AIMC Topic: Humans

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Decoding the quantitative structure-activity relationship and astringency formation mechanism of oxygenated aromatic compounds.

Food research international (Ottawa, Ont.)
Astringency is a common sensory experience in the mouth, characterized by dryness, roughness, and puckering. Due to the inefficiency and expense of conventional astringency evaluation methods, the quantitative structure-activity relationship (QSAR) m...

Evaluating Google Gemini's AI generated responses to questions after breast reconstruction.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: The purpose of this study was to evaluate Google Gemini's responses to common post-operative questions pertaining to breast reconstruction surgery.

From social effort to social behavior: An integrated neural model for social motivation.

Neuroscience and biobehavioral reviews
As humans rely on social groups for survival, social motivation is central to behavior and well-being. Here we define social motivation as the effort that initiates and directs behavior towards social outcomes, with the goal of satisfying our fundame...

U-shaped network combining dual-stream fusion mamba and redesigned multilayer perceptron for myocardial pathology segmentation.

Medical physics
BACKGROUND: Cardiac magnetic resonance imaging (CMR) provides critical pathological information, such as scars and edema, which are vital for diagnosing myocardial infarction (MI). However, due to the limited pathological information in single-sequen...

Joint resting state and structural networks characterize pediatric bipolar patients compared to healthy controls: a multimodal fusion approach.

NeuroImage
Pediatric bipolar disorder (PBD) is a highly debilitating condition, characterized by alternating episodes of mania and depression, with intervening periods of remission. Limited information is available about the functional and structural abnormalit...

Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Environment international
Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular disease and type 2 diabetes. Emerging evidence suggests that environmental chemical exposures may pl...

Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research.

Drug discovery today
Artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research, particularly in protein and nucleic acid studies. This review summarizes the current status of AI and ML applications in the pharmaceutical sector, fo...

Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing.

Drug discovery today
Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced techniques, including machine learning (ML), deep learning (DL), AI-powered digital twins (DTs), and AI-enhan...

Circular RNA discovery with emerging sequencing and deep learning technologies.

Nature genetics
Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disea...