Steroids

Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology.

IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical heterogeneity results in divergent renal outcomes, making early identification of high-risk patients essential. Prognostic models are crucial for stratifying ESKD risk, guiding treatment intensity, optimizing timing of interventions such as immunosu...

Machine Learning Models for Posttransplant Lymphoproliferative Disorder (PTLD) Risk Prediction in Thoracic Transplantation.

Posttransplant lymphoproliferative disorder (PTLD) is the second most common malignancy in thoracic transplant recipients and is associated with poor survival. Accurate risk prediction is essential for prevention and management. Adult (≥18 years) heart, lung, and heart-lung transplant recipients were extracted from the Scientific Registry of Transplant (SRTR) and the United Network of Organ Sharin...

Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is one of the most prevalent and lethal gastrointestinal malignancies. MutS homolog 2 (MSH2), a DNA mismatch repair pr...

Auditory and Vestibular Recovery in Otosclerosis Surgery: Clinical Efficacy and Predictive Modeling of Intraoperative Corticosteroid Use.

OBJECTIVE: To evaluate the effect of intraoperative local corticosteroid application on hearing, tinnitus, and dizziness outcomes after stapes surgery...

Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning.

Open-Source Machine Learning Computed Tomography Scan Segmentation for Spine Osteoporosis Diagnostics.

BACKGROUND AND OBJECTIVES: Dual-energy x-ray absorptiometry (DXA) is the standard for assessing bone mineral density (BMD); however, its accuracy is l...

Synergistic impact of Gram-negative bacterial co-infection on invasive pulmonary aspergillosis in non-neutropenic patients: a machine learning-based risk stratification approach.

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, where coexisting bacterial infections, part...

Extracellular vesicles in colorectal cancer: immunomodulation, diagnostics, and therapeutic perspectives.

Colorectal cancer (CRC) remains a significant global health concern and is among the leading causes of cancer-related mortality. The disease often pro...

Large-scale proteomic profiling identifies distinct inflammatory phenotypes in acute respiratory distress syndrome: a multicentre, prospective cohort study.

BACKGROUND: Host responses during acute respiratory distress syndrome are highly heterogeneous, contributing to inconsistent therapeutic outcomes. Pro...

Optimizing vedolizumab therapy in ulcerative colitis: A critical synthesis of trial evidence and the emerging role of artificial intelligence.

BACKGROUND: Vedolizumab, a monoclonal antibody targeting the α4β7 integrin, offers gut-selective immunosuppression and represents a cornerstone biolog...

Association of urinary heavy metals with osteoporosis in US adults using interpretable machine learning.

BACKGROUND: Exposure to heavy metals in the environment has always been the focus of public concern. More and more evidence suggests that heavy metal ...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are o...

Identification of patients at risk for adverse events and poor symptom improvement after transcatheter aortic valve implantation.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) aims to improve symptoms and prognosis, while minimising adverse outcomes. Available predic...

Contrasting land-use sources of microplastic and endocrine-disrupting compound pollution in a major coastal river network.

Environmental monitoring in coastal megacities prioritizes land-based microplastics (MPs) and endocrine-disrupting compounds (EDCs) as key threats to ...

Integrating network toxicology, machine learning, and molecular dynamics simulations to reveal tanshinone iia's dual mechanisms in TNBC and doxorubicin-induced cardiotoxicity.

Doxorubicin (Dox)-induced cardiotoxicity remains a critical barrier to optimizing breast cancer (BC) treatment, highlighting the urgent need to dissec...

Pubertal Hormones and the Early Adolescent Female Brain: A Multimodality Brain MRI Study.

Puberty is a critical developmental process that is associated with changes in pubertal (or steroid) hormone levels, which are believed to influence a...

Comprehensive tumour-immune profiling reveals TREM2+ tumour-associated macrophages facilitating lymph node metastasis in head and neck squamous cell carcinoma.

BACKGROUND: Lymph node (LN) metastasis is a well-established independent prognostic factor in head and neck squamous cell carcinoma (HNSCC). Formation...

Development of an individualized prediction model for dynamic adaptations in performance and immune function associated with dietary patterns in endurance athletes using machine learning.

BACKGROUND: Psychological resilience significantly influences immune function and health outcomes in high-stress populations, yet mechanisms underlyin...

Machine learning mortality prediction model for cyclosporine therapy in pediatric aplastic anemia.

The outcomes of children with aplastic anemia receiving cyclosporine monotherapy vary significantly in terms of mortality risk; therefore, a prognosti...

Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.

Artificial intelligence (AI) is increasingly transforming health care, particularly in solid organ transplantation, where it addresses complex challen...