Anemia

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing predictive models identifying 'healthy' people at risk. MM patients from Clalit Health Services (2002-2019) were matched with healthy controls. Stage I: EHR from 5 years prior to MM diagnosis were reviewed and >200 parameters were compared (patients vs. ...

Stereotactic Body Radiation Therapy for Primary Renal Cancer and Genetic Markers of Response: A Phase 2 Trial.

Controlled outcome assessment of radiotherapy for primary renal cell carcinoma (RCC) remains limited, particularly regarding its impact on ipsilateral renal function and predictors of response. We evaluated oncological and renal function outcomes of stereotactic body radiation therapy (SBRT) for RCC and identified genomic predictors of response. Our study cohort comprised 83 surgically unfit patie...

Advanced molecular approaches to thalassemia disorder and the selection of molecular-level diagnostic testing in resource-limited settings.

Beta-thalassemia is a genetic disorder that significantly burdens healthcare systems globally. This inherited blood disorder, categorized into beta-th...

Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.

PURPOSE: This study aimed to evaluate radiomic models' ability to predict hypoxia-related biomarker expression in clear cell renal cell carcinoma (ccR...

Prognostic value of iron-metabolism biomarkers in critically ill patients with atrial fibrillation: a machine learning-based retrospective cohort study.

Iron homeostasis may significantly impact outcomes in critically ill patients with atrial fibrillation (AF), yet its prognostic value remains unclear....

Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study.

Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) is critical due to the distinct treat...

Advancing blood cell detection and classification: performance evaluation of modern deep learning models.

The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leuke...

Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.

BACKGROUND: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. T...

Integration of Metabolomics and Proteomics Reveals the Molecular Characterization of High-Altitude Hyperuricemia.

Hypobaric hypoxia-induced hyperuricemia (HUA) is a major health challenge for high-altitude populations, yet its molecular mechanisms remain poorly un...

A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.

The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these ne...

Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review.

Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem pr...

Identification and validation of hypoxia-associated genes positively correlated with in pulmonary adenocarcinoma.

BACKGROUND: Hypoxia contributes to the proliferation, migration, and chemotherapy resistance of lung adenocarcinoma (LUAD). This study aimed to identi...

An online data-driven method for predicting crack propagation and remaining fatigue life via combining linear and nonlinear ultrasonic.

Ultrasonic features play a critical role in evaluating the structural integrity of metallic components, yet current approaches predominantly rely on i...

The Implications and Predictability of Sleep Reversal for People with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Machine Learning Approach.

BACKGROUND/OBJECTIVES: Impaired sleep is one of the core symptoms of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), yet the mechanisms a...

Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.

In order to address the challenges posed by the large number of images acquired during wireless capsule endoscopy examinations and fatigue-induced lea...

Mechanisms underlying obesity-malignancy connection: a systematic narrative review.

The association between obesity and cancer risk carries substantial public health ramifications as obesity promotes cancer advancement via many cellul...

Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study.

OBJECTIVES: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remis...

Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders.

Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocognitive risk factors. Current multi-sensor diagnostic...

Digital Biomarkers and AI for Remote Monitoring of Fatigue Progression in Neurological Disorders: Bridging Mechanisms to Clinical Applications.

Digital biomarkers for fatigue monitoring in neurological disorders represent an innovative approach to bridge the gap between mechanistic understandi...

Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.

The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination i...