Yang Liu, Qi Lu, Junjie Wu, Huaichang Yin, Shiwei Cheng^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070273 - 12 January 2026

Abstract The development of brain-computer interfaces (BCI) based on motor imagery (MI) has greatly improved patients’ quality of life with movement disorders. The classification of upper limb MI has been widely studied and applied in many fields, including rehabilitation. However, the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex, making it difficult to distinguish their features. Therefore, classifying lower limbs motor imagery is more challenging. In this study, we propose a feature extraction method based on functional connectivity, which utilizes phase-locked values to construct a… More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Virna Margarita Martín Giménez¹, SebastiáN GarcíA MenéNdez^2,3, Luiz Gustavo A. Chuffa⁴, Vinicius Augusto SimãO⁴, Russel J. Reiter⁵, Ramaswamy Sharma⁶, Walter Balduini⁷, Carla Gentile⁸, Walter Manucha^2,3,*

BIOCELL, Vol.49, No.12, pp. 2245-2282, 2025, DOI:10.32604/biocell.2025.071830 - 24 December 2025

Abstract Neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS) are characterized by progressive neuronal loss, which is closely linked to mitochondrial dysfunction. These pathologies involve a complex interplay of genetics, protein misfolding, and cellular stress, culminating in impaired energy metabolism, an increase in reactive oxygen species (ROS), and defective mitochondrial quality control. The accumulation of damaged mitochondria and dysregulation of pathways such as the Integrated Stress Response (ISR) are central to the pathogenesis of these conditions. This review explores the critical relationship between mitochondrial stress… More >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

Yang Xu^1,#, Yaqi Zhang^2,#, Meijiao Zhu³, Pengcheng Xue⁴, Siyu Ma¹, Di Yu¹, Liang Hu¹, Yuxi Zhang¹, Wei Peng¹, Jirong Qi¹, Xuyun Wen⁴, Ming Yang³, Xuming Mo^1,2,5,*

Congenital Heart Disease, Vol.20, No.5, pp. 559-570, 2025, DOI:10.32604/chd.2025.072242 - 30 November 2025

Abstract Background: The life-course management of children with tetralogy of Fallot (TOF) has focused on demonstrating brain structural alterations, developmental trajectories, and cognition-related changes that unfold over time. Methods: We introduce an magnetic resonance imaging (MRI) dataset comprising TOF children who underwent brain MRI scanning and cross-sectional neurocognitive follow-up. The dataset includes brain three-dimensional T1-weighted imaging (3D-T1WI), three-dimensional T2-weighted imaging (3D-T2WI), and neurodevelopmental evaluations using the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition (WPPSI-IV). Results: Thirty-one children with TOF (age range: 4–33 months; 18 males) were recruited and completed corrective surgery at the Children’s Hospital of Nanjing More >

Abdu Salam¹, Mohammad Abrar², Raja Waseem Anwer³, Farhan Amin^4,*, Faizan Ullah⁵, Isabel de la Torre^6,*, Gerardo Mendez Mezquita⁷, Henry Fabian Gongora⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2457-2479, 2025, DOI:10.32604/cmes.2025.072765 - 26 November 2025

Abstract Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations. Due to the increase in precision image-based diagnostic tools, driven by advancements in artificial intelligence (AI) and deep learning, there has been potential to improve diagnostic accuracy, especially with Magnetic Resonance Imaging (MRI). However, traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation. Thus, our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model. The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification. The proposed model… More >

Tomoki Sekimori^1,*, Ichiro Kawahata^2,*

BIOCELL, Vol.49, No.11, pp. 2125-2136, 2025, DOI:10.32604/biocell.2025.070394 - 24 November 2025

Abstract Lewy body diseases (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB), are neurodegenerative disorders characterized by the intracellular aggregation and accumulation of α-Synuclein (αSyn), leading to neuronal death. Although these diseases primarily present with symptoms affecting the central nervous system (CNS), such as motor and cognitive impairment, increasing research suggests that their roots may be found in the gut. This review summarizes recent findings and key historical insights into the involvement of the gut in αSyn pathology. The topics covered include pathological observations in patients with LBD, animal models investigating the propagation More >

Sudhakar Tummala^1,2,*, Sajjad Hussain Chauhdary³, Vikash Singh⁴, Roshan Kumar⁵, Seifedine Kadry⁶, Jungeun Kim^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731 - 30 September 2025

Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

Rosa Scarpitta^1,#, Emiliano Cappello^1,#, Alice Cangialosi¹, Veronica Gori¹, Giulia De Luca^1,2, Giovanni Gori³, Guido Bocci^1,*

Oncology Research, Vol.33, No.10, pp. 2603-2656, 2025, DOI:10.32604/or.2025.066260 - 26 September 2025

Abstract Central nervous system (CNS) tumors are the most common solid tumors in pediatric patients and the leading cause of childhood cancer-related mortality. Their rarity compared to adult cancers has made enrolling sufficient cases for clinical trials challenging. Consequently, pediatric CNS tumors were long treated with adult protocols despite distinct biological and clinical characteristics. This review examines key aspects of phase I pediatric oncology trials, including study design, primary outcomes, and pharmacological approaches, along with secondary considerations like clinical responses and ethical aspects. Firstly, we evaluated all phase I trial protocols focusing on pediatric CNS tumors… More > Graphic Abstract