Ravita Chahar, Ashutosh Kumar Dubey^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-65, 2026, DOI:10.32604/cmc.2025.066990 - 10 November 2025

Abstract Objective: The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods. Conditions such as anxiety, depression, stress, bipolar disorder (BD), and autism spectrum disorder (ASD) frequently arise from the complex interplay of demographic, biological, and socioeconomic factors, resulting in aggravated symptoms. This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions. Methods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025. The potential… More >

Hasan Jhaveri^*, Mariela Martinez-Rivera, Brent Nose, Jordan Foreman, Aaron C. Lentz

Canadian Journal of Urology, Vol.32, No.6, pp. 597-603, 2025, DOI:10.32604/cju.2025.068095 - 30 December 2025

Abstract Objectives: There is limited in vivo data on the maximum safe instrument size that can be passed through an artificial urinary sphincter (AUS) cuff. While 21 French instruments are generally safe with the commonly used 4.5 cm cuff, larger instruments or smaller cuffs may require unbuckling to avoid urethral erosion. This study aimed to identify if artificial urinary sphincter cuff ‘unbuckling’ affects device longevity and risk of erosion. Methods: A retrospective study of patients at a quaternary health system who underwent unbuckling was conducted. Using the Epic Clarity database and Duke Enterprise Data Unified Content Explorer… More >

Jun He¹, Jianli Luo¹, Yanling Wang^1,*, Dai Zhou^1,*, Shuanglin Xiang^2,*

Congenital Heart Disease, Vol.20, No.5, pp. 613-623, 2025, DOI:10.32604/chd.2025.071773 - 30 November 2025

Abstract Pediatric congenital heart disease (CHD) pharmacotherapy faces three fundamental barriers: developmental pharmacokinetic complexity, anatomic-genetic heterogeneity, and evidence chain gaps. Traditional agents exhibit critical limitations: digoxin’s narrow therapeutic index (0.5–0.9 ng/mL) is exacerbated by ABCB1 mutations (toxicity risk increases 4.1-fold), furosemide efficacy declines by 35% in neonates due to NKCC2 immaturity, and β-blocker responses vary by CYP2D6 polymorphisms (poor metabolizers require 50–75% dose reduction). Novel strategies demonstrate transformative potential—CRISPR editing achieves 81% reversal of BMPR2-associated pulmonary vascular remodeling, metabolically matured cardiac organoids replicate adult myocardial energy metabolism for drug screening, and SGLT2 inhibitors activate triple mechanisms (calcium overload More >

Dawa Chyophel Lepcha^1,*, Bhawna Goyal^2,3, Ayush Dogra⁴, Ahmed Alkhayyat⁵, Prabhat Kumar Sahu⁶, Aaliya Ali⁷, Vinay Kukreja⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1487-1573, 2025, DOI:10.32604/cmes.2025.070964 - 26 November 2025

Abstract Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the… More >

Jin Wang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011894

Abstract Shape-memory elastomers (SMEs) have emerged as promising smart-materials platforms for soft actuators and intelligent structures due to their programmable thermally-induced reversible shape transformations. However, four critical scientific and technological challenges impede their practical engineering implementation. First, the thermodynamic and molecular mechanisms governing their thermomechanical behavior remain incompletely elucidated. Second, achieving large reversible deformations requires retention of molecular orientation during thermal actuation cycles- a persistent challenge given their large strain recovery at the heating temperature. Third, while biological muscles achieve sub-second actuation, current SME systems exhibit response times spanning several seconds, necessitating at least one order More >

Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4407-4440, 2025, DOI:10.32604/cmc.2025.070689 - 23 October 2025

Abstract Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus More >

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Hiba Sameer Saeed^#, Amenah Dahim Abbood^#,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4375-4405, 2025, DOI:10.32604/cmc.2025.067783 - 23 October 2025

Abstract In recent years, the evolution of the community structure in social networks has gained significant attention. Due to the rapid and continuous evolution of real-world networks over time. This makes the process of identifying communities and tracking their topology changes challenging. To tackle these challenges, it is necessary to find efficient methodologies for analyzing the behavior patterns of dynamic communities. Several previous reviews have introduced algorithms and models for community detection. However, these methods have not been very accurate in identifying communities. Moreover, none of the reviewed papers made an apparent effort to link algorithms… More >

Robyn Lesch¹, Evan David Visser¹, Ntalane Sello Seroka^1,2,*, Lindiwe Khotseng^1,*

Journal of Renewable Materials, Vol.13, No.10, pp. 1935-1977, 2025, DOI:10.32604/jrm.2025.02025-0026 - 22 October 2025

Abstract The review investigates the use of biomass-derived carbon as precursors for nanomaterials, acknowledging their sustainability and eco-friendliness. It examines various types of biomasses, such as agricultural residues and food byproducts, focussing on their transformation via environmentally friendly methods such as pyrolysis and hydrothermal carbonisation. Innovations in creating porous carbon nanostructures and heteroatom surface functionalisation are identified, enhancing catalytic performance. The study also explores the integration of biomass-derived carbon with nanomaterials for energy storage, catalysis, and other applications, noting the economic and environmental benefits. Despite these advantages, challenges persist in optimising synthesis methods and scaling production. More > Graphic Abstract

Matthew Rubinstein^1,*, Madeline Lauren Hong¹, Rishi Kumar Nanda¹, Daniel Thomas Jones¹, Hazem Aboaid², Yin Mon Myat³, Kyaw Zin Thein⁴

Oncology Research, Vol.33, No.11, pp. 3161-3183, 2025, DOI:10.32604/or.2025.067791 - 22 October 2025

Abstract The ever-expanding development of tissue-agnostic therapies which target malignancies based on specific mutations rather than tissue origin have transformed the landscape of oncology. The purpose of this review is to explore the impact, safety, and challenges of tissue-agnostic therapies including pembrolizumab, dostarlimab, larotrectinib, entrectinib, repotrectinib, dabrafenib plus trametinib, selpercatinib, and trastuzumab deruxtecan. As the therapeutic arsenal continues to grow, it is crucial to understand how these therapies truly benefit patients and to address the barriers that stand in the way of making them more widely available. Although these therapies have shown effectiveness across multiple cancer More >