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  • Open Access

    ARTICLE

    Exploring the therapeutic potential of precision T-Cell Receptors (TCRs) in targeting KRAS G12D cancer through in vitro development

    WEITAO ZHENG1, DONG JIANG2, SONGEN CHEN1, MEILING WU1, BAOQI YAN2, JIAHUI ZHAI2, YUNQIANG SHI2, BIN XIE1, XINGWANG XIE2, KANGHONG HU1,*, WENXUE MA3,*

    Oncology Research, Vol.32, No.12, pp. 1837-1850, 2024, DOI:10.32604/or.2024.056565 - 13 November 2024

    Abstract Objectives: The Kirsten rat sarcoma virus (KRAS) G12D oncogenic mutation poses a significant challenge in treating solid tumors due to the lack of specific and effective therapeutic interventions. This study aims to explore innovative approaches in T cell receptor (TCR) engineering and characterization to target the KRAS G12D7-16 mutation, providing potential strategies for overcoming this therapeutic challenge. Methods: In this innovative study, we engineered and characterized two T cell receptors (TCRs), KDA11-01 and KDA11-02 with high affinity for the KRAS G12D7-16 mutation. These TCRs were isolated from tumor-infiltrating lymphocytes (TILs) derived from tumor tissues of patients More >

  • Open Access

    PROCEEDINGS

    A Study on the Extraction and Evaluation Method of Virtual Strain

    Peiyan Wang1,*, Haoyu Wang1, Minghui Liu2, Fuchao Liu1, Zhufeng Yue1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011318

    Abstract The virtual test is supported by the physical test data, and a high-precision simulation model needs to be established to maximize the alignment between the simulation prediction results and the physical test data. It can replace other physical tests and achieve the goal of reducing the design cycle time and cost. However, due to the errors caused by the position and angle deviation of the strain gauge paste, as well as the sensitivity coefficient of the strain gauge and the wire, it is difficult for the simulation results to correspond to the test results in… More >

  • Open Access

    PROCEEDINGS

    Mechanical Properties of CP Ti Processed via a High-Precision Laser Powder Bed Fusion Process

    Hui Liu1, Xu Song1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011362

    Abstract Because of its higher specific strength and better biocompatibility, commercially pure titanium (CP Ti) is widely used for product fabrication in the aerospace, medical, and other industries. Currently, different ways are adopted to strengthen CP Ti, such as solid-solution strengthening using oxygen or adding metal components to form alloys; however, the introduction of oxygen, other gases, or alloying elements reduces the corrosion resistance and biocompatibility. Herein, CP Ti with a low oxygen content was used to fabricate samples via a high-precision laser powder bed fusion process. Smaller laser beam diameter and thinner layer thickness lead More >

  • Open Access

    ARTICLE

    Precision Motion Control of Hydraulic Actuator Using Adaptive Back-Stepping Sliding Mode Controller

    Zhenshuai Wan1,2,*, Longwang Yue2, Yanfeng Wang2, Pu Zhao2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1047-1065, 2024, DOI:10.32604/cmes.2024.053773 - 27 September 2024

    Abstract Hydraulic actuators are highly nonlinear when they are subjected to different types of model uncertainties and dynamic disturbances. These unfavorable factors adversely affect the control performance of the hydraulic actuator. Although various control methods have been employed to improve the tracking precision of the dynamic system, optimizing and adjusting control gain to mitigate the hydraulic actuator model uncertainties remains elusive. This study presents an adaptive back-stepping sliding mode controller (ABSMC) to enhance the trajectory tracking precision, where the virtual control law is constructed to replace the position error. The adaptive control theory is introduced in More >

  • Open Access

    ARTICLE

    High-Precision Flow Numerical Simulation and Productivity Evaluation of Shale Oil Considering Stress Sensitivity

    Mingjing Lu1,2,*, Qin Qian1, Anhai Zhong1, Feng Yang1, Wenjun He1, Min Li1

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.10, pp. 2281-2300, 2024, DOI:10.32604/fdmp.2024.051594 - 23 September 2024

