Wen-Shou Tan^1,#, Hsuan Kuo^2,#, Chang-Ge Jiang¹, Mei-Han Lu¹, Yi-He Lu¹, Yung-Li Wang¹, Ching-Shuen Wang¹, Thi Thuy Tien Vo³, I-Ta Lee^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070871 - 30 December 2025

Abstract This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma (OSCC), a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis. Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes. Various salivary biomarkers, including microRNAs, cytokines, proteins, metabolites, and exosomes, have been linked to oncogenic signaling pathways involved in tumor progression, immune modulation, and therapeutic resistance. Advances in quantitative polymerase chain reaction, mass spectrometry, and next-generation sequencing have enabled comprehensive biomarker profiling, while point-of-care detection 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

Huihui Shi^1,#, Lei Chen^2,#, Juan Huang^3,#, Xuejing Lin², Lei Huang⁴, Min Tang⁴, Kai Lu^5,*, Wenchao Wang^4,*, Maoling Zhu^1,§,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.068737 - 30 December 2025

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. This study aimed to identify key genes involved in HCC development and elucidate their molecular mechanisms, with a particular focus on mitochondrial function and apoptosis. Methods: Differential expression analyses were performed across three datasets—The Cancer Genome Atlas (TCGA)-Liver Hepatocellular Carcinoma (LIHC), GSE36076, and GSE95698—to identify overlapping differentially expressed genes (DEGs). A prognostic risk model was then constructed. Cysteine/serine-rich nuclear protein 1 (CSRNP1) expression levels in HCC cell lines were assessed via western blot (WB) and quantitative reverse transcription polymerase chain reaction (qRT-PCR).… More > Graphic Abstract

Yechun Xin¹, Xinyuan Zhao¹, Dong Ding², Shuyu Chen², Chuanjie Wang², Tuo Wang^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069748 - 27 December 2025

Abstract To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current (HVDC) links and multi-infeed DC systems in load-center regions, this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter (MMC-CLCC) HVDC transmission system and its corresponding control strategy. First, the system topology is constructed, and a submodule configuration method for the MMC—combining full-bridge submodules (FBSMs) and half-bridge submodules (HBSMs)—is proposed to enable direct power flow reversal. Second, a hierarchical control strategy is introduced, including MMC voltage control, CLCC current control, and a coordination mechanism, along with the derivation of… More >

Mohammed Debakla^1,*, Ali Mezaghrani¹, Khalifa Djemal², Imane Zouaneb¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071927 - 09 December 2025

Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >

Rashid Jahangir^1,*, Nazik Alturki², Muhammad Asif Nauman³, Faiqa Hanif¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071574 - 09 December 2025

Abstract Human activity recognition (HAR) is a method to predict human activities from sensor signals using machine learning (ML) techniques. HAR systems have several applications in various domains, including medicine, surveillance, behavioral monitoring, and posture analysis. Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately. Several research studies on HAR have utilized Mel frequency cepstral coefficients (MFCCs) because of their effectiveness in capturing the periodic pattern of sensor signals. However, existing MFCC-based approaches often fail to capture sufficient temporal variability, which limits their ability to distinguish… More >

Muhammad Waqar Khan^1,*, Adnan Ahmed Siddiqui¹, Syed Sajjad Hussain Rizvi²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070858 - 09 December 2025

Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… More >

Lino Gonzalez-Garcia^*, Miguel-Angel Sicilia, Elena García-Barriocanal

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070813 - 09 December 2025

Abstract Classifying job offers into occupational categories is a fundamental task in human resource information systems, as it improves and streamlines indexing, search, and matching between openings and job seekers. Comprehensive occupational databases such as NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories, thereby facilitating standardization, cross-system interoperability, and access to metadata for each occupation (e.g., tasks, knowledge, skills, and abilities). In this work, we explore the effectiveness of fine-tuning existing language models (LMs) to classify job offers with occupational descriptors… More >

Yaxin Zhao¹, Qi Han², Hui Shu², Yan Guang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070511 - 09 December 2025

Abstract Large Language Models (LLMs) are increasingly applied in the field of code translation. However, existing evaluation methodologies suffer from two major limitations: (1) the high overlap between test data and pretraining corpora, which introduces significant bias in performance evaluation; and (2) mainstream metrics focus primarily on surface-level accuracy, failing to uncover the underlying factors that constrain model capabilities. To address these issues, this paper presents TCode (Translation-Oriented Code Evaluation benchmark)—a complexity-controllable, contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework. The dataset is carefully designed to control complexity along multiple dimensions—including syntactic… More >

Bin Zhang^*, Zhancheng Xu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-12, 2026, DOI:10.32604/cmc.2025.069979 - 09 December 2025

Abstract This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model. Two novel improvements were proposed and implemented in this paper: dynamically adjusting the loss function ratio and integrating an attention mechanism (CBAM). First, the loss function weights were adjusted dynamically. The grid search method is used for deciding the best ratio of 7:3. It gives greater emphasis to the cross-entropy loss, which resulted in better segmentation performance. Second, CBAM was applied at different layers of the 2D encoder. Heatmap analysis revealed that introducing it after the second… More >