Advanced analytical methods facilitate the exploration of microbiota complexity. In this study, 16S rRNA sequencing data f...

Physiological serum uric acid concentrations correlate with arterial stiffness in a sex-dependent manner.

BMC medicine
BACKGROUND: In humans, uric acid is a product of purine metabolism that impacts the vascular system. In addition to effects on arterial vascular tone, associations between serum uric acid concentrations-even in the physiological range-and arterial hy...