Jing Shan¹, Catherine King^2,3, Harunor Rashid^3,4, Veysel Kayser^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070851 - 30 December 2025

Abstract Objectives: B-cell maturation antigen (BCMA)-targeted antibody–drug conjugates (ADCs) have emerged as promising therapies for relapsed/refractory multiple myeloma (RRMM), but the overall efficacy and safety profile is unclear. This study aimed to synthesize the available evidence on the safety and efficacy of BCMA-ADCs in development for RRMM. Methods: A systematic search was conducted using six bibliographic databases and ClinicalTrials.gov up to November 2024. Studies were eligible if they were human clinical trials or animal studies evaluating BCMA-ADCs and reported efficacy and safety outcomes. Data extraction and quality assessments were conducted using validated tools, including ROBINS-I… More >

Giacomo Iovane^1,*, Luca Traman², Michele Maffezzoli^1,3, Giuseppe Fornarini², Domenico Corradi⁴, Debora Guareschi⁴, Matteo Santoni^5,#, Sebastiano Buti^1,#

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070523 - 30 December 2025

Abstract Background: While the treatment of metastatic renal cell carcinoma (mRCC) is evolving due to immune checkpoint inhibitors (ICIs), optimal strategies for later lines of therapy have yet to be defined. The combination of lenvatinib and everolimus represents a viable option, and the present review aimed to summarize its activity, effectiveness, and safety. Methods: A systematic review of the literature was conducted using PubMed, targeting studies published between 2018 and 2025. Eligible studies included English-language prospective and retrospective trials reporting survival outcomes in mRCC patients treated with lenvatinib and everolimus after at least one ICI-containing regimen. Results:… More > Graphic Abstract

Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control 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 >

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 >

John Hordines¹, Shirley Ge², Dima Raskolnikov¹, Alexander C. Small¹, Kara L. Watts^1,*

Canadian Journal of Urology, Vol.32, No.6, pp. 551-560, 2025, DOI:10.32604/cju.2025.065988 - 30 December 2025

Abstract Background: The healthcare industry contributes nearly 5% of worldwide carbon emissions. In an effort to mitigate this impact, urology practices can take steps to reduce their carbon footprints. We conducted a systematic review which aimed to summarise the current literature on the environmental impact of urologic-related care. Methods: A systematic literature review evaluating the impact of urologic procedures, telehealth and conferences/interviews was conducted on PubMed and Cochrane databases using a Boolean search strategy and the following search terms: urology, planetary health, environmental impact, carbon emissions, carbon footprint, and waste. Full-text articles published in English were… More >

Mirza Murad Baig¹, Muhammad Rehan Faheem^2,*, Lal Khan^3,*, Hannan Adeel², Syed Asim Ali Shah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2895-2935, 2025, DOI:10.32604/cmes.2025.073172 - 23 December 2025

Abstract With growing urban areas, the climate continues to change as a result of growing populations, and hence, the demand for better emergency response systems has become more important than ever. Human Behaviour Classification (HBC) systems have started to play a vital role by analysing data from different sources to detect signs of emergencies. These systems are being used in many critical areas like healthcare, public safety, and disaster management to improve response time and to prepare ahead of time. But detecting human behaviour in such stressful conditions is not simple; it often comes with noisy… More > Graphic Abstract

Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 2025

Abstract Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >

Andrea Panunzio¹, Rossella Orlando¹, Federico Greco^2,3, Giovanni Mazzucato⁴, Floriana Luigina Rizzo¹, Serena Domenica D’Elia¹, Antonio Benito Porcaro⁵, Alessandro Antonelli⁵, Alessandro Tafuri^1,6,*

Canadian Journal of Urology, Vol.32, No.5, pp. 375-384, 2025, DOI:10.32604/cju.2025.066140 - 30 October 2025

Abstract Background: The Barthel Index (BI) measures the level of patient independence in activities of daily living. This review aims to summarize current evidence on the use of the BI in urology, highlighting its potential as a tool for assessing patients prior to surgery. Materials and methods: A comprehensive search of PubMed, Scopus, and Web of Science databases was conducted for studies evaluating the BI in patients undergoing urologic surgery, following Systematic Review and Meta-analyses (PRISMA) guidelines. The BI was investigated both as a descriptor of baseline or postoperative health status and a prognostic indicator. A qualitative… 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 >