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http://dx.doi.org/10.3389/fimmu.2022.1075335 | DOI Listing |
Int J Oncol
February 2025
Department of Experimental Medicine, Laboratory of Radiobiology, University of L'Aquila, I-67100 L'Aquila, Italy.
Following the publication of the above article, a concerned reader drew to the Editor's attention that certain of the western blot assay data shown in Fig. 4G on p. 717 were strikingly similar to data that had appeared in a paper published previously in the journal , which had been written by different authors at different research institutes.
View Article and Find Full Text PDFEye Vis (Lond)
January 2025
ELZA Institute AG, Bahnhofstrasse 15, 8001, Zurich, Switzerland.
Background: Acanthamoeba keratitis (AK) is the most challenging corneal infection to treat, with conventional therapies often proving ineffective. While photoactivated chromophore for keratitis-corneal cross-linking (PACK-CXL) with riboflavin/UV-A has shown success in treating bacterial and fungal keratitis, and PACK-CXL with rose bengal/green light has demonstrated promise in fungal keratitis, neither approach has been shown to effectively eradicate AK. This case study explores a novel combined same-session treatment approach using both riboflavin/UV-A and rose bengal/green light in a single procedure.
View Article and Find Full Text PDFFront Oncol
December 2024
Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, Pavia, Italy.
Background: Interstitial lung diseases (ILDs) comprise a family of heterogeneous entities, primarily characterised by chronic scarring of the lung parenchyma. Among ILDs, idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial pneumonitis, associated with progressive functional decline leading to respiratory failure, a high symptom burden, and mortality. Notably, the incidence of lung cancer (LC) in patients already affected by ILDs-mainly IPF-is significantly higher than in the general population.
View Article and Find Full Text PDFEur Radiol
December 2024
Department of Radiology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Objective: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.
Materials And Methods: We compare a multi-organ segmentation approach and the fusion of multiple single-organ models, each dedicated to one OAR. All were trained using nnU-Net with the default parameters and the full-resolution configuration.
Front Med (Lausanne)
December 2024
Department of Physics, Faculty of Science, Minia University, Minia, Egypt.
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