Hematology

A Multiomic Liquid Biopsy for the Earlier Detection of Colorectal Cancer.

UNLABELLED: Timely diagnosis and intervention in colorectal cancer are critical to improving patient outcomes and limiting disease progression. Screening of average-risk individuals is essential for detecting tumors at an earlier, more treatable stage. However, adherence to current screening programs remains suboptimal. Liquid biopsies represent a promising alternative to stool-based tests and may...

An optimized machine learning model based on hematological indicators for the noninvasive identification of baicalin's therapeutic effects in pulmonary hypertension.

Pulmonary hypertension (PH) is a progressive cardiopulmonary disorder with high mortality, necessitating non-invasive methods for early detection and treatment evaluation. In this paper, this study proposes a novel machine learning model for non-invasively identifying the therapeutic effects of Baicalin in PH using routine hematological indicators. The core innovation is an enhanced Bat Algorithm ...

Early recurrence prediction and risk stratification of hepatocellular carcinoma after transarterial chemoembolization achieving imaging complete response based on contrast-enhanced CT machine learning.

OBJECTIVES: To develop and validate machine learning (ML) models using clinical and contrast-enhanced CT (CECT) parameters to assess recurrence risk i...

Integrated CTC Enrichment and Dual-Responsive Nanoprobe Identification Enable Intelligent Liquid Biopsy-Based Cancer Diagnosis.

This work addresses the challenge of accurately identifying living circulating tumor cell (CTC) from contaminating leukocytes by developing a novel, f...

TCR sequencing in cancer immunology and immunotherapy: what, when, where, why, and how.

T-cell receptors (TCRs) are generated through somatic recombination of variable (V), diversity (D), and joining (J) gene segments, resulting in an ext...

Proteomics-based machine learning model for predicting secondary infection in HBV-related liver failure.

Patients with Hepatitis B Virus-related liver failure are highly vulnerable to secondary infections (SI), yet early predictive tools remain limited. I...

Pharmacological stabilization of hypoxia-inducible factor 1-α dampens the interferon response and promotes glycolysis in Aicardi-Goutières syndrome.

Aicardi-Goutières syndrome (AGS) is a genetic type I interferon (IFN)-mediated disease characterized by neurological involvement with onset in utero o...

Circulating tumor cells (CTCs) enumeration and machine-learning based diagnostic biomarkers for breast cancer detection.

BACKGROUND: Circulating tumor cells (CTCs) are detectable in early-stage cancer and may enable early cancer detection. We evaluated a CTC-based assay ...

Predicting short-term mortality in severe cirrhosis: An interpretable machine learning model integrating routine clinical indicators.

BACKGROUND: The critical need for precise risk stratification in severe liver cirrhosis is underscored by its substantial 30-day mortality rates, dema...

Rapid Diagnosis of Bacteremia in Febrile Pediatric Oncology Patients via Host-Response Infrared Spectroscopy of Blood and Machine Learning.

Bacteremia is a life-threatening complication and a leading cause of sepsis and septic shock in patients. Conventional diagnostic methods, such as blo...

M[Formula: see text]DGAT: Multi-view multi-scale dynamic graph attention network(GAT) based prediction of Parkinson's disease(PD) progression using whole-blood RNA sequencing data.

With emerging single-cell transcriptomics data, deep learning approaches have enabled the diagnosis of neurodegenerative disorders such as Parkinson's...

Single capture quantitative oblique back-illumination microscopy.

Quantitative oblique back-illumination microscopy (qOBM) has emerged as a powerful technique for label-free, 3D quantitative phase imaging of arbitrar...

Explainable machine learning predicts mortality in critically ill patients with nonvariceal upper gastrointestinal bleeding: a MIMIC-IV study with external validation.

Characterization of the Sequence of Cytopenia after Total-body Irradiation using Unsupervised Machine Learning.

Hematopoietic acute radiation syndrome (H-ARS) elicits multidimensional effects, as total-body irradiation (TBI) induced myelosuppression results in d...

Advanced Drug Delivery Strategies for Overcoming Biological Barriers: Tumor Microenvironment and Blood-Brain Barrier.

Therapeutic efficacy for malignancies and neurological disorders is fundamentally restricted by biological barriers, particularly the complex tumor mi...

Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers.

PURPOSE: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic s...

CHOROIDAL VASCULARITY INDEX, RETINAL VASCULARITY, AND HEMOGLOBIN LEVELS IN PEDIATRIC SICKLE CELL MACULOPATHY.

PURPOSE: To evaluate the choroidal vascularity index (CVI) in pediatric patients with sickle cell disease (SCD) and its associations with retinal thic...

A predictive model for significant periodontal disease progression: A large-scale cohort study.

BACKGROUND: The progression of periodontitis is challenging to predict. This study aimed to develop and validate a machine learning model to identify ...

Machine Learning-Driven Prognostic Model Integrating Lymphocyte-to-C-Reactive Protein Ratio and TNM Staging in Gallbladder Cancer.

BACKGROUND: A comprehensive preoperative assessment of the patient's physical condition is crucial for predicting the prognosis of patients undergoing...

Machine Learning in Assessing Intraoperative Blood Loss: A Systematic Review and Meta-Analysis.

AIM: To evaluate the value of machine learning in assessing intraoperative blood loss by comparing associated outcomes with those of the gold standard...