AI Medical Compendium

A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.

Parasites & vectors May 16, 2026

BACKGROUND: Malaria remains a significant global health challenge, requiring diagnostic approaches that are rapid, cost-effective, and accurate. The present study proposes a hybrid deep learning framework for malaria diagnosis using thin blood smear ... read more

Microaneurysm segmentation under out-of-domain generalization: from diabetic retinopathy to leukemic retinopathy.

International journal of retina and vitreous May 16, 2026

PURPOSE: To propose inter-disease out-of-domain generalization (OODG) across retinal diseases for microaneurysm (MA) segmentation using a deep-learning model trained and validated on diabetic retinopathy (DR) and qualitatively evaluated on leukemic r... read more

cGAS-STING pathway regulated by spatiotemporal heterogeneity of tumor microenvironment and precision therapy strategies in lung cancer.

Journal of experimental & clinical cancer research : CR May 16, 2026

The cGAS-STING pathway is a central regulator of innate immunity and exhibits a complex dual function in lung cancer: it can activate anti-tumor immune responses but also promote immune escape and metastasis. This "double-edged sword" effect is highl... read more

Dendrobine inhibits the growth and invasion of human breast cancer cells by regulating the NF-κB signaling pathway via the SLC6A9 target.

Cytotechnology May 16, 2026

Dendrobine exhibits notable anti-tumor activity against breast cancer (BRCA). In this study, we integrated network pharmacology, bioinformatics, and experimental validation to elucidate its mechanisms. SwissTargetPrediction and differential gene anal... read more

Adaptive fusion of EEG and NIRS with explainable AI reveals neurophysiological markers of cognitive flexibility.

Computers in biology and medicine May 16, 2026

Cognitive flexibility enables individuals to adapt to changing rules, goals, or uncertainty. This study evaluates the discriminative power of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals from 42 healthy young... read more

Interpretable behavioral clusters of gamblers through unsupervised learning.

Acta psychologica May 16, 2026

Understanding the heterogeneity among highly involved gamblers is critical for the development of effective harm reduction strategies. This study employs unsupervised machine learning to segment a population of high-intensity Electronic Gambling Mach... read more

Strengthening Holistic Nursing Competence Among Emergency Nurses in the Digital Era: The Roles of Clinical Governance, Digital Empathy, and Artificial Intelligence Attitude.

Journal of emergency nursing May 16, 2026

INTRODUCTION: Digital technologies and artificial intelligence are rapidly transforming emergency nursing, raising concerns about how nurses can sustain holistic, person-centered care in highly technologized environments. Organizational conditions su... read more

Predicting Dry Matter Intake in Gestating Ewes Using Greenhouse Gas Measurements from Portable Accumulation Chambers.

Journal of animal science May 16, 2026

Accurate measurement of dry matter intake (DMI) in sheep is logistically challenging and costly, particularly under commercial conditions and across diverse production systems. This study evaluated whether methane (CH4), carbon dioxide (CO2) and oxyg... read more

Transport and distribution patterns of floating marine litter: Numerical modeling and AI-empowered solutions.

Marine environmental research May 16, 2026

Floating marine litter (FML) has emerged as a central priority in global ocean governance. Thoroughly deciphering the spatiotemporal evolutionary mechanisms of its "source-sink" system is fundamental to formulating scientifically robust management st... read more

Predicting type II diabetes mellitus in young and middle-aged adults: A machine learning approach using the Utah population database.

Diabetes research and clinical practice May 16, 2026

AIMS: To develop a machine learning framework for predicting type 2 diabetes mellitus (T2DM) using administrative data and electronic health records (EHR) that could be applied in healthcare settings. METHODS: Study population included parents of ind... read more

Artificial Intelligence Medical Compendium