Introduction: Dengue viruses (DENVs), the causative agents of dengue hemorrhagic fever and dengue shock syndrome, undergo genetic mutations that result in new strains and lead to ongoing global re-infections.
Objectives: To address the growing complexity of identifying and tracking biological samples, this study screened RNA barcode segments for the four DENV serotypes, ensuring high specificity and recall rates for DENV identification using segments.
Results: Through analyzing complete genome sequences of DENVs, we screened eight barcode segments for DENV, DENV-1, DENV-2, DENV-3, and DENV-4 identification. Comparing the screened barcode segments to sequences of known strains and determining the proportion of correctly or incorrectly identified nucleotides, these segments demonstrated an average recall rate at nucleotide level of 91.34% for four DENV serotypes, a specificity of 99.50% at species level within the family, and a precision rate of 100% for identifying DENVs. For arboviruses, the nucleotide-level specificity was 63.58%. We designed and used the "Barcoding" software to streamline segment design, integrating automated sequence preprocessing, evaluation of barcode segments, and primer design, significantly reducing manual intervention and enhancing overall efficiency. We also established an online database called "Barcodes" for storing and preparing barcode segments.
Conclusion: This work established a standard framework for DENV identification and barcode segment selection, promising significant advancements in the real-time management and control of DENVs, thereby enhancing surveillance capabilities and facilitating targeted interventions in dengue outbreak-prone regions.
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http://dx.doi.org/10.3389/fmicb.2024.1474406 | DOI Listing |
Front Microbiol
December 2024
College of Biology, Hunan University, Changsha, China.
Introduction: Dengue viruses (DENVs), the causative agents of dengue hemorrhagic fever and dengue shock syndrome, undergo genetic mutations that result in new strains and lead to ongoing global re-infections.
Objectives: To address the growing complexity of identifying and tracking biological samples, this study screened RNA barcode segments for the four DENV serotypes, ensuring high specificity and recall rates for DENV identification using segments.
Results: Through analyzing complete genome sequences of DENVs, we screened eight barcode segments for DENV, DENV-1, DENV-2, DENV-3, and DENV-4 identification.
Ultrasound Med Biol
January 2025
Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province, China.
Med Image Anal
January 2025
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China. Electronic address:
Radiation therapy is a primary and effective treatment strategy for NasoPharyngeal Carcinoma (NPC). The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Despite that deep learning has achieved remarkable performance on various medical image segmentation tasks, its performance on OARs and GTVs of NPC is still limited, and high-quality benchmark datasets on this task are highly desirable for model development and evaluation.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
December 2024
TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160, Derio - Bizkaia, Spain; University of the Basque Country, Plaza Torres Quevedo, 48013 Bilbao, Spain.
Am J Otolaryngol
November 2024
Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Background: Hyperostosis is a common radiographic feature of inverted papilloma (IP) tumor origin on computed tomography (CT). Herein, we developed a machine learning (ML) model capable of analyzing CT images and identifying IP attachment sites.
Methods: A retrospective review of patients treated for IP at our institution was performed.
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