Four types of human breast lesions and C3H mouse mammary adenocarcinomas (type A) were examined for the immunocytochemical localization of cells containing hormone-like substances. Insulin- or somatostatin-like immunoreactive material was observed in scattered single cells and nests of tumor cells in seven of eight infiltrating duct carcinomas, and in the majority of tumor cells from an anaplastic carcinoma. A few somatostatin-immunoreactive cells were observed in only one of seven fibroadenomas studied. No immunoreactive cells were observed in mouse adenocarcinomas or in human breast dysplasias. These results suggest that cells with hormone-like immunoreactivity may be a common feature in two types of malignant human breast tumors.
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http://dx.doi.org/10.1016/0024-3205(84)90138-3 | DOI Listing |
Breast Cancer Res Treat
January 2025
Google Health, 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA.
Purpose: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelerate the time to diagnostic imaging and biopsy diagnosis.
Methods: In this prospective randomized, unblinded, controlled implementation study we enrolled 1000 screening participants between March 2021 and May 2022.
Mol Biol Rep
January 2025
Department of Clinical Pathology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Background: The identification of circulating potential biomarkers may help earlier diagnosis of breast cancer, which is critical for effective treatment and better disease outcomes. We aimed to study the role of circ-FAF1 as a diagnostic biomarker in female breast cancer using peripheral blood samples of these patients, and to investigate the relation between circ-FAF1 and different clinicopathological features of the included patients.
Methods And Results: This case-control study enrolled 60 female breast cancer patients and 60 age-matched healthy control subjects.
Sci Rep
January 2025
Section General Internal Medicine, Department of Internal Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
Breast Implant Illness (BII) is characterized by a cluster of systemic and local symptoms affecting a subset of women with silicone breast implants. While symptom improvement is frequently observed following implant removal, the underlying mechanisms remain poorly understood, and the absence of reliable biomarkers complicates clinical decision-making. Here, we investigate inflammatory protein profiles in 43 women with BII, comparing pre- and post-explantation levels using the Olink Target 96 Inflammation panel and Meso Scale Discovery technology for absolute quantification.
View Article and Find Full Text PDFSci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
View Article and Find Full Text PDFSci Rep
January 2025
School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
This study presents an advanced dynamic finite element (FE) model of multiple components of the breast to examine the biomechanical impact of different types of physical activities and activity intensity on the breast tissues. Using 4D scanning and motion capture technologies, dynamic data are collected during different activities. The accuracy of the FE model is verified based on relative mean absolute error (RMAE), and optimal material parameters are identified by using a validated stepwise grid search method.
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