KARAN WADHWA¹, PAYAL CHAUHAN¹, SHOBHIT KUMAR², RAKESH PAHWA^3,*, RAVINDER VERMA⁴, RAJAT GOYAL⁵, GOVIND SINGH¹, ARCHANA SHARMA⁶, NEHA RAO³, DEEPAK KAUSHIK^1,*

Oncology Research, Vol.32, No.5, pp. 877-897, 2024, DOI:10.32604/or.2024.047278

Abstract Background: Glioblastoma multiforme (GBM) is recognized as the most lethal and most highly invasive tumor. The high likelihood of treatment failure arises from the presence of the blood-brain barrier (BBB) and stem cells around GBM, which avert the entry of chemotherapeutic drugs into the tumor mass. Objective: Recently, several researchers have designed novel nanocarrier systems like liposomes, dendrimers, metallic nanoparticles, nanodiamonds, and nanorobot approaches, allowing drugs to infiltrate the BBB more efficiently, opening up innovative avenues to prevail over therapy problems and radiation therapy. Methods: Relevant literature for this manuscript has been collected from a comprehensive and systematic search of… More > Graphic Abstract

Jiadong Wu, Yinan Wang^*, Zhiwei Li^*, Lun Lu, Qingjiang Li

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2909-2939, 2024, DOI:10.32604/cmc.2024.047240

Abstract Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance and practicability compared with the… More >

M. Adimoolam¹, K. Maithili², N. M. Balamurugan³, R. Rajkumar⁴, S. Leelavathy⁵, Raju Kannadasan⁶, Mohd Anul Haq^7,*, Ilyas Khan⁸, ElSayed M. Tag El Din⁹, Arfat Ahmad Khan¹⁰

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009

Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using… More >

PEI QIN^#, MIAO LIU^#, XIN WANG, JIANHUA MA^*

BIOCELL, Vol.48, No.3, pp. 513-523, 2024, DOI:10.32604/biocell.2023.045640

Abstract Background: L-dopa (Levodopa) is well known for managing PD (Parkinson’s disease); however, its prolonged use caused dyskinesia (LID). Due to the varied presentation of LID, effective treatment options are scarce. Flavonoids reported their neuroprotective activity by ameliorating acetylcholinesterase, monoamine oxidase, and neuroinflammation. Kaempferol is another flavonoid bearing these potentials. Aim: To evaluate neuroprotective activity of kaempferol in dyskinetic rats. Methods: PD was developed in Sprague-Dawley rats by injecting combination of L-ascorbic acid (10 µL) + 6-OHDA (12 µg) in medial forebrain bundle to induce neuronal damage in substantial nigra (SNr). LID was induced by administrating combination of L-dopa (20 mg/kg)… More > Graphic Abstract

PING YANG^1,2,3, YUANXIANG LAN^1,2, ZHONG ZENG^1,2, YAN WANG^1,2, HECHUN XIA^1,2,*

BIOCELL, Vol.48, No.1, pp. 149-162, 2024, DOI:10.32604/biocell.2023.042367

Abstract Background: As a form of biological therapy, placenta-derived mesenchymal stem cells (PDMSCs) exhibit considerable promise in addressing the complex pathological processes of traumaticbrain injury (TBI) due to their multi-target and multi-pathway mode of action. Material & Methods: This study investigates the protective mechanisms and benefits of PDMSCs in mitigating the effects of controlled cortical impact (CCI) in rats and glutamate-induced oxidative stress injury in HT22 cells in vitro. Our primary objective is to provide evidence supporting the clinical application of PDMSCs. Results: In the in vivo arm of our investigation, we observed a swift elevation of matrix metalloproteinase-9 (MMP-9) in… More > Graphic Abstract

Juhong Tie, Hui Peng^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 741-757, 2024, DOI:10.32604/cmes.2023.043353

Abstract Accurate automatic segmentation of gliomas in various sub-regions, including peritumoral edema, necrotic core, and enhancing and non-enhancing tumor core from 3D multimodal MRI images, is challenging because of its highly heterogeneous appearance and shape. Deep convolution neural networks (CNNs) have recently improved glioma segmentation performance. However, extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution, resulting in the loss of accurate spatial and object parts information, especially information on the small sub-region tumors, affecting segmentation performance. Hence, this paper proposes a novel multi-level parallel network comprising three different level parallel sub-networks to fully… More >

S. Kannan^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465

Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. With… More >

YINGDONG ZHANG^1,2, MING CHU^1,2,*, HONGZHOU LU^3,*

BIOCELL, Vol.47, No.12, pp. 2591-2595, 2023, DOI:10.32604/biocell.2023.030331

Abstract Human immunodeficiency virus (HIV) infection of the central nervous system (CNS) has attracted significant attention because it contributes to severe complications of acquired immunodeficiency syndrome (AIDS) and seriously impairs the life quality of infected patients. In this review, we briefly describe the latent infection of HIV in CNS and focus on the role of the important immune cells, such as T cells, in the formation and maintenance of the HIV reservoir in CNS. This review explores the mechanisms by which T cells enter CNS and establish latent infection of HIV in the CNS. In conclusion, we summarize the role of… More > Graphic Abstract

Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687

Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research applies several data augmentation techniques… More >

Mohammed Aly^1,*, Abdullah Shawan Alotaibi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 557-582, 2023, DOI:10.32604/cmc.2023.042493

Abstract Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection.… More >