Varicella zoster virus Vasculopathy as a Rare Cause of Stroke

Şimşek, Fatma; Eren, Alper; Kizildağ, Nazım

doi:10.1590/0037-8682-0215-2023

Cited by 2 publications

(1 citation statement)

References 2 publications

(3 reference statements)

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“…The role of laboratory parameters, including biochemical and serologic biomarkers, are explored for better stroke management. 85 , 86 Modified Rankin scale (mRS) is a clinical tool that assess the functional status of the patient. 87 It is a 5-point scale with worsening a status while the score is increasing.…”

Section: Clinical and Research Applicationsmentioning

confidence: 99%

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Koska,

Selver

2024

The Eurasian Journal of Medicine

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The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are important components of these chronic conditions. Stroke is the acute cessation of blood in the brain, which can lead to rapid tissue loss. Therefore, fast, accurate, and reliable automatic methods are required to facilitate stroke management. The performance of artificial intelligence (AI) methods is increasing in all domains. Vision tasks, including natural images and medical images, are particularly benefiting from the skills of AI models. The AI methods that can be applied to stroke imaging have a broad range, including classical machine learning tools such as support vector machines, random forests, logistic regression, and linear discriminant analysis, as well as deep learning models, such as convolutional neural networks, recurrent neural networks, autoencoders, and U-Net. Both tools can be applied to various aspects of stroke management, including time-to-event onset determination, stroke confirmation, large vessel occlusion detection, diffusion restriction, perfusion deficit, core and penumbra identification, affected region segmentation, and functional outcome prediction. While building these AI models, maximum care should be exercised in order to reduce bias and build generalizable models. One of the most important prerequisites for building unbiased models is collecting large, diverse, and quality data that reflects the underlying population well and splitting the training and testing parts in a way that both represent a similar distribution. Explainability and trustworthiness are other important properties of machine learning models that could be widely adopted in clinical practices.

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Section: Clinical and Research Applicationsmentioning

confidence: 99%

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Koska,

Selver

2024

The Eurasian Journal of Medicine

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Analysis of clinical characteristics of central nervous system varicella-zoster virus infection: A Single-Center Cases Series Study

Li,

Yu,

Fan

et al. 2024

Preprint

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Backgroud: To retrospectively analyze the clinical characteristics, laboratory examination, electroencephalogram, imaging findings, treatment and prognosis of central nervous system varicella-zoster virus (VZV) infection confirmed by metagenomic next-generation sequencing (mNGS), and to improve the early recognition of the disease. Methods: Patients with VZVencephalitis/meningitis or spinal meningitis diagnosed by mNGS of cerebrospinal fluid (CSF) in the Department of Neurology of the Second Hospital of Hebei Medical University from July 2018 to June 2021 were enrolled. The clinical characteristics, laboratory examination, electroencephalogram, imaging examination, diagnosis, treatment and prognosis were collected and analyzed. Results: All the 29 patients had acute onset, and the main clinical manifestations were headache, fever, cranial nerve damage, disturbance of consciousness, limb numbness and weakness, convulsion, etc. There were 8 cases of meningitis, 18 cases of meningoencephalitis, 2 cases of cerebrospinal meningitis and 1 case of myelomeningitis. The pressure of lumbar puncture was normal in most cases. The white blood cell count and protein content in CSF were increased remarkable. The abnormal rate of electroencephalogram (EEG) was low, and abnormal imaging could be found in severe cases. Most of the patients can obtain a good prognosis, but those with cranial nerve involvement, vasculitis or spinal cord injury often have poor prognosis. Conclusions: The clinical manifestations of VZV infection in central nervous system are nonspecific, and cranial nerves are prone to be involved. CSF parameters are diverse, which is easy to be misdiagnosed. CSF cytology can provide valuable clue for clinical diagnosis.

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Varicella zoster virus Vasculopathy as a Rare Cause of Stroke

Cited by 2 publications

References 2 publications

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Analysis of clinical characteristics of central nervous system varicella-zoster virus infection: A Single-Center Cases Series Study

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