LISANNE K.A. NEIJENHUIS^1,2,3,#, LEUTA L. NAUMANN^4,#, SONIA A.M. FERKEL¹, SAMUEL J.S. RUBIN¹, STEPHAN ROGALLA^1,*

Oncology Research, Vol.32, No.7, pp. 1163-1172, 2024, DOI:10.32604/or.2024.050907

Abstract Background: Osteosarcoma is the most common malignant primary bone tumor. The prognosis for patients with disseminated disease remains very poor despite recent advancements in chemotherapy. Moreover, current treatment regimens bear a significant risk of serious side effects. Thus, there is an unmet clinical need for effective therapies with improved safety profiles. Taurolidine is an antibacterial agent that has been shown to induce cell death in different types of cancer cell lines. Methods: In this study, we examined both the antineoplastic and antiangiogenic effects of taurolidine in animal models of osteosarcoma. K7M2 murine osteosarcoma cells were… More >

Samia Allaoua Chelloug^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4845-4861, 2024, DOI:10.32604/cmc.2024.051539

Abstract Intrusion detection is a predominant task that monitors and protects the network infrastructure. Therefore, many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection. In particular, the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) is an extensively used benchmark dataset for evaluating intrusion detection systems (IDSs) as it incorporates various network traffic attacks. It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models, but the performance of these models often decreases when evaluated on… More >

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Yi-Chun Lai¹, Shu-Yin Chiang², Yao-Chiang Kan³, Hsueh-Chun Lin^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376

Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More > Graphic Abstract

Hao Chen^#, Runfeng Xie^#, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan^*, Kai Zhang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4283-4296, 2024, DOI:10.32604/cmc.2024.049129

Abstract Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the long tail problem of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into traditional methods. To learn the contextual information of news text, we… More >

Dina Polosukhina^*, Harold D. Love^*†, Harold L. Moses^‡§¶#, Ethan Lee^**††, Roy Zent^{†§**‡‡}, Peter E. Clark^*‡

Oncology Research, Vol.25, No.9, pp. 1653-1664, 2017, DOI:10.3727/096504017X14992942781895

Abstract Wilms tumor (WT) is the most common renal malignancy in children and the fourth most common pediatric solid malignancy in the US. Although the mechanisms underlying the WT biology are complex, these tumors most often demonstrate activation of the canonical Wnt/β-catenin pathway. We and others have shown that constitutive activation of β-catenin restricted to the renal epithelium is sufficient to induce primitive renal epithelial tumors, which resemble human WT. Here we demonstrate that pharmacologic inhibition of β-catenin gene transcription with pyrvinium inhibits tumor growth and metastatic progression in a murine model of WT. Cellular invasion More >

Seoyun Kim^1,#, Hyerim Yu^2,#, Jeewoo Yoon^1,3, Eunil Park^1,2,*

Computer Systems Science and Engineering, Vol.48, No.3, pp. 861-861, 2024, DOI:10.32604/csse.2024.053659

Abstract This article has no abstract. More >

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

Adil Hussain^1,*, Ayesha Aslam²

Journal on Artificial Intelligence, Vol.6, pp. 129-152, 2024, DOI:10.32604/jai.2024.050277

Abstract The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, More >

Hüseyin Bilgin^*, Bredli Plaku

Structural Durability & Health Monitoring, Vol.18, No.3, pp. 197-222, 2024, DOI:10.32604/sdhm.2024.048645

Abstract In this study, the influence of confined concrete models on the response of reinforced concrete structures is investigated at member and global system levels. The commonly encountered concrete models such as Modified Kent-Park, Saatçioğlu-Razvi, and Mander are considered. Two moment-resisting frames designed according to the pre-modern code are taken into consideration to reflect the example of an RC moment-resisting frame in the current building stock. The building is in an earthquake-prone zone located on Z3 Soil Type. The inelastic response of the building frame is modelled by considering the plastic hinges formed on each beam… More >