Hematology

Heavy metal exposure and dental caries in children and adolescents: a NHANES-based analysis integrating machine learning approaches.

BACKGROUND: Dental caries remains a major public health concern among children and adolescents. While established risk factors such as diet and oral hygiene are well recognized, the association between environmental heavy metal exposure and pediatric dental caries is not fully understood. This study examined the relationship between blood heavy metal concentrations and dental caries in a nationall...

Identifying mitochondria-related signatures for tuberculosis diagnosis through machine learning on single-cell transcriptomics data and experimental verification.

Tuberculosis (TB) remains a major cause of infectious disease mortality. Early diagnosis is crucial for curbing transmission and initiating timely treatment. However, the lack of reliable non-sputum-based diagnostic tools often delays prompt detection. Since mitochondrial dysregulation facilitates Mycobacterium tuberculosis (MTB) evasion, we explored mitochondria-related gene signatures as diagnos...

Predicting blood levels of mercury and selenium in Amazonian riverines: A machine learning approach based on questionnaire data.

This study aims to develop and evaluate machine learning models to predict blood levels of mercury and selenium in Brazilian riverines. Blood samples ...

Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification.

Atherosclerosis, a major cause of adverse cardiovascular events and mortality rates worldwide, stems from sustained lipid accumulation and subsequent ...

Platelet-Rich Plasma Devices in Dermatology.

Platelet-rich plasma (PRP) has become an important therapeutic tool in dermatology, with expanding applications in both medical and cosmetic practice....

Clinical Translatable and Transparency of Neural Network for Blood-Based Personalized Cancer Risk.

OBJECTIVE: To support cancer screening and identify precancerous conditions, such as atypical hyperplasia, to improve cure rates and reduce mortality,...

Thrombosis after surgical splenectomy - why, in whom and can we prevent it?

Splenectomy remains a common operation performed in the setting of trauma, haematological disease, malignancy and diagnostic purposes. Contemporary ev...

Effect of a mobile health intervention optimised with artificial intelligence on blood pressure and other cardiovascular risk factors in adults with high blood pressure: rationale and design of My Intelligent Cardiac Assistant (MICArdiac) randomised controlled trial.

INTRODUCTION: Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message inte...

Co-occurrence patterns of malnutrition indicators among children in sub-Saharan Africa.

BACKGROUND: Despite progress towards SDG2, malnutrition remains a major concern. Little is known about co-occurrence patterns of nutritional deficits,...

Ischaemia in tuberculous meningitis: update on pathophysiology, clinical predictors and management.

PURPOSE OF REVIEW: Tuberculous meningitis (TBM) is a severe manifestation of Mycobacterium tuberculosis infection, associated with high mortality and ...

Digital SERS Nanoprobe Immunoassay With Single-Particle Sensing Resolution for Detection of Pancreatic Cancer Biomarkers From Clinical Blood Samples.

Surface-enhanced Raman scattering (SERS) nanoprobe-based immunoassay is an emerging liquid biopsy modality for the detection of blood biomarkers. Howe...

Machine Learning in Predicting the Risk of Esophagogastric Variceal Bleeding Among Patients With Liver Cirrhosis: Systematic Review and Meta-Analysis.

BACKGROUND: Liver cirrhosis (LC) can lead to several complications. Esophageal variceal bleeding (EVB) and esophagogastric variceal bleeding (EGVB) ar...

Towards noninvasive blood count using a deep learning pipeline from bulbar conjunctiva videos.

Invasive venous blood draws remain the clinical standard for hematology, yet they are invasive, time-consuming, and costly. We introduce Video-to-Vess...

Explainable machine learning for coagulant dosage prediction in coal port wastewater treatment.

Coal-laden runoff at coal ports, if inadequately treated before discharge, can compromise nearshore water quality. As the primary process for treating...

A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a major complication in the intensive care unit (ICU), and early risk stratification remains challen...

Enhancing prediabetes and diabetes detection through a machine learning-enabled self-assessment approach.

OBJECTIVES: Reliable, accessible, noninvasive self-assessment screening for prediabetes/diabetes is lacking, leading to missed opportunities for early...

Transcranial Doppler ultrasonography in geriatric anesthesia: moving toward personalized brain protection strategies.

The increasing volume of geriatric surgical procedures presents a critical challenge: protecting the aging brain from perioperative complications such...

mHealth-Enabled Stroke Screening for Pediatric Sickle Cell Disease in Low-Resource Settings: Systematic Literature Review of Critical Barriers, Emerging Technologies, and AI-Driven Solutions.

BACKGROUND: Sickle cell disease (SCD) is a genetic blood disorder affecting millions globally, with life-threatening complications, and most patients ...

Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation.

Cerebral blood flow is essential for brain function and is governed by cerebral perfusion pressure and physiological mechanisms collectively referred ...

Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnos...