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Chromatography, Liquid

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Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals.

Journal of chromatography. A
The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screeni...

In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis.

Forensic science international
Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the an...

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Analytical chemistry
Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learni...

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.

Molecular & cellular proteomics : MCP
Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold s...

Discrimination of Common Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning.

Journal of the American Society for Mass Spectrometry
Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we ut...

Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND & OBJECTIVE: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach ...

Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy.

Cellular and molecular life sciences : CMLS
AIM: The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identi...

Autonomous mobile robots for exploratory synthetic chemistry.

Nature
Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making. Most autonomous laboratories involve bespoke automated equipment, and reaction outcomes are ofte...

Identification of novel hypertension biomarkers using explainable AI and metabolomics.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization's Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This ...

Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate can...