Menopause

Machine learning for predicting and optimizing the performance of a commercial-scale anaerobic digester with diverse feedstocks and operating conditions.

The improper disposal of food waste and livestock manure poses significant environmental risks, including nutrient pollution, water contamination, and greenhouse gas emissions. Anaerobic digestion (AD) provides a sustainable pathway for converting organic waste into biogas while reducing environmental impacts. However, optimizing AD performance at a commercial scale remains challenging due to feed...

Quantitative CT Imaging in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations. Efforts to quantify its various components using CT imaging have advanced, aiming for more precise, objective, and reproducible assessment and management. Beyond emphysema and small airway disease, the two major components of COPD, CT quantification...

Discovery of novel cathepsin K inhibitors for osteoporosis treatment using a deep learning-based strategy.

BACKGROUND: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeut...

Data-driven approach to the deep learning of the dynamics of a non-integrable Hamiltonian system.

The dynamics of non-integrable Hamiltonian systems, described by area-preserving mappings, are regulated by the KAM theorem. This states that the phas...

Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.

Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates nume...

LncRNAs regulates cell death in osteosarcoma.

Despite improvements, prognosis in osteosarcoma patients remains poor, making it essential to identify additional and more robust therapeutic targets....

Driving Multifunctional Nanomedicine Design for Non-Inflammatory Tumor Therapy with Integrated Machine Learning and Density Functional Theory.

Due to the promotive role of inflammation in tumor progression, designing multifunctional nanomedicines that synergistically combine anti-tumor and an...

Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of n...

Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.

Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing ...

Formulation of low temperature mixed mode crack propagation behavior of crumb rubber modified HMA using artificial intelligence.

Determining mixed mode fracture parameters asphalt concrete mixtures remains an engineering challenge due to non-homogeneity and inelasticity of the m...

Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.

OBJECTIVE: This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approa...

Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.

OBJECTIVES: The composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an import...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tu...

ERRα-Predictor: A Framework of Ensemble Models for Prediction of ERRα Binders, Antagonists, and Agonists Using Artificial Intelligence.

Estrogen-related receptor α (ERRα) is considered a promising target for the treatment of cancer and metabolic diseases. The development of comprehensi...

Novel Deep Learning Model to Estimate Knee Flexion and Adduction Moments with Wearable IMUs during Treadmill and Overground Walking.

A major issue after total knee replacement (TKR) surgery is asymmetric gait kinetics, which increases knee loads on the non-operated knee. This imbala...

Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis.

High-throughput screening and computational models have been effective in predicting chemical interactions with estrogen and androgen receptors, but s...

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategi...

Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis.

Osteoporosis is widespread with a high incidence rate, resulting in fragility fractures which are a major contributor to mortality among the elderly. ...

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.

Nasal polyps (NP) are benign mucosal outgrowths associated with chronic inflammation that can significantly reduce quality of life. This study aimed t...