Nan Ding¹, Xingyu Zeng^2,*, Jianping Wu³, Liutao Zhao³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5299-5315, 2025, DOI:10.32604/cmc.2025.064822 - 30 July 2025

Abstract Predicting the health status of stroke patients at different stages of the disease is a critical clinical task. The onset and development of stroke are affected by an array of factors, encompassing genetic predisposition, environmental exposure, unhealthy lifestyle habits, and existing medical conditions. Although existing machine learning-based methods for predicting stroke patients’ health status have made significant progress, limitations remain in terms of prediction accuracy, model explainability, and system optimization. This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence (XAI) for predicting the health status of stroke patients. First, we design a More >

Chayut Bunterngchit¹, Laith H. Baniata², Mohammad H. Baniata³, Ashraf ALDabbas⁴, Mohannad A. Khair⁵, Thanaphon Chearanai⁶, Sangwoo Kang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 517-536, 2025, DOI:10.32604/cmc.2025.060368 - 26 March 2025

Abstract Stroke is a leading cause of death and disability worldwide, significantly impairing motor and cognitive functions. Effective rehabilitation is often hindered by the heterogeneity of stroke lesions, variability in recovery patterns, and the complexity of electroencephalography (EEG) signals, which are often contaminated by artifacts. Accurate classification of motor imagery (MI) tasks, involving the mental simulation of movements, is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability. To address these challenges, this study introduces a graph-attentive convolutional long short-term memory (LSTM) network (GACL-Net), a novel hybrid deep learning model… More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Long Qi¹, Chaoran Wu¹, Hao Sun^1,2,*, Hong Liao^1,*

BIOCELL, Vol.49, No.2, pp. 167-180, 2025, DOI:10.32604/biocell.2025.059159 - 28 February 2025

Abstract Glutamate is an essential excitatory neurotransmitter in the brain, playing a vital role in regulating synaptic activity and maintaining the homeostasis of the cerebral environment but also serves as a central hub for neuronal injury and inflammatory responses. In various pathological conditions, such as ischemic stroke, glutamate is released and accumulates excessively in the brain, leading to heightened stimulation of neurons and excitotoxicity. This phenomenon positions glutamate as a primary inducing factor for neuronal damage following cerebral ischemia. Glutamate exerts its effects primarily through two types of receptors: ionotropic and metabotropic glutamate receptors, both of… More >

ELMIRA YAKUPOVA¹, VALENTINA BABENKO^1,2, ALEXEY BOCHARNIKOV¹, KSENIYA FEDULOVA¹, DENIS SILACHEV^1,2, EGOR PLOTNIKOV^1,2,*

BIOCELL, Vol.48, No.12, pp. 1827-1834, 2024, DOI:10.32604/biocell.2024.058496 - 30 December 2024

Abstract Background: The active use of stem and progenitor cells in the therapy of various diseases requires the development of approaches for targeted modification of their properties. One such approach is the induction of a pro- or anti-inflammatory phenotype. Methods: In this study, we investigated the effect of a pro-inflammatory environment in vitro on multipotent mesenchymal stromal cells (MSC) by incubation with lipopolysaccharide (LPS). iCELLigence real-time cell analysis system was used for monitoring cell culture growth. Cell energy metabolism was assessed using the Seahorse XFp Analyzer. For the rat stroke experiment, we used a photoinduced thrombosis (PT)… More >

Suzhe Wang^*, Xueying Zhang, Fenglian Li, Zelin Wu

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1177-1196, 2024, DOI:10.32604/cmc.2024.056016 - 15 October 2024

Abstract Early and timely diagnosis of stroke is critical for effective treatment, and the electroencephalogram (EEG) offers a low-cost, non-invasive solution. However, the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning. To address this issue, our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention (PCGAN-EASA), which incrementally improves the quality of generated EEG features. This network can yield full-scale, fine-grained EEG features from the low-scale, coarse ones. Specially, to overcome the limitations of traditional generative models… More >

MIN SHEN^1,2, XIAOMAN XU^1,2, GUANGLING SUN¹, LIANGZHU WANG¹, TAO YING¹, HANG SU¹, WEI WANG¹, QINGHUA CAO^1,*, ZHEZHE SUN^1,*

BIOCELL, Vol.48, No.3, pp. 491-499, 2024, DOI:10.32604/biocell.2023.047640 - 15 March 2024

Abstract Objective: Circular RNAs (circRNAs) have been shown to involve in pathological processes of ischemic stroke (IS), including autophagy. This study was designed to explore the effect of circR-ZC3HC1 on neuronal autophagy in IS and the related mechanisms. Methods: Expression of circR-ZC3HC1 in blood samples of IS patients and healthy controls was detected. Hippocampal neurons were treated with oxygen and glucose deprivation (OGD) to establish IS in vitro model. The expression of LC3 and p62 and the number of autophagosomes were examined to evaluate the autophagy level of OGD induced neurons using western blotting and transmission electron… More >

Congjun Rao¹, Mengxi Li¹, Tingting Huang^2,*, Feiyu Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898 - 30 December 2023

Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline… More >

Zuo-Hua Li¹, Qing-Gui Wu^1,*, Jun Teng^1,*, Chao-Jun Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 865-884, 2024, DOI:10.32604/cmes.2023.029927 - 22 September 2023

Abstract Collisions between a moving mass and an anti-collision device increase structural responses and threaten structural safety. An active mass damper (AMD) with stroke limitations is often used to avoid collisions. However, a stroke-limited AMD control system with a fixed limited area shortens the available AMD stroke and leads to significant control power. To solve this problem, the design approach with variable gain and limited area (VGLA) is proposed in this study. First, the boundary of variable-limited areas is calculated based on the real-time status of the moving mass. The variable gain (VG) expression at the More >

Mesfer Al Duhayyim^1,*, Sidra Abbas^2,*, Abdullah Al Hejaili³, Natalia Kryvinska⁴, Ahmad Almadhor⁵, Uzma Ghulam Mohammad⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 413-429, 2023, DOI:10.32604/csse.2023.037127 - 26 May 2023

Abstract Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this… More >