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Precision Medicine for Stroke and Cerebrovascular Neurology
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Precision Medicine for Stroke and Cerebrovascular Neurology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 7653

Special Issue Editor

Dr. Wenjun Tu

E-Mail Website1 Website2
Guest Editor
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
Interests: stroke prevention, treatment and rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Stroke has become the leading cause of mortality and long-term impairment globally. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.

The era of precision medicine has arrived and conveys tremendous potential, particularly for stroke neurology. The guiding principles of precision medicine in stroke underscore the need to identify, value, organize, and analyze the multitude of variables obtained from each individual to generate a precise approach to optimize cerebrovascular health.

In this Special Issue of the Journal of Personalized Medicine, we will highlight the current state of the art and showcase some of the latest findings in the field of personalized stroke medicine. This involves the evaluation of biomarkers, expanded advanced risk factor analysis, micronutrient testing, risk-scoring systems, genetics, gene expression testing, and non-invasive and invasive testing, as well as research and debate about the effectiveness of personalized medicine as a more suitable tool for the prevention and treatment of cerebrovascular diseases than the traditional one-size-fits-all recommendations. In this Special Issue, we welcome original research and review articles related to the role of precision medicine in stroke and cerebrovascular neurology, from underlying molecular and cellular mechanisms to clinical translational applications.

Dr. Wenjun Tu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • precision medicine
  • stroke
  • structural imaging
  • functional imaging
  • algorithms

Published Papers (4 papers)

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Research

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11 pages, 243 KiB  
Article
Stroke and Risk Factors in Antiphospholipid Syndrome
by Yangyi Fan, Yicheng Xu, Sifan Zhang, Xiaodong Song, Zunjing Liu, Wenjun Tu and Chun Li
J. Pers. Med. 2024, 14(1), 24; https://doi.org/10.3390/jpm14010024 - 24 Dec 2023
Viewed by 815
Abstract
Stroke is considered one of the most common and life-threatening manifestations of antiphospholipid syndrome (APS), which leads to high mortality and permanent disability. This study investigated the prevalence and the potential risk factors of stroke in APS. We enrolled 361 APS patients retrospectively [...] Read more.
Stroke is considered one of the most common and life-threatening manifestations of antiphospholipid syndrome (APS), which leads to high mortality and permanent disability. This study investigated the prevalence and the potential risk factors of stroke in APS. We enrolled 361 APS patients retrospectively from 2009 to 2022 at Peking University People’s Hospital. Stroke was found in 25.8% (93/361) of the participants. The multivariate logistic regression showed that hypertension, diabetes, livedo reticularis, and other central nervous system involvements were significant related factors for stroke. The use of hydroxychloroquine appeared to relate to a lower incidence of stroke. During a median follow-up of 3.0 years, 11.8% (11/93) of the individuals with a previous stroke developed stroke recurrence, and thrombocytopenia seemed to be a predictor of stroke recurrence. Full article
(This article belongs to the Special Issue Precision Medicine for Stroke and Cerebrovascular Neurology)
16 pages, 1745 KiB  
Article
Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
by Ahmad A. Abujaber, Ibrahem Albalkhi, Yahia Imam, Abdulqadir J. Nashwan, Said Yaseen, Naveed Akhtar and Ibraheem M. Alkhawaldeh
J. Pers. Med. 2023, 13(11), 1555; https://doi.org/10.3390/jpm13111555 - 30 Oct 2023
Cited by 2 | Viewed by 1350
Abstract
(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry [...] Read more.
(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors. Full article
(This article belongs to the Special Issue Precision Medicine for Stroke and Cerebrovascular Neurology)
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15 pages, 5161 KiB  
Article
Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation
by Shaoguo Cui, Haojie Song, Huanhuan Ren, Xi Wang, Zheng Xie, Hao Wen and Yongmei Li
J. Pers. Med. 2022, 12(12), 2052; https://doi.org/10.3390/jpm12122052 - 12 Dec 2022
Cited by 1 | Viewed by 1256
Abstract
Hemorrhagic complication (HC) is the most severe complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). This study aimed to build a machine learning (ML) prediction model and an application system for a personalized analysis of the risk of HC [...] Read more.
Hemorrhagic complication (HC) is the most severe complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). This study aimed to build a machine learning (ML) prediction model and an application system for a personalized analysis of the risk of HC in patients undergoing IVT therapy. We included patients from Chongqing, Hainan and other centers, including Computed Tomography (CT) images, demographics, and other data, before the occurrence of HC. After feature engineering, a better feature subset was obtained, which was used to build a machine learning (ML) prediction model (Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB)), and then evaluated with relevant indicators. Finally, a prediction model with better performance was obtained. Based on this, an application system was built using the Flask framework. A total of 517 patients were included, of which 332 were in the training cohort, 83 were in the internal validation cohort, and 102 were in the external validation cohort. After evaluation, the performance of the XGB model is better, with an AUC of 0.9454 and ACC of 0.8554 on the internal validation cohort, and 0.9142 and ACC of 0.8431 on the external validation cohort. A total of 18 features were used to construct the model, including hemoglobin and fasting blood sugar. Furthermore, the validity of the model is demonstrated through decision curves. Subsequently, a system prototype is developed to verify the test prediction effect. The clinical decision support system (CDSS) embedded with the XGB model based on clinical data and image features can better carry out personalized analysis of the risk of HC in intravenous injection patients. Full article
(This article belongs to the Special Issue Precision Medicine for Stroke and Cerebrovascular Neurology)
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Review

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18 pages, 2536 KiB  
Review
Pulmonary Vascular Remodeling in Pulmonary Hypertension
by Zhuangzhuang Jia, Shuai Wang, Haifeng Yan, Yawen Cao, Xuan Zhang, Lin Wang, Zeyu Zhang, Shanshan Lin, Xianliang Wang and Jingyuan Mao
J. Pers. Med. 2023, 13(2), 366; https://doi.org/10.3390/jpm13020366 - 19 Feb 2023
Cited by 8 | Viewed by 3583
Abstract
Pulmonary vascular remodeling is the critical structural alteration and pathological feature in pulmonary hypertension (PH) and involves changes in the intima, media and adventitia. Pulmonary vascular remodeling consists of the proliferation and phenotypic transformation of pulmonary artery endothelial cells (PAECs) and pulmonary artery [...] Read more.
Pulmonary vascular remodeling is the critical structural alteration and pathological feature in pulmonary hypertension (PH) and involves changes in the intima, media and adventitia. Pulmonary vascular remodeling consists of the proliferation and phenotypic transformation of pulmonary artery endothelial cells (PAECs) and pulmonary artery smooth muscle cells (PASMCs) of the middle membranous pulmonary artery, as well as complex interactions involving external layer pulmonary artery fibroblasts (PAFs) and extracellular matrix (ECM). Inflammatory mechanisms, apoptosis and other factors in the vascular wall are influenced by different mechanisms that likely act in concert to drive disease progression. This article reviews these pathological changes and highlights some pathogenetic mechanisms involved in the remodeling process. Full article
(This article belongs to the Special Issue Precision Medicine for Stroke and Cerebrovascular Neurology)
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