XUEFENG WANG^1,#, SICONG JIANG^2,#, XINHONG ZHOU³, XIAOFENG WANG⁴, LAN LI⁵, JIANJUN TANG^1,*

Oncology Research, Vol.31, No.5, pp. 697-714, 2023, DOI:10.32604/or.2023.029458

Abstract Background: Pancreatic cancer is associated with high mortality and is one of the most aggressive of malignancies, but studies have not fully evaluated its molecular subtypes, prognosis and response to immunotherapy of different subtypes. The purpose of this study was to explore the molecular subtypes and the key genes associated with the prognosis of pancreas cancer patients and study the clinical phenotype, prognosis and response to immunotherapy using single-cell seq data and bulk RNA seq data, and data retrieved from GEO and TCGA databases. Methods: Single-cell seq data and bioinformatics methods were used in this study. Pancreatic cancer data were… More >

YUTING WANG^1,2,3, PEIWEN FAN^1,2,3, YANING FENG^1,2,3, XUAN YAO⁴, YANCHUN PENG⁴, RUOZHENG WANG^1,2,3,*

BIOCELL, Vol.47, No.6, pp. 1365-1375, 2023, DOI:10.32604/biocell.2023.027201

Abstract Background: This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally advanced cervical cancer (LACC). Methods: Thirty-four patients who received concurrent chemoradiotherapy and twenty-one patients who merely underwent radiotherapy were enrolled in this study. We retrospectively analyzed the T cell markers (i.e., CD3, CD4, CD8), memory markers (i.e., CD45, CCR7), and differentiation markers (i.e., CD27) in the peripheral blood and tumor tissues of patients with LACC before treatment based on flow cytometry. We also analyzed the relationship of T cell subsets between peripheral blood and… More >

Piao Yongfeng^*†‡§1, Jiang Chuner^*¶#1, Wang Lei^*†‡§, Yan Fengqin^*†‡§, Ye Zhimin^*†‡§, Fu Zhenfu^*†‡§, Jiang Haitao^*,**††, Jiang Yangming^‡‡, Wang Fangzheng^*†‡§

Oncology Research, Vol.28, No.6, pp. 605-613, 2020, DOI:10.3727/096504020X16022401878096

Abstract The aim of this study was to explore the predictive role of pretreatment MRI-based radiomics on early response of neoadjuvant chemotherapy (NAC) in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Between January 2016 and December 2016, a total of 108 newly diagnosed NPC patients who were hospitalized in the Cancer Hospital of the University of Chinese Academy of Sciences were reviewed. All patients had complete data of enhanced MR of nasopharynx before treatment, and then received two to three cycles of TP-based NAC. After 2 cycles of NAC, enhanced MR of nasopharynx was conducted again. Compared with the enhanced MR images… More >

Mehdi Hassan^1,*, Safdar Ali², Muhammad Sanaullah³, Khuram Shahzad⁴, Sadaf Mushtaq^5,6, Rashda Abbasi⁶, Zulqurnain Ali⁴, Hani Alquhayz⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2743-2760, 2022, DOI:10.32604/cmc.2022.020055

Abstract Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and… More >

Tianyu Wang¹, Wael A. Altabey^1,2, Mohammad Noori^3,*, Ramin Ghiasi¹

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 315-338, 2020, DOI:10.32604/sdhm.2020.011083

Abstract Beam-like structures are a class of common but important structures in engineering. Over the past few centuries, extensive research has been carried out to obtain the static and dynamic response of beam-like structures. Although building the finite element model to predict the response of these structures has proven to be effective, it is not always suitable in all the application cases because of high computational time or lack of accuracy. This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural network and the governing differential equation of Euler-Bernoulli beam. The Prandtl-Ishlinskii… More >