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Ischemic Stroke l: Introduction

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.

Ischemic Stroke ll: Pathophysiology

Ischemic Stroke ll: Pathophysiology

An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...

Hemorrhagic Stroke l: Introduction

Hemorrhagic Stroke l: Introduction

A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...

Hemorrhagic Stroke ll: Pathophysiology

Hemorrhagic Stroke ll: Pathophysiology

A hemorrhagic stroke develops when a cerebral blood vessel ruptures, allowing blood to escape into the surrounding brain tissue, as in intracerebral hemorrhage (ICH), or into the subarachnoid space, as in subarachnoid hemorrhage (SAH). Because the skull is a rigid compartment, the sudden presence of extravascular blood rapidly increases intracranial pressure and compresses adjacent neural structures, leading to immediate tissue injury and impaired cerebral perfusion.Mass Effect and Primary...

Transient Ischemic Attack l: Introduction

Transient Ischemic Attack l: Introduction

A transient ischemic attack (TIA) is a brief episode of neurological dysfunction caused by a temporary, focal reduction in cerebral blood flow. Although symptoms resemble those of an ischemic stroke, the interruption in perfusion is short-lived and does not cause permanent infarction. TIAs are clinically important because they often serve as early warning events for future stroke.Mechanisms of Transient Cerebral IschemiaTransient cerebral ischemia may arise through several mechanisms. One...

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning.

Haijie Liu¹, Liping Hou², Shanhu Xu³

¹Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.

Frontiers in Genetics

|September 20, 2021

Summary

This study introduces a novel framework using network representation learning to identify genes linked to cerebral ischemic stroke (IS). The method improves accuracy in predicting IS-related genes for better diagnosis and treatment.

Keywords:

PPI network cerebral ischemic stroke disease gene prediction network embedding network representation learning

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Cerebral ischemic stroke (IS) is a complex condition influenced by vascular, genetic, and environmental factors, complicating the identification of disease-related genes.
Current gene prediction methods for IS often rely on guilt-by-association (GBA), which may not fully utilize the global structure of protein-protein interaction (PPI) networks.

Purpose of the Study:

To develop and validate a novel framework for identifying genes associated with cerebral ischemic stroke (IS).
To improve the accuracy of IS-related gene prediction by incorporating network topology and advanced machine learning techniques.

Main Methods:

The framework captures PPI network topology using network representation learning (NRL).
Gene features are denoised using a stacked autoencoder (SAE).
A support vector machine (SVM) classifier is optimized for IS-related gene identification.

Main Results:

The proposed framework demonstrates superior accuracy in predicting IS-related genes compared to existing methods.
A case study confirmed the framework's capability to effectively identify IS-related genes.

Conclusions:

The novel NRL-based framework offers a more accurate approach to identifying genes implicated in cerebral ischemic stroke.
This advancement holds potential for improving the understanding, diagnosis, and clinical treatment of IS.