Raquel Sanchez-Baltasar¹, Nerea Castañeda-Fernández¹, Jorge Olivares-Arancibia², Carlos Torres-Villar^3,4, Julio Plaza-Diaz^5,6,7,8,*, Lourdes Herrera-Quintana^1,*

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.081924 - 16 June 2026

Abstract Breast cancer (BC) is the most frequently diagnosed malignancy in women worldwide and remains one of the leading causes of cancer-related mortality, with substantial international disparities in incidence, stage at diagnosis, access to treatment, and survival. In recent years, BC management has evolved rapidly through advances in molecular characterization, imaging, pathology, targeted therapies, immunotherapy, and survivorship care. Nevertheless, important gaps persist in early and accurate detection, biomarker standardization, equitable access to care, and patient-specific treatment selection. These advances require timely, evidence-based, and context-specific clinical frameworks to support appropriate implementation, and to avoid the use of… More >

Zehao Wei^1,#, Xuejian Liu^2,#, Zheng Zhang¹, Yimin Ma^2,*, Min Xu^1,*

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.078793 - 16 June 2026

Abstract Pancreatic cancer is one of the most lethal malignancies, characterized by difficulties in early diagnosis, limited therapeutic options, and generally poor patient prognosis. In recent years, immunotherapy has provided new opportunities for the treatment of pancreatic cancer; however, its clinical efficacy has been substantially constrained by the complex tumor microenvironment (TME) and immune evasion mechanisms. With the rapid advancement of artificial intelligence (AI) technologies, AI has demonstrated great potential in the early detection of pancreatic cancer, prediction of immunotherapeutic responses, and design of personalized treatment strategies. This review systematically summarizes the latest advances in the More >

Yu Zhang^1,2,#, Shihan Tan^1,#, Guangyao Lian², Congying Dun³, Qiwei Hu^1,*, Chiming Guo^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081931 - 15 June 2026

Abstract Gearboxes are critical components in the transmission systems of various mechanical equipment. Subjected to complex and harsh operating conditions for a long time, they suffer from a high failure rate and potentially severe consequences. Traditional fault diagnosis methods are limited by problems such as noise interference, and can hardly meet the requirements in terms of diagnostic accuracy, generalization ability, and reliability. To tackle the deficiencies of traditional gearbox fault diagnosis methods, including insufficient utilization of features, poor generalization under small-sample conditions, and weak model interpretability, this paper proposes a fault diagnosis method based on multi-dimensional… More >

Huaixuan Yan, Yan Gong^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080178 - 15 June 2026

Abstract To correctly diagnose faults in farm machinery, we need to know a lot about the field and have experience with maintenance. However, most of this important information is stored in old, unstructured documents like technical manuals and expert logs. These documents don’t have a standard way to be represented digitally, which makes it very hard to build automated diagnosis systems. There are three main technical problems with getting structured knowledge out of this kind of text: noise from optical character recognition (OCR) during digitization, the extreme lack of labeled samples in specialized fields (low-resource constraints),… More >

Aygül Kaya Akıllı¹, Osman Akdeniz², Celal Özcan^3,*

Structural and Congenital Heart Disease, Vol.21, No.2, 2026, DOI:10.32604/schd.2026.076628 - 11 June 2026

Abstract Background: Congenital heart disease (CHD) refers to malformations of the heart or great vessels that occur during the intrauterine period. Critical CHD refers to heart conditions that require medical intervention or surgical procedures in the early stages of life. Methods: In this retrospective cohort study, newborns aged 0 to 28 days who were admitted to the Neonatal Intensive Care Unit and the Pediatric Cardiovascular Surgery Clinic of our hospital with a diagnosis of critical CHD between January 2019 and September 2024 were evaluated. Results: Among 160 patients, 52 (32.5%) had a prenatal diagnosis. Overall mortality was significantly… More >

Amel Ksibi^1,*, Manel Ayadi¹, Hela Elmannai², Monia Hamdi², Ala Saleh Alluhaidan¹, Imen Ksibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078347 - 27 May 2026

Abstract Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including More >

Kelan Wang¹, Jianfei Chen^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079644 - 08 May 2026

Abstract As 6G-enabled Industrial Internet of Things (IIoT) evolves, green and sustainable industrial monitoring increasingly relies on edge AI to deliver low-latency diagnosis under tight resource constraints. Industrial cyber–physical systems increasingly rely on heterogeneous sensing and communication infrastructures, where network-side attacks can propagate into physical processes and appear as coupled anomalies. Reliable diagnosis therefore requires joint learning from time-synchronized cyber and physical telemetry rather than modeling them as independent signals. This paper develops Cyber–Physical Symbiosis Network (CPSNet), a model designed for edge-AI deployment with a dual-stream architecture for fixed-window multiclass cross-domain anomaly diagnosis in IIoT. CPSNet… More >

Soo-Hyun Sung¹, Seungwon Shin², Seok-Hwan Kim³, Minjung Park^4,*

International Journal of Mental Health Promotion, Vol.28, No.4, 2026, DOI:10.32604/ijmhp.2026.077710 - 28 April 2026

Abstract Objectives: Psychiatric inpatient care plays a critical role in stabilizing acute mental health crises, yet the optimal length of stay (LOS) and its impact on short-term clinical outcomes remain poorly defined across diagnostic groups. This study aimed to examine how LOS in psychiatric inpatient units is associated with clinical improvement at discharge and to determine whether this association differs across major diagnostic groups, using nationally representative hospital discharge data from Korea. Methods: A cross-sectional secondary analysis was conducted using the 2022–2023 Korea National Hospital Discharge In-depth Injury Survey. Adults whose primary discharge diagnosis was a mental… More >

Rafael de Oliveira Silva^1,2,*, Roberto Outa³, Fábio Roberto Chavarette⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080607 - 27 April 2026

Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >

Yue Su¹, Shukuan Zhang^1,*, Jinghao Jiao¹, Jiankang Zhong², Qianxi Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079927 - 27 April 2026

Abstract This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed. Firstly, mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals. Then, the SABO algorithm is used to optimize the VMD parameters, followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions (IMFs), and the time-domain parameters More >