Oncology Research Editorial Office

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.077270 - 30 December 2025

Abstract This article has no abstract. More >

Shenghui Su, Yu Zeng, Jiaxin Chen, Xieping Dong^*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070558 - 30 December 2025

Abstract Background: Recent studies have shown glycerolipid metabolism played an essential role in multiple tumors, however, its function in osteosarcoma is unclear. This study aimed to explore the role of glycerolipid metabolism in osteosarcoma. Methods: We conducted bioinformatics analysis using data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and single-cell RNA sequencing. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the Glycerolipid metabolism-related genes associated with the clinical outcome of osteosarcoma. Tumor-associated macrophages (TAMs) and their interactions with immune cells were examined through single-cell analysis and co-culture experiments.… More >

Kai Gui^1,#, Tianyi Yang^1,#, Chengying Xiong¹, Yue Wang¹, Zhiqiang He¹, Wuxian Li^2,3,*, Min Tang^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070143 - 30 December 2025

Abstract Objectives: The mechanism by which specific tumor subsets in colorectal cancer (CRC) use alternative metabolic pathways, particularly those modulated by hypoxia and fructose, to alter the tumor microenvironment (TME) remains unclear. This study aimed to identify these malignant subpopulations and characterize their intercellular signaling networks and spatial organization through an integrative multi-omics approach. Methods: Leveraging bulk datasets, single-cell RNA sequencing, and integrative spatial transcriptomics, we developed a prognostic model based on hypoxia-and fructose metabolism-related genes (HFGs) to delineate tumor cell subpopulations and their intercellular signaling networks. Results: We identified a specific subset of stanniocalcin-2 positive (STC2+)… More > Graphic Abstract

Shaojie Min¹, Yaxiao Luo¹, Kebing Liu¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.073175 - 09 December 2025

Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >

Chang Su, Liangliang Zhao^*, Dongbing Xiang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.071319 - 09 December 2025

Abstract To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field (A-APF). Centered on the Q-learning framework, the algorithm leverages safety-oriented guidance generated by A-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation. The proposed system comprises four core modules: (1) an environment modeling module that constructs grid-based obstacle maps; (2) an A-APF module that combines heuristic search from A* algorithm with repulsive force strategies from… More >

Ans D. Alghamdi^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-35, 2026, DOI:10.32604/cmc.2025.071210 - 09 December 2025

Abstract Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. More >

Zhijie Lin¹, Chao Yang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-33, 2026, DOI:10.32604/cmc.2025.070735 - 09 December 2025

Abstract Sustainable aluminum alloys, renowned for their lower energy consumption and carbon emissions, present a critical path towards a circular materials economy. However, their design is fraught with challenges, including complex performance variability due to impurity elements and the time-consuming, cost-prohibitive nature of traditional trial-and-error methods. The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development. This paper provides a comprehensive review of Artificial Intelligence (AI) techniques applied to sustainable aluminum alloy design, analyzing their methodologies and identifying key challenges and optimization strategies. We review how… More >

Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman^*, Prashant Pillai

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.070316 - 09 December 2025

Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >

Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Cyreneo Dofitas¹, Yong-Woon Kim², Yung-Cheol Byun^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069374 - 09 December 2025

Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >