WEIGANG XIU^1,#, XINGYU LIU^1,2,#, KAIXIN HU^1,2, QIN ZHANG^3,*, HUASHAN SHI^4,*

Oncology Research, Vol.32, No.10, pp. 1613-1621, 2024, DOI:10.32604/or.2024.047933

Abstract Elevated serum cholesterol metabolism is associated with a reduced risk of lung cancer. Disrupted cholesterol metabolism is evident in both lung cancer patients and tumor cells. Inhibiting tumor cell cholesterol uptake or biosynthesis pathways, through the modulation of receptors and enzymes such as liver X receptor and sterol-regulatory element binding protein 2, effectively restrains lung tumor growth. Similarly, promoting cholesterol excretion yields comparable effects. Cholesterol metabolites, including oxysterols and isoprenoids, play a crucial role in regulating cholesterol metabolism within tumor cells, consequently impacting cancer progression. In lung cancer patients, both the cholesterol levels in the… More >

EGOR V. BATOROV^1,2,*, ALISA D. INESHINA², TATIANA A. ARISTOVA³, VERA V. DENISOVA³, SVETLANA A. SIZIKOVA³, DARIA S. BATOROVA³, GALINA Y. USHAKOVA³, EKATERINA Y. SHEVELA¹, ELENA R. CHERNYKH¹

Oncology Research, Vol.32, No.10, pp. 1575-1587, 2024, DOI:10.32604/or.2024.047893

Abstract Background: Immune checkpoint ligand-receptor interactions appear to be associated with multiple myeloma (MM) progression. Simultaneously, previous studies showed the possibility of PD-1 and TIM-3 expression on T cells upon stimulation with common γ-chain family cytokines in vitro and during homeostatic proliferation. The aim of the present work was to study the impact of homeostatic proliferation on the expansion of certain T cell subsets up-regulating PD-1 and TIM-3 checkpoint molecules. Methods: The expression of CD25, CD122, CD127 common γ-chain cytokine receptors, phosphorylated signal transducer and activator of transcription-5 (pSTAT5) and eomesodermin (EOMES) was comparatively assessed with flow… More >

SHUBHRA CHANDRA^1,2, JENNIFER C. WILSON^1,2, DAVID GOOD³, MING Q. WEI^1,2,*

Oncology Research, Vol.32, No.10, pp. 1543-1564, 2024, DOI:10.32604/or.2024.043987

Abstract The advent of RNA therapy, particularly through the development of mRNA cancer vaccines, has ushered in a new era in the field of oncology. This article provides a concise overview of the key principles, recent advancements, and potential implications of mRNA cancer vaccines as a groundbreaking modality in cancer treatment. mRNA cancer vaccines represent a revolutionary approach to combatting cancer by leveraging the body’s innate immune system. These vaccines are designed to deliver specific mRNA sequences encoding cancer-associated antigens, prompting the immune system to recognize and mount a targeted response against malignant cells. This personalized… More > Graphic Abstract

YUN WANG¹, XIAOJIANG LI², DALONG LIU², ZHIFENG WANG³, JICHEN XIA⁴, LIJUN WANG⁵, XUDONG ZHANG^6,*

Oncology Research, Vol.32, No.10, pp. 1649-1660, 2024, DOI:10.32604/or.2024.043482

Abstract Exosomes, minute vesicles ubiquitously released by diverse cell types, serve as critical mediators in intercellular communication. Their pathophysiological relevance, especially in malignancies, has garnered significant attention. A meticulous exploration of the exosomal impact on cancer development has unveiled avenues for innovative and clinically valuable techniques. The cargo conveyed by exosomes exerts transformative effects on both local and distant microenvironments, thereby influencing a broad spectrum of biological responses in recipient cells. These membrane-bound extracellular vesicles (EVs) play a pivotal role in delivering bioactive molecules among cells and organs. Cellular and biological processes in recipient cells, ranging… More > Graphic Abstract

Varada Rajkumar Kukkala¹, Surapaneni Phani Praveen², Naga Satya Koti Mani Kumar Tirumanadham³, Parvathaneni Naga Srinivasu^4,5,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603

Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >

Zakir Hussain Ahmed^1,*, Maha Ata Al-Furhood², Abdul Khader Jilani Saudagar³, Shakir Khan⁴

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1113-1131, 2024, DOI:10.32604/csse.2024.053574

Abstract The generalized travelling salesman problem (GTSP), a generalization of the well-known travelling salesman problem (TSP), is considered for our study. Since the GTSP is NP-hard and very complex, finding exact solutions is highly expensive, we will develop genetic algorithms (GAs) to obtain heuristic solutions to the problem. In GAs, as the crossover is a very important process, the crossover methods proposed for the traditional TSP could be adapted for the GTSP. The sequential constructive crossover (SCX) and three other operators are adapted to use in GAs to solve the GTSP. The effectiveness of GA using More >

Raed Alotaibi¹, Omar Reyad^2,3, Mohamed Esmail Karar^4,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1133-1147, 2024, DOI:10.32604/csse.2024.053358

Abstract E-learning behavior data indicates several students’ activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures. This article proposes a new analytics system to support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments. The proposed e-learning analytics system includes a new deep forest model. It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks. The developed forest model can analyze each student’s activities during the use of an e-learning… More >

Jyun-Guo Wang^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931

Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

Mohammad Ababneh^*, Ayat Al-Droos, Ammar El-Hassan

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1171-1191, 2024, DOI:10.32604/csse.2024.052875

Abstract With the high level of proliferation of connected mobile devices, the risk of intrusion becomes higher. Artificial Intelligence (AI) and Machine Learning (ML) algorithms started to feature in protection software and showed effective results. These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’ malware, especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly. In this article, we present a framework for mobile malware detection based on a… More >

Amita U. Dessai^*, Hassanali G. Virani

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1193-1211, 2024, DOI:10.32604/csse.2024.052710

Abstract Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction (HCI). However, physical methods of emotion recognition such as facial expressions, voice, and text data, do not always indicate true emotions, as users can falsify them. Among the physiological methods of emotion detection, Electrocardiogram (ECG) is a reliable and efficient way of detecting emotions. ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments. Researchers use deep machine learning techniques for emotion recognition using ECG signals, but there is a need to develop efficient models… More >