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Related Experiment Video

Updated: Dec 5, 2025

Using a Cell-Tracer Injection to Investigate the Origin of Neointima-Forming Cells in a Rat Saccular Side Wall Model

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Published on: March 16, 2022

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

Kerry E Poppenberg^1,2, Vincent M Tutino^1,3,2,4, Lu Li⁵

¹Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY, 14214, USA.

Journal of Translational Medicine

|October 16, 2020

Summary

We developed a new machine learning model using neutrophil gene expression to predict intracranial aneurysms (IAs) with high accuracy. This advancement improves early detection of IAs, potentially preventing rupture.

Keywords:

Inflammation Intracranial aneurysm Machine learning Neutrophil Prediction model Transcriptomics

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Using a Cell-Tracer Injection to Investigate the Origin of Neointima-Forming Cells in a Rat Saccular Side Wall Model

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Published on: March 16, 2022

Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI

Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI

Published on: December 16, 2021

Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Intracranial aneurysms (IAs) pose a rupture risk.
Previous work identified RNA expression differences in neutrophils for IA prediction.
Machine learning models were trained using 40 neutrophil transcriptomes.

Purpose of the Study:

Develop an improved predictive model for unruptured intracranial aneurysms (IAs).
Utilize neutrophil transcriptomes from a larger patient population.
Employ more robust machine learning methodologies.

Main Methods:

RNA sequencing of neutrophil samples from 134 patients (55 IA, 79 controls).
Least Absolute Shrinkage and Selection Operator (LASSO) for transcript selection.
Four supervised machine learning algorithms (KNN, Random Forest, SVM) were used for model construction and testing.
Real-time quantitative PCR (RT-qPCR) validated gene expression in a subset of samples.

Main Results:

LASSO identified 37 IA-associated transcripts.
The predictive models achieved up to 90% accuracy in the testing cohort.
Average Area Under the ROC Curve (AUC) was 0.97, an improvement over prior models.
Random Forest demonstrated the best performance.
RT-qPCR confirmed expression differences in 7 of 9 tested genes.
Gene ontology analysis indicated dysregulated inflammation, cell signaling, and apoptosis.

Conclusions:

Enhanced IA prediction models were developed using larger sample sizes and advanced machine learning.
Neutrophil transcriptomes show promise for non-invasive IA detection.
Further validation in larger cohorts is recommended to confirm model efficacy and explore covariate effects.