AIMC Topic: Adult

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Classifying complex multimorbidity using latent class analysis and machine learning to generate insights into clustering of mental and cardiometabolic conditions.

PloS one
Machine learning techniques earn higher accuracy and robustness in multimorbidity prediction at this moment in time. Among various forms of multimorbidity, complex multimorbidity, especially the intersection of cardiometabolic disorders and mental he...

Human asymmetries in AI art: Syntax and writing direction effects on agent position in AI-generated images.

PloS one
The present study investigates positional patterns in visual representations generated by two artificial intelligence (AI) models in response to textual prompts describing interactions between two animate entities. The primary objective is to assess ...

Electroencephalography source-space functional connectivity reveals frequency-specific brain network dysfunctions in obsessive-compulsive disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Obsessive-compulsive disorder (OCD) is characterized by disruptions in large-scale brain networks. However, the role of high-frequency neural synchrony in these abnormalities remains unclear. Elucidating frequency-specific alterations may...

The association between estimated glucose disposal rate and the prevalence and mortality of chronic kidney disease: a cross-sectional study with linked mortality follow-up.

European journal of medical research
BACKGROUND: Metabolic disorders represented by insulin resistance (IR) are at risk of chronic kidney disease (CKD). Estimated glucose disposal rate (eGDR) reflects IR. The relationship between eGDR and CKD was unclear. This study aimed at discussing ...

Identification of ferroptosis- and mitochondrial metabolism-related biomarkers and the potential molecular mechanisms of poor ovarian response.

Journal of ovarian research
BACKGROUND: Ferroptosis and mitochondrial metabolism are closely associated with the pathological processes of various diseases. However, the role of ferroptosis-related genes (FRGs) and mitochondrial metabolism-related genes (MMRGs) in poor ovarian ...

Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus.

Cardiovascular diabetology
BACKGROUND AND AIM: Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there i...

Prediction of uterine cavity conception environment using two-dimensional transvaginal ultrasound imaging semantic feature-based machine learning: a case-control study.

BMC pregnancy and childbirth
BACKGROUND: Independently investigating the association between pregnancy outcomes and the uterine cavity conception environment (UCCE) is challenging. Therefore, this study aimed to employ a range of machine learning algorithms to systematically ana...

Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.

BMC infectious diseases
BACKGROUND: HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identifying high-risk pat...

Non-invasive identification of mesenchymal glioblastoma using quantitative radiomic features from advanced diffusion MRI: a preclinical-to-clinical transfer learning strategy.

European radiology experimental
BACKGROUND: Glioblastoma (GBM) is no longer regarded as a single disease, as distinct molecular subgroups exist, with the mesenchymal (MES) having the worst prognosis. As such, there is a critical need for noninvasive methods to determine GBM molecul...

The need for speed: using nystagmus velocity profiles and machine learning models to separate canalithiasis BPV from its mimics.

Journal of neurology
PURPOSE: Separating BPV from other positional nystagmus types can be challenging. We examined the utility of nystagmus slow-phase velocity (SPV) profiles when seeking to separate canalithiasis benign positional vertigo (BPV) from its mimics.