Artificial Intelligence Medical Compendium

OBJECTIVES: The global crisis of antimicrobial resistance (AMR) demands a paradigm shift in traditional drug discovery, and artificial intelligence (AI) is uniquely positioned to lead this transformation. METHODS: We highlight how AI and machine lear... read more 

The use of artificial intelligence for emotion recognition is the focus of improving human-computer interaction. Recently, deep learning has been widely used in the study of emotion recognition. However, how to correctly identify emotions still faces... read more 

OBJECTIVES: To develop an artificial intelligence (AI)-assisted system for the automated quantitative evaluation of temporomandibular joint (TMJ) condylar morphological changes. METHODS: This study utilized 212 cone-beam computed tomography (CBCT) sc... read more 

OBJECTIVES: This scoping review aimed at identifying laboratory-based additive modifications in commercial glass ionomer cements (GICs), analyzing trends between additives and properties improved, and highlighting research gaps relevant to clinical p... read more 

BACKGROUND: Alzheimer's disease (AD) is increasingly recognized as a multifactorial network disorder in which amyloid and tau pathology interact with mitochondrial dysfunction, neuroinflammation, metabolic impairment, vascular dysregulation, and syna... read more 

Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential nutrients for animals that must be obtained from the diet, as mammals cannot synthesize them. This study developed an engineered Pichia pastoris strain for e... read more 

Microplastics (MPs) contamination in agricultural soils has emerged as a critical environmental challenge, particularly in Bangladesh, where agriculture underpins food security and trade. This study provides one of the first comprehensive assessments... read more 

Deep clustering of single-cell RNA-seq data faces significant challenges due to extreme sparsity and noise. We present DAGCL (Dynamic Attention-enhanced Graph Embedding with Curriculum Learning), a dynamic graph embedding framework that reframes repr... read more 

As a novel paradigm for learning with inexact supervision, Pcomp classification reduces the annotation costs of training a binary classifier by using ordered pairwise samples without requiring precise labels. However, existing methods fail to fully a... read more 

Deep multi-view clustering has rapidly developed in recent years, leveraging the powerful representation capabilities of deep neural networks. Among them, instance-level feature contrastive learning is widely used in deep multi-view clustering to ali... read more