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Steroids

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Severe community-acquired pneumonia (sCAP): advances in management and future directions.

Severe community-acquired pneumonia (sCAP) is a major global health challenge, with high morbidity a...

Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.

Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging...

Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records.

BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and...

Predicting treatment outcome in congenital adrenal hyperplasia using urine steroidomics and machine learning.

OBJECTIVE: Treatment monitoring of individuals with congenital adrenal hyperplasia (CAH) remains uns...

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasur...

Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and inf...

Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data.

Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated ...

Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution.

The pharmaceutical sector is aware of supercritical CO (SC-CO) as a possible replacement for problem...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is...

Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.

This study systematically examined the impact of three feature selection techniques (Boruta, Extreme...

Molecular Insights into the Binding of Organophosphate Flame Retardant Key Metabolites with Mineralocorticoid and Estrogen Receptors.

Flame retardants (FR) encompass a wide range of chemicals designed to inhibit and reduce the spread ...

Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study.

Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Da...

Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory.

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and ...

Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.

BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods...

AI-Enabled Exoskeletal Robotics for Enhancing Mobility, Bone Regeneration, and Functional Rehabilitation in Osteoporosis: A Literature Review.

The care of osteoporosis is being revolutionized by developments in AI-enabled exoskeletal robotics,...

Fusogenic Lipid Nanovesicles as Multifunctional Immunomodulatory Platforms for Precision Solid Tumor Therapy.

Although immunotherapy demonstrates considerable prospect in overcoming solid tumors, its clinical e...

Dendritic cell-based microrobots for enhanced systemic antigen-specific immune tolerance.

Current immunotherapeutic approaches for autoimmune disorders primarily rely on the use of generaliz...

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