AIMC Topic: Humans

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Multifactorial Biomarkers for "Talk and Deteriorate" after Head Trauma Identified Using Machine Learning.

Neurologia medico-chirurgica
Talk and Deteriorate refers to a clinical course where a patient is able to speak immediately after a traumatic brain injury but subsequently deteriorates in consciousness. Talk and Deteriorate outcomes are poor, and reliable prediction may help impr...

Artificial intelligence in the non-clinical laboratory: enhancing good laboratory and documentation practices.

International journal of pharmaceutics
Non-clinical laboratories are under increasing regulatory pressure from agencies such as the FDA, EMA, and MHRA to ensure data integrity, traceability, and compliance with Good Laboratory Practice (GLP) and Good Documentation Practice (GDP). While Ar...

Linarin protects against asthma-induced airway epithelial ferroptosis and inflammation via ALDH2 regulation.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Asthma, a complex disease characterized by airway epithelial dysfunction and chronic inflammation, poses ongoing therapeutic challenges.

Comparative analysis of robotic assisted vs. traditional spinal angiography in a large single-center experience.

Journal of the neurological sciences
BACKGROUND: Spinal angiography (SA) remains the gold standard for evaluating spinal cord vasculature, but traditional approaches expose operators and patients to significant ionizing radiation. Robotic-assisted platforms offer potential advantages th...

Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic.

Vaccine
Understanding vaccine hesitancy is a critical public health challenge, yet traditional statistical methods often fail to capture the complex drivers behind it. This study uses COVID-19 vaccine hesitancy in a rural population as a case study to demons...

Machine Learning Models to Predict Withdrawal of Life-Sustaining Therapy in Patients With Severe Traumatic Brain Injury.

Neurology
BACKGROUND AND OBJECTIVES: Over half of all deaths after traumatic brain injury (TBI) follow the decision to withdraw life-sustaining therapy (WLST). Despite recent improvements in TBI mortality, rates of WLST have remained unchanged, potentially ref...

Mapping Context-Aware Phosphosite Regulation of Protein-Protein Interactions Using Deep Learning and Pan-Cancer Proteomics.

Journal of chemical information and modeling
Phosphorylation dynamically orchestrates the protein-protein interaction (PPI) network that governs cellular signaling, and its dysregulation frequently drives malignant transformation and neurodegeneration. We present PhosPPI-SEQ, an interpretable d...

GLA-Synergy: An Interpretable Global-Local Adaptive Framework for Drug Synergy Prediction in Cancer Treatment.

Journal of chemical information and modeling
Effective anticancer drug combinations are crucial for advancing cancer treatment, yet predicting drug synergy remains challenging due to the complexity of biological interactions. Existing methods struggle to integrate multimodal features and to mod...

Deep Learning-Aided Noninvasive Monitoring of Skin Tissue Temperature Distribution and Blood Perfusion Rate Based on Fractal Conformal Sensors.

ACS sensors
Skin thermophysical properties are key for health assessment with real-time monitoring enabling early detection of skin-related issues. A polydimethylsiloxane-encapsulated Peano fractal conformal sensor is fabricated by flexible printed circuit techn...

Development of a Novel Hydroxylamine-Based Stable Isotope Labeling Reagent for Profiling Aldehyde Metabolic Biomarkers in Diabetes Using LC-MS/MS and Machine Learning.

Analytical chemistry
Aldehyde compounds are significantly associated with diabetes mellitus. The metabolic profile of aldehydes can enhance understanding of the mechanisms underlying development of diabetes. This study employed a pair of stable isotope labeling (SIL) rea...