AIMC Topic: Humans

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Development and validation of a predictive model for refracture risk in elderly individuals with osteoporotic vertebral compression fracture: a retrospective study in China.

Aging clinical and experimental research
BACKGROUND: The rising incidence of refractures and associated adverse outcomes among individuals with osteoporotic vertebral compression fractures has gained significant attention. Identifying refracture risk is crucial for implementing effective pr...

Retrospective cohort study of infection and risk stratification using 6-year UBT data.

Frontiers in public health
BACKGROUND: () infection is a major global health concern, linked to gastric cancer and metabolic disorders. Despite its widespread prevalence, accurate risk stratification remains challenging. This study aims to develop a machine learning (ML)-base...

Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management.

Frontiers in endocrinology
Prediabetes represents an early stage of glucose metabolism disorder with significant public health implications. Although traditional lifestyle interventions have demonstrated some efficacy in preventing the progression to type 2 diabetes, their lim...

The development of predictive biomarkers and immunologic markers for breast cancer: current status and future perspectives.

Brazilian journal of biology = Revista brasleira de biologia
Breast cancer is the leading cause of cancer-related mortality among women worldwide. The development of predictive biomarkers and immunologic markers has revolutionized breast cancer diagnosis and treatment, enabling personalized medicine approaches...

MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data.

Journal of proteome research
MultiOmicsAgent (MOAgent) is an innovative, Python-based open-source tool for biomarker discovery, utilizing machine learning techniques, specifically extreme gradient-boosted decision trees, to process multiomics data. With its cross-platform compat...

MobNas ensembled model for breast cancer prediction.

Scientific reports
Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning ...

Prediction of reproductive and developmental toxicity using an attention and gate augmented graph convolutional network.

Scientific reports
Due to the diverse molecular structures of chemical compounds and their intricate biological pathways of toxicity, predicting their reproductive and developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship ...

Personalized learning in hybrid education.

Scientific reports
The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied l...

Sex-related differences and associated transcriptional signatures in the brain ventricular system and cerebrospinal fluid development in full-term neonates.

Biology of sex differences
BACKGROUND: The cerebrospinal fluid (CSF) is known to serve as a unique environment for neurodevelopment, with specific proteins secreted by epithelial cells of the choroid plexus (CP) playing crucial roles in cortical development and cell differenti...

A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

Journal of neuroengineering and rehabilitation
BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying ...