Hematology

ANGPTL4 in gestational diabetes: a diagnostic biomarker linked to placental senescence.

Current diagnostic methods for Gestational Diabetes Mellitus (GDM) inadequately reflect early placental pathophysiology. Accelerated cellular senescence in trophoblasts has been reported as a pathological feature associated with the GDM placenta. However, the potential of senescence-related genes (SRGs) as candidate biomarkers or molecular indicators for GDM has not been systematically investigate...

Can artificial intelligence lead the next frontier of hemophilia management and care?

Predicting gingival embrasure risk after invisible orthodontics using multimodal data and machine learning.

OBJECTIVE: To develop and validate a risk prediction model for gingival embrasures after clear aligner therapy using multimodal oral data. METHODS: A ...

Preoperative PET/CT phenotype of gastric-type endocervical adenocarcinoma: integrated morphological, metabolic, serological, and explainable machine-learning analysis.

PURPOSE: Gastric-type endocervical adenocarcinoma (GAS) is an aggressive, non-HPV-associated cervical adenocarcinoma that is often difficult to recogn...

clinTALL: machine learning-driven multimodal subtype classification and treatment outcome prediction in pediatric T-ALL.

BACKGROUND: Childhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-c...

Prognostic value of nutritional and inflammatory biomarkers in patients with non-small cell lung cancer and bone metastasis: A retrospective study.

To evaluate the prognostic significance of nutritional-inflammatory biomarkers in non-small cell lung cancer (NSCLC) patients with bone metastases. Th...

Hybrid circular-square complementary split-ring resonator sensor for monitoring abnormal blood protein levels for disease diagnosis.

The proposed work aims to develop a compact, low-cost, and commercially viable microwave sensor for early monitoring of high protein content in blood,...

Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.

This study aimed to develop an interpretable machine learning-based scoring system for predicting sepsis and septic shock among febrile patients at em...

Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.

Atrial fibrillation (AF) is the most prevalent sustained arrhythmia worldwide. Acute myocardial infarction (AMI) is closely intertwined with AF throug...

An Interpretable Machine Learning Model for Predicting the Presence of Talaromycosis in HIV Patients Lacking Skin Lesions.

INTRODUCTION: The existing predictive models for talaromycosis in people living with HIV without skin lesions are limited by established risk factors ...

Advances in predictive factors for pathological complete response after neoadjuvant therapy in locally advanced rectal cancer.

For locally advanced rectal cancer (LARC), neoadjuvant chemoradiotherapy (nCRT) combined with total mesorectal excision has emerged as the standard th...

Association of preoperative monocyte level with postoperative delirium in patients receiving cardiac surgery under propofol-based anesthesia: a retrospective study.

BACKGROUND: Postoperative delirium is a common complication after cardiac surgery, and perioperative inflammation may contribute to its development. H...

Development of a Screening Model for Exercise-Induced Desaturation by Machine Learning Method.

BACKGROUND: Exercise-induced desaturation (EID) during the 6-minute walk test (6MWT) is an established marker of adverse outcomes in patients with chr...

AI-assisted decision support for sickle cell disease severity stratification using routine blood tests: a systematic review and meta-analysis.

BACKGROUND: Stratifying sickle cell disease (SCD) severity remains challenging, particularly in resource-limited settings. Artificial intelligence (AI...

A feature-efficient dual-task machine learning framework for predicting bone mineral density and osteoporosis stratification in resource-constrained environments.

Osteoporosis is a chronic skeletal disorder characterized by progressive bone mineral density (BMD) loss and structural deterioration, significantly i...

Model evaluation for automated scoring of electropenetrography waveform data from mosquitoes.

Electropenetrography (EPG) is a powerful tool for quantifying how arthropods interact with their hosts, but manually labeling EPG feeding behavior wav...

A clinically interpretable model for predicting pharyngocutaneous fistula after total laryngectomy.

BACKGROUND: Pharyngocutaneous fistula (PCF) is a frequent complication following total laryngectomy. While various risk models exist, their practical ...

Development and temporal validation of an interpretable XGBoost model for predicting cardiovascular death in patients with diabetic foot.

Patients with diabetic foot (DF) have a high risk of cardiovascular (CV) death, yet dedicated risk-prediction tools for this population are lacking. W...

Mechanistic study on comorbidities of kidney stones and sepsis using transcriptome sequencing combined with single-cell spatial transcriptome sequencing.

Kidney stones and sepsis have a complex pathological association. Urinary obstruction and infection caused by kidney stones can easily induce sepsis, ...

Predicting severe COVID-19 in elderly patients using routine laboratory indicators: Diagnostic accuracy of machine learning models.

To enable early warning of severe 2019 coronavirus disease in elderly patients, this study collected routine laboratory indicators and clinical parame...