AIMC Topic: Male

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Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development.

Reproductive biology and endocrinology : RB&E
BACKGROUND: The application of Artificial Intelligence (AI) to sperm selection during Intracytoplasmic Sperm Injection (ICSI) procedures represents one of the most innovative advances in assisted reproductive technology (ART). Traditional sperm selec...

Integrated Metabolomics and Lipidomics of Tissue and Serum Reveal Mechanistic Pathways and Lipid Signatures Distinguishing Meningioma Grades.

Journal of proteome research
Meningioma, the most prevalent primary intracranial tumor, presents significant clinical challenges due to unclear molecular mechanisms underlying its progression from low-grade (LG) to high-grade (HG) and lack of grade-specific biomarkers. Here, we ...

Simulating human well-being with large language models: Systematic validation and misestimation across 64,000 individuals from 64 countries.

Proceedings of the National Academy of Sciences of the United States of America
Subjective well-being is central to economic, medical, and policy decision-making. We evaluate whether large language models (LLMs) can provide valid predictions of well-being across global populations. Using natural-language profiles from 64,000 ind...

Beyond swimming: emerging parameters for predicting the fertility of mouse spermatozoa.

Lab animal
Cryopreservation of spermatozoa can be used as a cost-effective way of preserving the ever-increasing number of genetically modified mouse lines. Nevertheless, discontinuing the breeding of a line or strain is only warranted after the quality control...

Association of the dietary index for gut microbiota with metabolic syndrome and its components combining interpretable machine learning algorithms.

Journal of health, population, and nutrition
BACKGROUND: Previous studies have emphasized the critical role of diet and gut microbiome in Metabolic syndrome (MetS). The dietary index for gut microbiota (DI-GM) represents a novel dietary index that effectively reflects the diversity of gut micro...

Functional archetypes in the human gut microbiome reveal metabolic diversity, stability, and influence disease-associated signatures.

Microbiome
BACKGROUND: Understanding the functional diversity of the gut microbiome is critical for elucidating its roles in human health and disease. While traditional approaches focus on taxonomic composition, functional configurations of the microbiome remai...

Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.

European journal of medical research
BACKGROUND: Delirium frequently complicates elderly chronic kidney disease (CKD) patients due to multifactorial vulnerability. Early detection in geriatric intensive care unit (ICU) settings is challenged by traditional assessments' communication def...

Knowledge-level comparison in pulpal and periapical diseases: dental students versus artificial intelligence models (Gemini, Microsoft Copilot, ChatGPT-3.5, ChatGPT-4o): cross-sectional study.

BMC medical education
BACKGROUND: This study explored the diagnostic accuracy of artificial intelligence (AI) chatbots and dental students when responding to questions related to pulpal and periapical diseases. Rapid advancements in AI have led to increased interest in th...

The relationship between amyloid-β peptide spectrum and the spastic paraparesis phenotype in autosomal dominant Alzheimer's disease.

Alzheimer's research & therapy
BACKGROUND: More than 300 mutations in presenilin 1 (PSEN1) lead to autosomal dominant Alzheimer's disease (ADAD). PSEN1, as the catalytic subunit of γ-secretase, generates amyloid-β (Aβ) peptides through a sequential proteolysis of the amyloid precu...

Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography.

BMC medicine
BACKGROUND: Coronary computed tomography angiography (CCTA) is widely used as a first-line tool for diagnosing and managing coronary artery disease (CAD), and machine learning (ML)-based analysis shows promise for quantitative CAD assessment.