    Abstract Continental shale oil reservoirs, characterized by numerous bedding planes and micro-nano scale pores, feature significantly higher stress sensitivity compared to other types of reservoirs. However, research on suitable stress sensitivity characterization models is still limited. In this study, three commonly used stress sensitivity models for shale oil reservoirs were considered, and experiments on representative core samples were conducted. By fitting and comparing the data, the “exponential model” was identified as a characterization model that accurately represents stress sensitivity in continental shale oil reservoirs. To validate the accuracy of the model, a two-phase seepage mathematical model More >

  • Open Access

    ARTICLE

    Cartographie Automatique et Comptage des Arbres Oliviers A Partir de L’Imagerie de Drone par Un Reseau de Neurones Covolutionnel

    Oumaima Ameslek1,*, Hafida Zahir2, Soukaina Mitro2, El Mostafa Bachaoui1

    Revue Internationale de Géomatique, Vol.33, pp. 321-340, 2024, DOI:10.32604/rig.2024.054838 - 03 September 2024

    Abstract L’agriculture de précision (AP) est une stratégie de gestion agricole fondée sur l’observation, la mesure et la réponse à la variabilité des cultures inter/intra-champ. Il comprend des avancées en matière de collecte, d’analyse et de gestion des données, ainsi que des développements technologiques en matière de stockage et de récupération de données, de positionnement précis, de surveillance des rendements et de télédétection. Cette dernière offre une résolution spatiale, spectrale et temporelle sans précédent, mais peut également fournir des informations détaillées sur la hauteur de la végétation et diverses observations. Aujourd’hui, le succès des nouvelles technologies… More >

  • Open Access

    ARTICLE

    Genomic profiling of colorectal cancer in large-scale Chinese patients: amplification and somatic mutations in ERBB2

    YUZHI LIU1,#, EVELYNE BISCHOF2,#, ZHIQIN CHEN1, JIAHUAN ZHOU3, BEI ZHANG4, DING ZHANG4, YONG GAO1,*, MING QUAN1,*

    Oncology Research, Vol.32, No.9, pp. 1429-1438, 2024, DOI:10.32604/or.2024.047309 - 23 August 2024

    Abstract Objectives: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have demonstrated potential benefits for metastatic colorectal cancer (mCRC) patients with HER2 amplification, but are not satisfactory in cases of HER2 mutant CRCs. Methods: Consequently, further elucidation of amplifications and somatic mutations in erythroblastic oncogene B-2 (ERBB2) is imperative. Comprehensive genomic profiling was conducted on 2454 Chinese CRC cases to evaluate genomic alterations in 733 cancer-related genes, tumor mutational burden, microsatellite instability, and programmed death ligand 1 (PD-L1) expression. Results: Among 2454 CRC patients, 85 cases (3.46%) exhibited ERBB2 amplification, and 55 cases (2.24%) carried ERBB2 mutation.… More > Graphic Abstract

    Genomic profiling of colorectal cancer in large-scale Chinese patients: amplification and somatic mutations in ERBB2

  • Open Access

    ARTICLE

    Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

    Yuejiao Wang, Zhong Ma*, Chaojie Yang, Yu Yang, Lu Wei

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 819-836, 2024, DOI:10.32604/cmc.2024.047108 - 25 April 2024

    Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to… More >

  • Open Access

    ARTICLE

    Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

    LU ZHANG*, JINGUO CHU*, YUSHAN YU

    Oncology Research, Vol.32, No.4, pp. 703-716, 2024, DOI:10.32604/or.2023.030988 - 20 March 2024

    Abstract Background: Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC). Methods: We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using… More >

  • Open Access

    ARTICLE

    Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

    Nazik Alturki1, Abdulaziz Altamimi2, Muhammad Umer3,*, Oumaima Saidani1, Amal Alshardan1, Shtwai Alsubai4, Marwan Omar5, Imran Ashraf6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3513-3534, 2024, DOI:10.32604/cmes.2023.045868 - 11 March 2024

    Abstract Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) More >

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