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Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach.

European radiology experimental
OBJECTIVES: To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps.

Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Breast cancer is the most common cancer among women, with its burden increasing over the past decades. Early diagnosis significantly improves survival rates and reduces lethality. Innovative technologies are being developed for early de...

Mucosal immune responses and intestinal microbiome associations in wild spotted hyenas (Crocuta crocuta).

Communications biology
Little is known about host-gut microbiome interactions within natural populations at the intestinal mucosa, the primary interface. We investigate associations between the intestinal microbiome and mucosal immune measures while controlling for host, s...

Gut microbiota alterations are linked to COVID-19 severity in North African and European populations.

NPJ biofilms and microbiomes
Although COVID-19 primarily affects the respiratory system, many patients experience gastrointestinal symptoms, suggesting a role for the gut microbiota in disease pathogenesis. To explore this, we performed shotgun metagenomic sequencing on stool sa...

Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.

PloS one
INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This high...

Same-model and cross-model variability in knee cartilage thickness measurements using 3D MRI systems.

PloS one
PURPOSE: Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical...

Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors.

International journal of medical informatics
BACKGROUND AND OBJECTIVES: The treatment of central nervous system (CNS) tumors is complex and resource-intensive, with higher mortality in underserved regions. Large language models (LLMs) show promise in medical support, but their real-world perfor...

NeuroEmo: A neuroimaging-based fMRI dataset to extract temporal affective brain dynamics for Indian movie video clips stimuli using dynamic functional connectivity approach with graph convolution neural network (DFC-GCNN).

Computers in biology and medicine
FMRI, a non-invasive neuroimaging technique, can detect emotional brain activation patterns. It allows researchers to observe functional changes in the brain, making it a valuable tool for emotion recognition. For improved emotion recognition systems...

Role of large language models in mental health research: an international survey of researchers' practices and perspectives.

BMJ mental health
BACKGROUND: Large language models (LLMs) offer significant potential to streamline research workflows and enhance productivity. However, limited data exist on the extent of their adoption within the mental health research community.