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  • Open Access

    ARTICLE

    Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

    Farman Saifi1,*, Mohd Javaid1, Abid Haleem1, S. M. Anas2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2747-2777, 2024, DOI:10.32604/cmes.2024.051490 - 08 July 2024

    Abstract Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infrastructure systems and networks capable of withstanding blast loading. Initially centered on high-profile facilities such as embassies and petrochemical plants, this concern now extends to a wider array of infrastructures and facilities. Engineers and scholars increasingly prioritize structural safety against explosions, particularly to prevent disproportionate collapse and damage to nearby structures. Urbanization has further amplified the reliance on oil and gas pipelines, making them vital for urban life and prime targets for terrorist activities. Consequently, there is a growing imperative for computational… More >

  • Open Access

    ARTICLE

    Prognostic-related genes for pancreatic cancer typing and immunotherapy response prediction based on single-cell sequencing data and bulk sequencing data

    XUEFENG WANG1,#, SICONG JIANG2,#, XINHONG ZHOU3, XIAOFENG WANG4, LAN LI5, JIANJUN TANG1,*

    Oncology Research, Vol.31, No.5, pp. 697-714, 2023, DOI:10.32604/or.2023.029458 - 21 July 2023

    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… More >

  • Open Access

    ARTICLE

    Clinical implication of naive and memory T cells in locally advanced cervical cancer: A proxy for tumor biology and short-term response prediction

    YUTING WANG1,2,3, PEIWEN FAN1,2,3, YANING FENG1,2,3, XUAN YAO4, YANCHUN PENG4, RUOZHENG WANG1,2,3,*

    BIOCELL, Vol.47, No.6, pp. 1365-1375, 2023, DOI:10.32604/biocell.2023.027201 - 19 May 2023

    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… More >

  • Open Access

    ARTICLE

    Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

    Mehdi Hassan1,*, Safdar Ali2, Muhammad Sanaullah3, Khuram Shahzad4, Sadaf Mushtaq5,6, Rashda Abbasi6, Zulqurnain Ali4, Hani Alquhayz7

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2743-2760, 2022, DOI:10.32604/cmc.2022.020055 - 27 September 2021

    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… More >

  • Open Access

    ARTICLE

    The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma

    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… More >

  • Open Access

    ARTICLE

    A Deep Learning Based Approach for Response Prediction of Beam-like Structures

    Tianyu Wang1, Wael A. Altabey1,2, Mohammad Noori3,*, Ramin Ghiasi1

    Structural Durability & Health Monitoring, Vol.14, No.4, pp. 315-338, 2020, DOI:10.32604/sdhm.2020.011083 - 04 December 2020

    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 More >

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