Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a software application was developed to assist the radiologist in the analysis of thoracic CT data for the purpose of evaluating the response to tumor therapy. The application provides follow-up support for monitoring of tumor therapy by means of volumetric quantification of tumors and temporal registration. In addition, anatomically adequate three-dimensional visualization techniques for convenient examination of large data sets are included. With close cooperation between computer scientists and radiologists, the application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out, the results of which indicated that the application improves therapy monitoring with respect to accuracy and time required.
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http://dx.doi.org/10.1148/rg.253045163 | DOI Listing |
Sci Rep
December 2024
Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
The mungbean yellow mosaic India virus (MYMIV, Begomovirus vignaradiataindiaense) causes Yellow Mosaic Disease (YMD) in mungbean (Vigna radiata L.). The biochemical assays including total phenol content (TPC), total flavonoid content (TFC), ascorbic acid (AA), DPPH (2,2-diphenyl-1-picrylhydrazyl), and FRAP (Ferric Reducing Antioxidant Power) were used to study the mungbean plants defense response to MYMIV infection.
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December 2024
Computer Science Department, Saarland University, Saarbrücken, Germany.
Estimating the numbers and whereabouts of internally displaced people (IDP) is paramount to providing targeted humanitarian assistance. In conflict settings like the ongoing Russia-Ukraine war, on-the-ground data collection is nevertheless often inadequate to provide accurate and timely information. Satellite imagery may sidestep some of these challenges and enhance our understanding of the IDP dynamics.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
The Epstein-Barr virus (EBV) is widespread and has been related to a variety of malignancies as well as infectious mononucleosis. Despite the lack of a vaccination, antiviral medications offer some therapy alternatives. The EBV BZLF1 gene significantly impacts viral replication and infection severity.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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