MARKO SKELIN^1,2,3,*, BRUNA PERKOV-STIPIČIN¹, SANJA VUŠKOVIĆ⁴, MARINA ŠANDRK PLEHAČEK⁵, ANE BAŠIĆ⁶, DAVID ŠARČEVIĆ⁷, MAJA ILIĆ⁸, IVAN KREČAK^2,3,9

Oncology Research, Vol.32, No.5, pp. 807-815, 2024, DOI:10.32604/or.2024.048948

Abstract Background: The European Society for Medical Oncology (ESMO) guidelines are among the most comprehensive and widely used clinical practice guidelines (CPGs) globally. However, the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated. This study assessed ESMO CPG levels of evidence (LOE) and grades of recommendations (GOR), as well as their trends over time across various cancer settings. Methods: We manually extracted every recommendation with the Infectious Diseases Society of America (IDSA) classification from each CPG. We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.… More >

XUHUI ZHAO^1,2, XIAOMIN HUANG¹, CHUNYAN DANG², XIA WANG¹, YUJIAO QI³, HONGLING LI^2,*

Oncology Research, Vol.32, No.5, pp. 999-1009, 2024, DOI:10.32604/or.2024.046679

Abstract Background: EBV-miR-BARTs exhibit significant relevance in epithelial tumors, particularly in EBV-associated gastric and nasopharyngeal cancers. However, their specific mechanisms in the initiation and progression of gastric cancer remain insufficiently explored. Material and Methods: Initially, EBV-miRNA-BART6-5p and its target gene SMAD4 expression were assessed in EBV-associated gastric cancer tissues and cell lines. Subsequent transfection induced overexpression of EBV-miRNA-BART6-5p in AGS and MKN-45, and downregulation in EBV-positive cells (SUN-719). The subsequent evaluation aimed to observe their impact on gastric cancer cell proliferation, migration, and glycolytic processes, with the TGF-β/SMAD4 signaling pathway value clarified using a TGF-β inhibitor. Results: EBV-miRNA-BART6-5p exhibits pronounced upregulation… More >

EFFAT ALEMZADEH¹, LEILA ALLAHQOLI², AFROOZ MAZIDIMORADI³, ESMAT ALEMZADEH^1,4, FAHIMEH GHASEMI^4,5, HAMID SALEHINIYA⁶, IBRAHIM ALKATOUT^7,*

Oncology Research, Vol.32, No.5, pp. 831-847, 2024, DOI:10.32604/or.2024.031006

Abstract Ovarian cancer is among the most lethal gynecological cancers, primarily due to the lack of specific symptoms leading to an advanced-stage diagnosis and resistance to chemotherapy. Drug resistance (DR) poses the most significant challenge in treating patients with existing drugs. The Food and Drug Administration (FDA) has recently approved three new therapeutic drugs, including two poly (ADP-ribose) polymerase (PARP) inhibitors (olaparib and niraparib) and one vascular endothelial growth factor (VEGF) inhibitor (bevacizumab) for maintenance therapy. However, resistance to these new drugs has emerged. Therefore, understanding the mechanisms of DR and exploring new approaches to overcome them is crucial for effective… More >

Tao Yi^1,3, Xingshu Chen^1,2,*, Mingdong Yang³, Qindong Li¹, Yi Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793

Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the… More >

Haosong Gou¹, Gaoyi Zhang¹, Renê Ripardo Calixto², Senthil Kumar Jagatheesaperumal³, Victor Hugo C. de Albuquerque^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1077-1102, 2024, DOI:10.32604/cmes.2024.047806

Abstract Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally,… More >

Lyuchao Liao^1,2, Hankun Xiao^2,*, Pengqi Xing², Zhenhua Gan^1,2, Youpeng He², Jiajun Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >

Huaiguang Wu, Yibo Peng, Yaqiong He^*, Jinlin Fan

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 77-108, 2024, DOI:10.32604/cmes.2024.046758

Abstract In recent years, the number of smart contracts deployed on blockchain has exploded. However, the issue of vulnerability has caused incalculable losses. Due to the irreversible and immutability of smart contracts, vulnerability detection has become particularly important. With the popular use of neural network model, there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts. This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts. Subsequently, it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection. These… More > Graphic Abstract

Qing Zhu^1,*, Linlin Gu^1,2, Huijie Lin^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 577-591, 2024, DOI:10.32604/cmes.2023.043307

Abstract With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to… More >

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052

Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained… More >

JIA SHAO^1,2, CAN ZHANG², YAONAN TANG², AIQIN HE², WEIPEI ZHU^1,*

BIOCELL, Vol.48, No.4, pp. 571-580, 2024, DOI:10.32604/biocell.2024.049092

Abstract Objective: Previous studies indicated that aberrant circular RNA (circRNA) expression affects gene expression regulatory networks, leading to the aberrant activation of tumor pathways and promoting tumor cell growth. However, the expression, clinical significance, and effects on cell propagation, invasion, and dissemination of circRNA_001896 in cervical cancer (CC) tissues remain unclear. Methods: The Gene Expression Omnibus (GEO) datasets (GSE113696 and GSE102686) were used to examine differential circRNA expression in CC and adjacent tissues. The expression of circRNA_001896 was detected in 72 CC patients using fluorescence quantitative PCR. Correlation analysis with clinical pathological features was performed through COX multivariate and univariate analysis.… More >