Hematology

How machine learning on real world clinical data improves adverse event recording for endoscopy.

Endoscopic interventions are essential for diagnosing and treating gastrointestinal conditions. Accurate and comprehensive documentation is crucial for enhancing patient safety and optimizing clinical outcomes; however, adverse events remain underreported. This study evaluates a machine learning-based approach for systematically detecting endoscopic adverse events from real-world clinical metadata...

Recent advancement in endoscopic diagnosis for risk stratification of gastric cancer.

Approximately 90% of cases of gastric cancer (GC) are caused by Helicobacter pylori infection, and screening esophagogastroduodenoscopy is effective for secondary prevention of GC. Endoscopic findings of the stomach due to H. pylori infection vary widely, and the risk of GC varies according to each finding. GC risk is evaluated by combining endoscopic and histopathological findings. In the operati...

Inhibiting heme piracy by pathogenic Escherichia coli using de novo-designed proteins.

Iron is an essential nutrient for most bacteria and is often growth-limiting during infection, due to the host sequestering free iron as part of the i...

Advantages and effectiveness of AI three-dimensional reconstruction technology in the preoperative planning of total hip arthroplasty.

In order to explore the application effect of artificial intelligence (AI) 3D reconstruction technology in total hip arthroplasty (THA), this study in...

Prediction and characterisation of the human B cell response to a heterologous two-dose Ebola vaccine.

Ebola virus disease (EVD) outbreaks are increasing, posing significant threats to affected communities. Effective outbreak management depends on prote...

Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model.

Intravenous immunoglobulin (IVIG) has been established as the first-line therapy for Kawasaki disease (KD). However, approximately 10%-20% of pediatri...

Predicting SARS-CoV-2-specific CD4 and CD8 T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models.

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune response...

Identifying propionate metabolism-related genes as biomarkers of sepsis development and therapeutic targets.

The treatment of sepsis is challenging due to unclear mechanisms. Propionate is increasingly seen as critical to sepsis pathophysiology by bridging gu...

Recent advances of engineering cell membranes for nanomedicine delivery across the blood-brain barrier.

The blood-brain barrier (BBB) poses a major challenge to the effective delivery of therapeutic agents for the treatment of central nervous system (CNS...

From imaging to computational domains for physics-driven molecular biology simulations: Hindered diffusion in platelet masses.

When formed in vivo, murine hemostatic thrombi exhibit a heterogeneous architecture comprised of distinct regions of densely and sparsely packed plate...

Multiple omics-based machine learning reveals peripheral blood immune cell landscape during acute rejection of kidney transplantation and constructs a precise non-invasive diagnostic strategy.

Kidney transplantation is the optimal treatment for end-stage renal disease (ESRD), but acute rejection (AR) remains a major factor affecting graft su...

Multi-omics analysis of the effects of pla2g4a on the prognosis of various cancers and its experimental validation in breast cancer cell lines.

BACKGROUND: Platelet-related exosomes (PREs) are microparticles secreted by platelets into the bloodstream and are implicated in various cancer proces...

A comprehensive targeted panel of 295 genes: Unveiling key disease initiating and transformative biomarkers in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a hematological malignancy that progresses from a benign precursor stage known as Monoclonal Gammopathy of Undete...

Development of a Risk Prediction Model for Linezolid-Induced Thrombocytopenia Based on the Machine Learning Algorithm.

The research aimed to develop a validated model for predicting the risk of linezolid-induced thrombocytopenia (LIT). An XGBoost model and SelectFromMo...

Femtosecond laser-assisted printing of hard magnetic microrobots for swimming upstream in subcentimeter-per-second blood flow.

Magnetic microrobots have garnered increasing attention in the biomedical field due to their wireless motion control and noninvasive therapeutic poten...

Identifying features of prior hemorrhage in cerebral cavernous malformations on quantitative susceptibility maps: a machine learning pilot study.

Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often disappear after several weeks, yet the risk of reble...

Machine learning-based prediction of G-CSF-induced hematopoietic stem cell mobilization outcomes in healthy volunteers.

Haematopoietic stem cell transplantation (HSC-T) has been established as a fundamental therapeutic intervention for a wide range of hematological mali...

Tracing the evolutionary pathway of SARS-CoV-2 through RNA sequencing analysis.

The COVID-19 pandemic, driven by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has underscored the need to understand the virus's ...

eNCApsulate: neural cellular automata for precision diagnosis on capsule endoscopes.

PURPOSE: Wireless capsule endoscopy (WCE) is a noninvasive imaging method for the entire gastrointestinal tract and is a pain-free alternative to trad...

Glycoproteomics Analysis to Identify Potential Biomarkers and Investigate CD14 Monocyte Activation Mechanisms via Exosomal Proteins in Cerebral Infarction Using a Novel Glycogen-Functionalized Nanoprobe.

Cerebral infarction (CI) is a leading cause of disability and mortality, with activated plasma monocytes and altered protein glycosylation identified ...