Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 64,791 to 64,800 of 231,605 articles

Deciphering the 3D genome organization across species from Hi-C data.

Nucleic acids research
3D genome organization is essential for gene regulation, yet in various species it is driven by different biological mechanisms. Species-specific factors and DNA sequences influence chromatin folding, complicating cross-species comparisons. Leveragin... read more 

Modeling multidrug resistance in Campylobacter coli and Campylobacter jejuni isolated from swine at U.S. slaughter plants.

International journal of food microbiology
Antimicrobial-resistant and multidrug-resistant Campylobacter spp. poses a human health risk. A total of 1694 isolates from market hogs (1599 Campylobacter coli and 95 C. jejuni) and 965 isolates from sows (918 C. coli and 47 C. jejuni) that were iso... read more 

Volatile methyl siloxanes in electric vehicle cabin air: The first investigation of occurrence, influencing factors, and inhalation risks.

Journal of hazardous materials
Volatile methyl siloxanes (VMS) are synthetic chemicals extensively used in various components of electric vehicles (EVs). However, their release characteristics in EV cabins and the potential inhalation risks to occupants remain unclear. This study ... read more 

CoT defender: Preemptive chain-of-thought occupation for jailbreak attack mitigation.

Neural networks : the official journal of the International Neural Network Society
With the development of large language models (LLMs), numerous studies have demonstrated their vulnerability to carefully crafted jailbreak attacks. However, existing mitigation measures rarely balance model usability with significant protective effe... read more 

Ultrashort echo time MRI radiomics as a predictor of clinical outcomes in patellar tendinopathy: Insights from a large prospective clinical trial.

European journal of radiology
PURPOSE: To evaluate the predictive utility of radiomic features extracted from ultrashort echo time (UTE) MRI in comparison to conventional proton density (PD) sequences for short-term (24-week) and long-term (5-year) clinical outcomes in patients w... read more 

Predictors of past 30-day vaping abstinence among young e-cigarette users: Machine learning analysis of a longitudinal cohort.

Addictive behaviors
INTRODUCTION: Our existing knowledge on factors influencing vaping abstinence are still limited. The objective of this study was to build a machine learning (ML)-based model to predict past 30-day vaping abstinence and identify predictors among young... read more 

Identification of COL8A2, MICAL2, and TNFSF10 as potential biomarkers associated with both exercise response and osteoarthritis: a multi-omics integration study.

3 Biotech
UNLABELLED: To identify molecular biomarkers associated with both osteoarthritis (OA) pathology and exercise response through multi-omics integration. Bulk RNA-seq, exercise transcriptomics, and single-cell RNA-seq datasets were integrated. Machine l... read more 

Distinct neural signatures in a sensorimotor synchronization-continuation task.

Imaging neuroscience (Cambridge, Mass.)
Optimal sensorimotor timing hinges on the generation, refinement, and employment of internal models to meet task demands. In finger tapping sensorimotor synchronization tasks, this occurs across and within tapping conditions that prompt externally-cu... read more 

Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation: a cross-sectional study with interpretable diagnostic boundaries.

PeerJ
BACKGROUND: Differentiating preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD) is challenging. Traditional biphasic CT scans are limited by radiation exposure, while single-inspiratory CT-based deep learning... read more 

Development and deployment of an interpretable stacking ensemble model for predicting in-hospital mortality in ICU patients with chronic kidney disease and sepsis.

Digital health
OBJECTIVE: To develop an interpretable stacking ensemble model for predicting in-hospital mortality in intensive care unit (ICU) patients with CKD and sepsis and to deploy it as a web-based tool for bedside clinical use. METHODS: Data were extracted ... read more