Shijia Lu^1,#, Yanmin Wang^1,#, Han Zhang¹, Mengjia Yan¹, Mengdan Sang², Jinle Wang¹, Huaying Du³, Jinwen Sima³, Yiran Zhen², Xue Yang², Yutong Zhang¹, Hongwei Zhou^1,*

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.079988 - 16 July 2026

Abstract Proliferating Cell Nuclear Antigen (PCNA) is a core protein in DNA replication and repair. Its functional dysregulation drives tumorigenesis and therapeutic resistance, making it a critical anticancer target. However, the fundamental conflict between PCNA’s indispensable “guardian” function in normal cells and its hijacked “accomplice” role in cancer cells constitutes the central challenge for targeted intervention: how to eradicate tumors while avoiding severe toxicity to normal tissues. This review aims to systematically review the latest advances and translational dilemmas in the field of PCNA-targeted therapy. It outlines various intervention strategies, including small-molecule inhibitors, proteolysis-targeting chimeras, post-translational More > Graphic Abstract

Yen-Dun Tony Tzeng^1,2,#, Chen-Yueh Wen^3,4,#, Su-Boon Yong^5,6, Zhi-Hong Wen^7,8, An-Jen Chiang^9,*, Chia-Jung Li^8,10,11,12,*

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.078924 - 16 July 2026

Abstract Breast cancer (BC) management has transitioned from histological classification to molecular subtyping, yet therapeutic resistance and intratumor heterogeneity remain critical clinical challenges. This review examines the emerging paradigm shift toward integrating mitochondrial metabolism into the precision medicine framework. We detail the complex mitonuclear crosstalk where nuclear genetic alterations, such as Breast Cancer 1 (BRCA1) deficiency and TP53 mutations, fundamentally reprogram mitochondrial bioenergetics. Specifically, the loss of BRCA1 function triggers a systemic NAD⁺ depletion trap through PARP1 hyperactivation, while oncogenic drivers like MYC coordinate with PGC1α to enhance mitochondrial biogenesis for metastatic survival. We evaluate the diagnostic potential of… More > Graphic Abstract

Gábor Hasulyó^*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.081099 - 12 July 2026

Abstract The current global energy situation is very fragile. Much more stable and predictable energy security is needed. Due to global climate conditions, it is advisable to prioritize fuels that are high in energy content and relatively easy to produce, such as hydrogen. However, the widespread deployment of hydrogen refueling stations is hampered by significant safety challenges, including hydrogen’s high flammability, its tendency to leak, and high-pressure storage requirements. This study examines how digital twin technology can be implemented to improve the safety and operational efficiency of hydrogen facilities. A dynamic digital twin model was developed… More >

Chengcheng Sha^*, Manyuan Li, Xiaolei Pan^*

Journal of Psychology in Africa, Vol.36, No.3, pp. 341-349, 2026, DOI:10.32604/jpa.2026.074832 - 30 June 2026

Abstract This study explores how authentic leadership reduces workplace deviance behavior in the era of artificial intelligence (AI) through a chain mediation mechanism involving positive psychological capital and organizational identification. The sample comprised 619 business professionals who regularly used AI tools in their work (52% male; M = 34 years, 20.7% from the service education). The results revealed a significant workplace deviance behavior to be lower with authentic leadership. Organizational identification mediated the relationship between authentic leadership and workplace deviance behavior for lower workplace deviance. Although positive psychological capital alone did not mediate this relationship, it More >

Khlieeq Ul Zaman^1,2,*, Ahmad Saeed³, Muhammad Awais Khan², Hafiz Abdul Basit⁴, Shaharyar², Maria Anum², Rani Ummay Farwa^2,*, Mahmood Iqbal¹, Ali Raza²

Revue Internationale de Géomatique, Vol.35, pp. 409-422, 2026, DOI:10.32604/rig.2026.083214 - 02 July 2026

Abstract Groundwater is the primary buffer against water scarcity in rapidly urbanizing regions, yet its sustainable management is constrained by limited hydrogeological data. This study presents an integrated Geographic Information System (GIS) remote sensing framework strengthened with Artificial Intelligence (AI) to delineate groundwater potential zones (GWPZs) in the Islamabad Capital Territory. Six thematic layers—slope, drainage density, lithology, rainfall, land use/land cover (LULC), and the Normalized Difference Vegetation Index (NDVI)—were derived from SRTM DEM, Sentinel-2 imagery, geological maps, and climate records. Each layer was standardized, reclassified, and weighted using the Analytical Hierarchy Process (AHP). A complete pairwise… More >

Siamak Talatahari^*, Amin Beheshti

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084097 - 30 June 2026

Abstract This article has no abstract. More >

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 >

Leonidas Theodorakopoulos, Alexandra Theodoropoulou^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082270 - 15 June 2026

Abstract Auditable autonomy is becoming a practical requirement for deploying large language model (LLM) agents in operational workflows where recommendations can trigger consequential actions. Many autonomy claims remain hard to evaluate because studies emphasize task completion or fluent explanations while underreporting tool privileges, verification conditions, rollback feasibility, and trace completeness. This review develops a decision-making–centered framework that treats autonomy as an auditable engineering property. It introduces a three-plane big data foundation: an evidence plane with provenance and freshness constraints; a decision-trace plane that records retrieval identifiers, tool invocations, intermediate checks, and policy evaluations; and an outcomes More >

Naeem Ullah^1,*, Javed Ali Khan², Michelina Ruocco³, Antonio Della Cioppa⁴, Ivanoe De Falco⁵, Giovanna Sannino⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081477 - 15 June 2026

Abstract Fruit diseases significantly impact agricultural productivity, yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images, particularly in orange fruit disease datasets. Current deep learning approaches are prone to background bias, which reduces explainability and generalization. To address this, we propose a deep learning framework that explicitly reduces background noise and bias in orange fruit disease image classification while providing interpretable, pixel-level predictions. The framework integrates existing architectural components, including grouped convolutions with channel shuffling, Leaky ReLU and clipped ReLU activations, and attention-based feature extraction, within a… More >