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Predicting Carbonic Anhydrase Binding Affinity: Insights from QM Cluster Models.
J Phys Chem B
January 2025
School of Chemistry, The University of New South Wales, Sydney, NSW 2052, Australia.
A systematic series of QM cluster models has been developed to predict the trend in the carbonic anhydrase binding affinity of a structurally diverse dataset of ligands. Reference DLPNO-CCSD(T)/CBS binding energies were generated for a cluster model and used to evaluate the performance of contemporary density functional theory methods, including Grimme's "3c" DFT composite methods (rSCAN-3c and ωB97X-3c). It is demonstrated that when validated QM methods are used, the predictive power of the cluster models improves systematically with the size of the cluster models.
View Article and Find Full Text PDFScreening of Oncogenic Proteins and Development of a Multiepitope Peptide Vaccine Targeting AKT1 and PARP1 for Breast Cancer by Integrating Reverse Vaccinology and Immune-Informatics Approaches.
Asian Pac J Cancer Prev
January 2025
Parul Institute of Applied Sciences, Parul University, Vadodara, India.
Background: Breast cancer remains a significant global health challenge, requiring innovative therapeutic strategies. In silico methods, which leverage computational tools, offer a promising pathway for vaccine development. These methods facilitate antigen identification, epitope prediction, immune response modelling, and vaccine optimization, accelerating the design process.
View Article and Find Full Text PDFImmunohistochemical Expression of PD-L1 and CTLA-4 in Triple Negative Breast Cancer and Their Prognostic Associations.
Asian Pac J Cancer Prev
January 2025
Department of Pathology, Faculty of Medicine, Mansoura University, Mansoura, Egypt.
Objective: Programmed Death-Ligand 1 (PD-L1) and Cytotoxic T Lymphocyte -Associated Antigen-4 (CTLA-4) are presently considered as prognostic markers and therapeutic targets in numerous human malignancies. The goal of this study was to determine whether PD-L1 and CTLA-4 might be used to predict patients' survival in Triple Negative Breast Cancer (TNBC).
Methods: This retrospective cohort study analyzed 100 primary TNBC cases that had surgical resection at the Oncology Center of Mansoura University (OCMU), Faculty of Medicine, Egypt.
Analyzing Secondary Cancer Risk: A Machine Learning Approach.
Asian Pac J Cancer Prev
January 2025
Department of Physics, Faculty of Sciences, Arak University, Arak, Iran.
Objective: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors including treatment modalities, lifestyle choices, and habits such as smoking and alcohol consumption. This study aims to establish a novel relationship using linear regression models between dose and the risk of SC, comparing different prediction methods for lung, colon, and breast cancer.
View Article and Find Full Text PDFThe Role of Tumor-Associated Neutrophils in Early Luminal HER2-Negative Breast Cancer Progression.
Asian Pac J Cancer Prev
January 2025
D1 S.P. Botkin City Clinical Hospital, Moscow, Russia.
Objectives: To study the predictive role of tumor-associated neutrophils in early luminal HER2-negative breast cancer.
Materials And Methods: This is a retrospective study conducted on 60 women cases aged from 31 to 79 years underwent surgery for luminal HER2-negative ductal breast cancer in tertiary care cancer centre. We first estimated basic morphological signs: tumor size, tumor grade (by Nottingham Histologic Score), tumor infiltrating lymphocytes (TILs), Lymphovascular invasion, hormonal receptors status, proliferative index, and regional lymph nodes metastasis.
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