AIMC Topic: Creatinine

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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...

Development and validation of a machine learning model integrating BUN/Cr ratio for mortality prediction in critically ill atrial fibrillation patients.

Scientific reports
Atrial fibrillation (AF), the most prevalent critical care arrhythmia, demonstrates substantial mortality associations where renal dysfunction management plays a pivotal therapeutic role. We examined the prognostic capacity of admission blood urea ni...

Exploring the potential relationship between kidney disease index and cognitive dysfunction: a machine learning approach with NHANES data.

BMC geriatrics
OBJECTIVE: This study investigates the relationship between the Kidney Disease Index (KDI) and cognitive function, evaluating its potential as a predictive marker for cognitive impairment in older adults. We also compare the performance of KDI with t...

An artificial intelligence-assisted, kilometer-scale wireless and wearable biochemical sensing platform for monitoring of key biomarkers in urine.

Biosensors & bioelectronics
Wearable biochemical sensors enabling non-invasive monitoring of biomarkers in bodily fluids play a pivotal role in advancing personalized healthcare. The state-of-the-art wireless and wearable biochemical sensors still suffer from large form factors...

Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

Scientific reports
This study aims to establish and validate prediction models based on novel machine learning (ML) algorithms for augmented renal clearance (ARC) in critically ill patients with sepsis. Patients with sepsis were extracted from the Medical Information M...

Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Scientific reports
Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. This study aimed to develop a machine learning (ML) model to predict serum creatinin...

A neural network approach to glomerular filtration rate estimation: a single-centre retrospective audit.

Nuclear medicine communications
OBJECTIVES: The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race correction factor is frequently used for an estimate of glomerular filtration rate (eGFR) and to support a single-sample GFR regime. This study exa...

Determining IFI44 as a key lupus nephritis's biomarker through bioinformatics and immunohistochemistry.

Renal failure
BACKGROUND: Lupus nephritis (LN) emerges as a severe complication of systemic lupus erythematosus (SLE), significantly affecting patient survival. Despite improvements in treatment reducing LN's morbidity and mortality, existing therapies remain subo...

Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.

Journal of medical Internet research
BACKGROUND: Most artificial intelligence-based research on acute kidney injury (AKI) prediction has focused on intensive care unit settings, limiting their generalizability to general wards. The lack of standardized AKI definitions and reliance on in...