Hematology

Development and validation of a nomogram for predicting hemoptysis risk in patients with bronchiectasis: A study based on Random Forest algorithm.

Hemoptysis is a severe and potentially life-threatening complication of bronchiectasis. There is currently a lack of reliable tools for the individualized prediction of hemoptysis risk in these patients. This study aimed to utilize a machine learning algorithm to develop and validate a nomogram for predicting the risk of hemoptysis in patients with bronchiectasis. This retrospective study enrolled...

Infrared Spectroscopy for Determining Bone Graft Healing and Readiness in Tissue Models.

OBJECTIVE: Successful dental implant placement relies on the proper integration of bone graft material into an area of deficient native bone. Traditional radiographic methods provide only qualitative assessments, making it challenging to accurately determine graft maturation and the appropriate time for successful implant placement. This study investigates the potential of visible near-infrared (V...

Reconsidering Fibrinolysis in Selected Patients with Non-ST-Elevation Acute Coronary Syndrome: A Hypothesis-Generating, Research-Based Framework.

For decades, the use of fibrinolytic agents in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) has been contraindicated by major cli...

Oncology Education in Lithuania: An Integrated Curriculum at the Lithuanian University of Health Sciences.

Cancer remains a major health burden in Lithuania, emphasizing the importance of effective undergraduate oncology education. This paper describes the ...

Artificial intelligence shows comparable or improved performance to traditional risk models in predicting atrial fibrillation after cryptogenic stroke.

BACKGROUND AND AIMS: Detecting subclinical atrial fibrillation (AF) and initiating anticoagulation therapy are critical for secondary stroke preventio...

Integrative multi-omics and machine learning identify CHRNA1 putative circadian-immune hub in COPD.

BACKGROUND: Circadian rhythm disruption is increasingly recognized as a contributor to chronic inflammatory disorders; however, its specific significa...

Combined circulating tumor antigen model demonstrates additional prognostic value in first-line treatment of non-small cell lung cancer.

BACKGROUND: Circulating tumor antigens (ctA; tumor markers) are blood-based proteins that can offer prognostic value in non-small cell lung cancer (NS...

Multi-domain identification of myocardial infarction incidence using explainable AI: the overlooked role of periodontal health.

Myocardial infarction (MI) is a major global health concern influenced by diverse risk factors. Despite growing evidence of oral-systemic connections,...

Predictive biomarkers for immunotherapy response in renal cell carcinoma: why have they failed?

PURPOSE OF REVIEW: The heterogeneity of response to immunotherapies in renal cell carcinoma has created a strong need for predictive biomarkers to gui...

Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.

Blood smear examination involves classifying cells by morphology under a microscope, a labour-intensive process prone to subjective variation. Recent ...

Development of a machine learning-based noninvasive diagnostic model for liver fibrosis in metabolic-associated steatotic liver disease.

BACKGROUND: To address the lack of simple tools for assessing fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD), this study...

The use of artificial intelligence to predict postoperative outcomes following percutaneous nephrolithotomy: A systematic review of prognostic modeling studies.

Artificial intelligence (AI) is increasingly applied in clinical practice to enhance prediction of postoperative outcomes. This systematic review eval...

Stepwise increasing input to machine learning models predicting 30-day AMI or death in emergency department chest-pain patients.

Introduction This study evaluated the performance of neural network (NN) models with stepwise increasing input for identifying acute myocardial infarc...

Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records.

BACKGROUND: Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, externally validate, and prospe...

Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.

OBJECTIVE: To identify poor prognostic factors in Epstein-Barr virus (EBV)-positive systemic lupus erythematosus (SLE) using interpretable machine-lea...

Haematological pre-staging of colorectal cancer: Longitudinal trends identify high-risk metastatic phenotypes 24 months prior to diagnosis.

BACKGROUND: The latent systemic phase of colorectal cancer (CRC) offers a critical but often missed window for early intervention. This study evaluate...

Screening of potential targets of (2S)-2'-methoxykurarinone against sepsis and in vivo verification in zebrafish model.

OBJECTIVE: To screen the potential core targets of (2S)-2'-Methoxykurarinone, a dimethyldihydroflavonoid derived from Sophora flavescens, against seps...

Hematology-Oncology Fellows' Use of Artificial Intelligence: A Multicenter Educational Practice and Needs Assessment Survey.

Artificial Intelligence (AI) is rapidly being incorporated within healthcare, including medical education. Hematology-oncology (HO) is a complex field...

A wearable patch for continuous levodopa monitoring in sweat: Towards exertion and power-free pharmacodynamic assessment in Parkinson's disease.

Precision management of Parkinson's disease (PD) requires frequent levodopa (L-dopa) dose adjustments, yet current monitoring relies on subjective sym...

Soft, Multi-Wavelength Photoplethysmography Enables Reliable Neonatal Blood Pressure Monitoring Via Error Stratification.

Continuous noninvasive blood pressure monitoring is important for neonatal hemodynamic management. However, cuffless photoplethysmography (PPG) is sti...