Hematology

Decoding neuron-specific lineage to identify diagnostic biomarkers and therapeutic targets for ischemic stroke.

Ischemic stroke (IS) imposes a major global health burden. To uncover new diagnostic and therapeutic targets, we profiled neuronal heterogeneity during IS using single-cell RNA sequencing. Our analysis decoded neuronal lineage trajectories, identified a critical cell-cell communication network, and pinpointed key gene modules. By integrating multiple machine learning algorithms, we constructed a h...

An interpretable machine learning model for preoperative risk stratification in paediatric congenital heart disease surgery: a retrospective cohort study.

BACKGROUND: Congenital heart disease (CHD), one of the most common birth defects, poses challenges to preoperative risk stratification due to its anatomical complexity and developmental vulnerability. Existing tools inadequately predict critical outcomes, including mortality and ventilator dependence. We developed a machine learning-based clinical tool to enable precise preoperative risk assessmen...

Machine learning versus clinical scoring systems for predicting poor wound healing after posterior surgery for thoracolumbar tuberculosis.

OBJECTIVE: Postoperative poor wound healing (PWH) is a significant complication following posterior surgery for thoracolumbar tuberculosis, leading to...

Development, validation, and user-centric evaluation of an interpretable machine learning decision support tool for the preoperative prediction of mild bleeding disorders (MBD-Check): a prospective diagnostic prediction study.

BACKGROUND: Mild bleeding disorders are the most common inherited bleeding disorders, often leading to perioperative haemorrhages. Preoperative screen...

Tissueformer: extending single-cell foundation models to predict population-level phenotypes.

BACKGROUND: Single-cell RNA sequencing technologies have enabled unprecedented insights into gene expression and opened new pathways for diagnostics a...

A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.

BACKGROUND: Heart failure is not only a prevalent disease with a high mortality rate, but also generates high costs for healthcare systems. By trainin...

Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data.

BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is currently a lack of precise early prediction models. T...

Fluorescence spectroscopy and machine learning methods for detection of Alzheimer's disease from circulating white blood cells.

BackgroundAlzheimer's disease (AD) is the most common cause of dementia whose prevalence is projected to increase significantly in the coming decades....

Single-cell sequencing in systemic lupus erythematosus.

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by heterogeneous clinical manifestations and multi-organ in...

Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.

BACKGROUND: Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of...

Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.

BACKGROUND: Accurate real-time prediction of blood glucose (BG) levels is essential for improving insulin-dosing decision support systems, including c...

An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.

Acute Lymphoblastic Leukemia (ALL) is one of the most aggressive hematological malignancies, and its early diagnosis remains challenging due to non-sp...

Non-destructive detection of internal egg defects: Transmission imaging for blood spots and Vis-NIR spectroscopy for runny yolk.

Ensuring the internal quality of eggs is essential for food safety and industrial-scale grading. While current systems can detect blood spots, non-des...

Development and external validation of the HCH and HPMS prognostic indices for sepsis: a retrospective model development study using a Multi-Objective Non-Newtonian Fluid optimization algorithm.

BACKGROUND: The pathological heterogeneity of sepsis makes it challenging for traditional scoring systems to balance early-warning sensitivity, dynami...

Development and early feasibility testing of machine-learning algorithms to non-invasively assess hemoglobin levels.

The HeMonitor study evaluated the feasibility and accuracy of non-invasive hemoglobin (Hb) assessment using image-based techniques and machine learnin...

Synthetic dataset of PLGA and liposome nanocarrier formulations for brain-cancer-relevant drug delivery, release, blood-brain-barrier transport, and paired cell-viability proxies.

This data article describes an original synthetic/simulated dataset designed to support materials-informatics and comparative formulation analysis of ...

Early Prediction of Diabetic Macular Edema via Machine Learning Survival Analysis on Checkup Data.

PURPOSE: To predict the risk of diabetic macular edema (DME) onset and to identify features of the risk subgroups. DESIGN: Population-based observatio...

Landscape of T-cell bispecific antibodies in cancer therapy: therapeutic strategies, challenges and future prospection.

T-cell bispecific antibodies (TCBs), a specialized subclass of bispecific antibodies (BsAbs), are engineered to simultaneously engage T cells and tumo...

Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives.

Previously, we reported a dual combination based on 4-hydroxycoumarin and dodecanedioic acid that could synergistically bind to human serum albumin (H...

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...