Importance: Pathogenesis of acute radiation dermatitis (ARD) is not completely understood. Pro-inflammatory cutaneous bacteria may contribute to cutaneous inflammation after radiation therapy.
Objective: To evaluate whether nasal colonization with Staphylococcus aureus (SA) before radiation therapy is associated with ARD severity in patients with breast or head and neck cancer.
Design, Setting, And Participants: This prospective cohort study with observers blinded to colonization status was conducted from July 2017 to May 2018 at an urban academic cancer center. Patients aged 18 years or older with breast or head and neck cancer and plans for fractionated radiation therapy (≥15 fractions) with curative intent were enrolled via convenience sampling. Data were analyzed from September to October 2018.
Exposures: Staphylococcus aureus colonization status before radiation therapy (baseline).
Main Outcomes And Measures: The primary outcome was ARD grade using the Common Terminology Criteria for Adverse Event Reporting, version 4.03.
Results: Among 76 patients analyzed, mean (SD) age was 58.5 (12.6) years and 56 (73.7%) were female. All 76 patients developed ARD: 47 (61.8%) with grade 1, 22 (28.9%) with grade 2, and 7 (9.2%) with grade 3. The prevalence of baseline nasal SA colonization was higher among patients who developed grade 2 or higher ARD compared with those who developed grade 1 ARD (10 of 29 [34.5%] vs 6 of 47 [12.8%]; P = .02, by χ2 test).
Conclusions And Relevance: In this cohort study, baseline nasal SA colonization was associated with development of grade 2 or higher ARD in patients with breast or head and neck cancer. The findings suggest that SA colonization may play a role in the pathogenesis of ARD.
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http://dx.doi.org/10.1001/jamaoncol.2023.0454 | DOI Listing |
Mol Cancer
January 2025
Foshan Maternity and Child Healthcare Hospital; School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 515150, China.
Background: Intratumor-resident bacteria represent an integral component of the tumor microenvironment (TME). Microbial dysbiosis, which refers to an imbalance in the bacterial composition and bacterial metabolic activities, plays an important role in regulating breast cancer development and progression. However, the impact of specific intratumor-resident bacteria on tumor progression and their underlying mechanisms remain elusive.
View Article and Find Full Text PDFGeroscience
January 2025
Dept. of Bioinformatics, Semmelweis University, 1094, Budapest, Hungary.
Glucagon-like peptide-1 receptor (GLP-1R) agonists, such as exenatide (Byetta, Bydureon), liraglutide (Victoza, Saxenda), albiglutide (Tanzeum), dulaglutide (Trulicity), lixisenatide (Lyxumia, Adlyxin), semaglutide (Ozempic, Rybelsus, Wegovy), and tirzepatide (Mounjaro, Zepbound), are widely used for the treatment of type 2 diabetes mellitus (T2DM) and obesity. While these agents are well known for their metabolic benefits, there is growing interest in their potential effects on cancer biology. However, the role of GLP-1R agonists in cancer remains complex and not fully understood, particularly across different tumor types.
View Article and Find Full Text PDFOncol Lett
March 2025
Department of Hematology, Shengli Oilfield Central Hospital, Dongying, Shandong 257000, P.R. China.
The FAT atypical cadherin 1 (FAT1) gene is the ortholog of the fat gene and encodes the protocadherin FAT1. FAT1 belongs to the cadherin superfamily, a group of full-length membrane proteins that contain cadherin-like repeats. In various types of human cancer, FAT1 is one of the most commonly mutated genes, and is considered to be an emerging cancer biomarker and a potential target for novel therapies.
View Article and Find Full Text PDFVis Comput Ind Biomed Art
January 2025
Faculty of Sciences, Sfax, Tunisia.
The vision transformer (ViT) architecture, with its attention mechanism based on multi-head attention layers, has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medical image information. ViTs are notably recognized for their complex architecture, which requires high-performance GPUs or CPUs for efficient model training and deployment in real-world medical diagnostic devices. This renders them more intricate than convolutional neural networks (CNNs).
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
Background/objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers.
Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs).
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