AIMC Topic: Humans

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Leveraging Large Language Models to Enhance Emotional Intelligence Training in Anesthesiology.

Anesthesiology
Emotional intelligence is essential for high-stakes interactions in the perioperative setting. Whether addressing patient concerns, resolving conflicts, or triaging cases, anesthesiologists rely on emotional intelligence for effective communication. ...

The correlation of liquid biopsy genomic data to radiomics in colon, pancreatic, lung and prostatic cancer patients.

European journal of cancer (Oxford, England : 1990)
INTRODUCTION: With the advances in artificial intelligence (AI) and precision medicine, radiomics has emerged as a promising tool in the field of oncology. Radiogenomics integrates radiomics with genomic data, potentially offering a non-invasive meth...

Artificial intelligence driven neuropsychiatry: a systematic review of electroencephalography-based computational techniques for major depressive disorder prediction.

Neuroscience
Major Depressive Disorder is the most prominent global mental health issue impacting millions of individuals worldwide. Electroencephalogram signals capturing intricate brain dynamics have emerged as a promising modality for predicting depression. Th...

Artificial intelligence-assisted nursing in cancer care: A meta-analysis of its impact on pain, anxiety, and quality of life.

Applied nursing research : ANR
BACKGROUND: In recent years, artificial intelligence (AI) applications have been recognized as a supportive technological method for effectively managing the challenges faced by patients with cancer. AI applications are anticipated to be beneficial i...

MedKA: A knowledge graph-augmented approach to improve factuality in medical Large Language Models.

Journal of biomedical informatics
Large language models (LLMs) have demonstrated remarkable potential in medical applications. However, they still face critical challenges such as hallucinations, knowledge inconsistency, and insufficient integration of domain-specific medical experti...

Deep Learning in Antimicrobial Peptide Prediction.

Journal of chemical information and modeling
Antimicrobial peptides (AMPs) have garnered significant attention from researchers as effective alternatives to antibiotics. In recent years, deep learning has demonstrated unique advantages in AMP prediction, surpassing traditional machine learning ...

Integrating Protein Language Models and Geometric Deep Learning for Peptide Toxicity Prediction.

Journal of chemical information and modeling
Peptide toxicity prediction is a critical task in biomedical research, influencing drug safety and therapeutic development. Traditional methods, relying on sequence similarity or handcrafted features, struggle to capture the complex relationship betw...

Deep Learning-Enhanced Hand-Driven Spatial Encoding Microfluidics for Multiplexed Molecular Testing at Home.

ACS nano
The frequent global outbreaks of viral infectious diseases have significantly heightened the urgent demand for molecular testing at home. However, the labor-intensive sample preparation and nucleic acid amplification steps, along with the complexity ...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Analytical chemistry
Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive diagnostics. Extracellular vesicles (EVs) serve as molecular messengers of the tumor microenvironme...

Prediction of Internal Exposures after Virtual Oral Doses of Disparate Chemicals in Rats and Humans Using Simplified Physiologically Based Pharmacokinetic Models with Generated Input Parameters.

Chemical research in toxicology
Toxicological evaluation of industrial chemicals with a broad range of chemical structures, for example, bioactive food components, toxic food-derived compounds, and drugs, usually involves the estimation of human clearance by allometric extrapolatio...