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Potential application value of xenon in stroke treatment.

ABSTRACT: Stroke is an acute disease with extremely high mortality and disability, including ischemic stroke and hemorrhagic stroke. Currently only limited drugs and treatments have been shown to have neuroprotective effects in stroke. As a medical gas, xenon has been proven to have neuroprotective effect in considerable amount of previous study. Its unique properties are different from other neuroprotective agents, making it is promising to play a special therapeutic role in stroke, either alone or in combination with other treatments. In this article, we aim to review the role of xenon in the treatment of stroke, and summarize the mechanism of using xenon to produce therapeutic effects after stroke according to the existing research. Moreover, we intend to explore and demonstrate the feasibility and safety of xenon for clinical treatment of stroke. Despite the disadvantages of difficulty in obtaining and being expensive, as long as the use of reasonable methods, xenon can play an important role in the treatment of stroke.

SUBMITTER: Zhao CS

PROVIDER: S-EPMC6178644 | biostudies-literature | 2018 Jul-Sep

REPOSITORIES: biostudies-literature

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Potential application value of xenon in stroke treatment.

Zhao Chong-Shun CS Li Hao H Wang Zhong Z Chen Gang G

Medical gas research 20180701 3

Stroke is an acute disease with extremely high mortality and disability, including ischemic stroke and hemorrhagic stroke. Currently only limited drugs and treatments have been shown to have neuroprotective effects in stroke. As a medical gas, xenon has been proven to have neuroprotective effect in considerable amount of previous study. Its unique properties are different from other neuroprotective agents, making it is promising to play a special therapeutic role in stroke, either alone or in co ...[more]

PMID: 30319767

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Project description:Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ? 2b) and functional independence (modified Rankin Scale ?2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

Project description:This was a case-control study conducted from August 2020 to January 2021, in which 9 ischemic stroke and 2 healthy individuals (healthy control group) attending the Guangdong Provincial People's Hospital were recruited for plasma exoLRs sequencing. The other 10 normal samples from a previous study published in the Gene Expression Omnibu (GEO) database (Accession No. GSE159657) were used as part of healthy control group to analyzed the expression profile together. 30 ischemic stroke patients and 30 healthy individuals were also recruited for validation of the expression of exoLRs. Ischemic stroke diagnosis was performed according to World Health Organization guidelines and validated using computed tomography or magnetic resonance imaging. The ischemic stroke must have lasted not more 48 hours prior to recruitment. The participants ranged between 18–75 years old and were from either gender. To be included in this study, healthy individuals had to have displayed normal renal and liver function, and with no history of smoking, malignancy, recent cardiovascular or cerebrovascular events, rheumatologic disorders, chronic heart failure, diabetes, acute or chronic infectious disease, aortic dissection, pulmonary embolism, myocarditis, pericarditis or congenital heart disease.Blood samples were collected immediately at admission before thrombolytic therapy. For each patient, 2 ml of venous blood was collected in ethylene diamine tetraacetic acid routine blood tubes. In order to extract plasma, the blood was centrifuged at 3000×g for 10 min at 4 °C and the supernatant was carefully transferred to a new tube for repeated centrifugation. Then the plasma was stored in cryogenic vials at −80 °C till further use. Total RNA in exosomal vesicles was extracted using the exoRNeasy Serum/Plasma kit (Qiagen), following the manufacturer’s protocol. The RNA-seq libraries were constructed using SMART technology (Clontech). RNA sequencing was performed by the illumine Nova-Seq 6000 System.

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Potential application value of xenon in stroke treatment.

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Potential application value of xenon in stroke treatment.

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