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1382 published stories · page 56 of 93

Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance
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Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identifica…

Journal of Computer-Aided Molecular Design
Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning
Evidence-backed gain

Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning

Statement of problem Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation-such as the implant passport or surgical report-is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction. Purpose The purpose of this study was to develop and validate an artificial intelligence…

Adaptive level modification via player skill classification and large language models
Evidence-backed gain

Adaptive level modification via player skill classification and large language models

Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level cont…

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment
Evidence-backed gain

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment

Aim Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor progression, but its role in CRC heterogeneity and the tumor immune microenvironment (TIME) requires systematic investigation. Methods A systematic evaluation of 101 combinations of machine learning and statistical algorithms was conducted within a 10-fold cross-validation framework to develop and validate the optimal model, termed the methionine metabolism-related…

Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging
Evidence-backed gain

Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging

Purpose This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clinical activity score and key patient characteristics in thyroid eye disease. Methods The publicly available TOM500 dataset, consisting of orbital magnetic resonance imaging scans and clinical data from 500 thyroid eye disease patients, was split into training ( n = 360), validation ( n = 100), and test ( n = 40) sets. A ResNet-50 convolutional neural network pretrain…

Health

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors

Purpose Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy. Methods A cohort of patients receiving ICI therapy from 2010 to 2023 was identified from the TriNetX database. Cardiac i…

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors
Health

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis

Background Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for precise risk stratification. This study aimed to develop a metabolomics-based prediction model to identify high-risk metabolic phenotypes among obese participants and to elucidate the causal metabolic pathways involved. Methods Forty-one-thousand-four-hundred-fifty-nine obese participants were followed for a median of 14.4 years. We integrated multiple machine learning…

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis
Health

Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging

Clinical justification remains fundamental to the safe use of imaging involving ionising radiation, requiring a favourable balance between diagnostic benefit and stochastic risk. Concurrently, advances in imaging technology and artificial intelligence have enabled opportunistic identification of additional pathologies beyond the primary indication for imaging. This opinion article discusses how emerging opportunistic osteoporosis detection technologies may eventually transition into clinically justified diagnost…

Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging
Health

An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers

Background Cervical cancer screening in primary care is hindered by expert pathologist shortages and heavy diagnostic workloads, leading to fatigue-induced misdiagnoses. This study evaluated the diagnostic capacity, subpopulation robustness, and operational efficiency of an interpretable machine learning (ML) tool within a large Chinese healthcare network. Methods A retrospective database of 5,000 women was audited. Archived liquid-based cytology (LBC) digital slides were evaluated via a parallel validation chan…

An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers
Climate

The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0

The topic’s relevance is related to the need to improve the efficiency of municipal waste management in the context of the development of Industry 4.0, where artificial intelligence (AI) can play a key role in optimizing the processes of sorting, collecting, and recycling waste. The purpose of the study is to study the potential of AI to improve environmental and operational indicators in the field of waste management, as well as to test hypotheses regarding the impact of AI on reducing costs, increasing efficie…

The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0