Steroids

Integrated analysis implicates novel insights of NMB into lactate metabolism and immune response prediction in primary glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor in adults, exhibits profound treatment resistance and poor prognosis. Despite advances in immunotherapy for other cancers, GBM remains refractory, potentially due to its lactate-rich, immunosuppressive tumor microenvironment (TME). While aerobic glycolysis-driven lactate accumulation is known to acidify the TME and impair immune function,...

Machine Learning Ensemble Reveals Age-Specific Responses of Murine Mammary Tissue to Spaceflight With Relevance to Breast Cancer: An Observational Study

Spaceflight presents unique environmental stressors, such as microgravity and radiation, that significantly affect biological systems at the molecular, cellular, and organismal levels. Astronauts face an increased risk of developing cancer due to exposure to ionizing radiation and other spaceflight-related factors. Age plays a crucial role in the body’s response to the cellular stresses that lead ...

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor, has a median survival of approximately 15 months. Twenty percent of patients survive beyo...

Inter- and intra-individual differences in brain sex map to neuroendocrine profiles

Sex hormone fluctuations modulate structural and functional brain dynamics, yet little is known how sex steroid levels map onto the expression of sex ...

ROSIE-Enabled Spatial Mapping Reveals Architectural Fragmentation and Immune Reprogramming in Lung Adenocarcinoma Evolution

The progression of lung adenocarcinoma (LUAD) from precancerous lesions to invasive carcinoma entails extensive remodeling of tissue architecture and ...

Asymmetric Cross-Reactivity of Nuclear Receptors Reveals an Evolutionary Buffer Between Estrogen and Androgen Signaling

A comprehensive all-by-all receptor ligand affinity screen using Boltz-2, a deep learning framework for protein-ligand interaction prediction, reveals...

Large language models outperform traditional structured data-based approaches in identifying immunosuppressed patients

Identifying immunosuppressed patients using structured data can be challenging. Large language models effectively extract structured concepts from uns...

CT-based Osteoporosis Classification and Bone-Muscle Interaction Mapping Using Multiple Interpretable Machine Learning Models with the BMINet Framework

Osteoporosis progresses through stages characterized by declining bone mineral density, vertebral deterioration, and muscle atrophy, with bone-muscle ...

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders

Biomarker research in psychopathology increasingly employs high-dimensional omics approaches. Yet, proteomics based on human hair remain largely unexp...

Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality

Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) could be associated with fracture and mortality risk....

Deep learning clarifies association of osteoporosis risk with bone metastasis in premenopausal women after surgery for early-stage breast cancer: a multicenter retrospective cohort study

Adjuvant use of bone-modifying agents (BMAs) to early-stage breast cancer (eBC) aims to maintain bone density, leading to prevention of bone metastasi...

Plasma Cell-Free RNA Captures Immune Dynamics and Predicts GVHD after Hematopoietic Stem Cell Transplantation

Despite long-standing success of hematopoietic stem cell transplantation (HSCT) in the treatment of blood cancers and severe immune disorders, monitor...

Steroid Metabolome Profiling Identifies a Unique Androgen Hormone Signature Associated with Endometriosis

Endometriosis is a chronic, hormone-dependent condition that affects 190 million women worldwide. There are no validated biomarkers for endometriosis ...

Leveraging Machine Learning and Clinical Data to Predict Response to Intralesional Corticosteroids in Keloid Patients

Intralesional corticosteroid injections (ILCS) are a common treatment for keloid lesions; however, many patients exhibit resistance, and some experien...

Deep Learning-Based Opportunistic CT Osteoporosis Screening and Establishment of Normative Values

Osteoporosis is underdiagnosed and undertreated prompting the exploration of opportunistic screening using CT and artificial intelligence (AI). To dev...

Exploring Novel Biomarkers for Early Detection of Osteoporosis

Osteoporosis is characterized by diminished BMD and deteriorated bone microstructure, significantly increasing fracture susceptibility. This study lev...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depression

Steroid hormone profiles in affective disorders suggest hypothalamic– pituitary–adrenal (HPA) axis dysregulation and may reveal novel therapeutic targ...

Symbolic Regression for Mycophenolic Acid Dosage Prediction in Kidney Transplant Recipients

Chronic kidney disease (CKD) affects millions worldwide and often progresses to end-stage renal disease (ESRD), for which kidney transplantation remai...

Machine learning algorithm to predict fragility fractures and identification of important features – an explainable approach

In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites. We inve...

Predicting Rejection Risk in Heart Transplantation: An Integrated Clinical–Histopathologic Framework for Personalized Post-Transplant Care

Cardiac allograft rejection (CAR) remains the leading cause of early graft failure after heart transplantation (HT). Current diagnostics, including hi...