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| http://dx.doi.org/10.1016/S1473-3099(20)30125-0 | DOI Listing |
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Recent developments and future directions in point-of-care next-generation CRISPR-based rapid diagnosis.
Clin Exp Med
January 2025
Department of Zoology, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt.
The demand for sensitive, rapid, and affordable diagnostic techniques has surged, particularly following the COVID-19 pandemic, driving the development of CRISPR-based diagnostic tools that utilize Cas effector proteins (such as Cas9, Cas12, and Cas13) as viable alternatives to traditional nucleic acid-based detection methods. These CRISPR systems, often integrated with biosensing and amplification technologies, provide precise, rapid, and portable diagnostics, making on-site testing without the need for extensive infrastructure feasible, especially in underserved or rural areas. In contrast, traditional diagnostic methods, while still essential, are often limited by the need for costly equipment and skilled operators, restricting their accessibility.
View Article and Find Full Text PDFLeveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks.
Sci Rep
December 2024
Public Health and community medicine Department, Theodor Bilharz Research Institute, Helwan University, Cairo, Egypt.
Infectious diseases significantly impact both public health and economic stability, underscoring the critical need for precise outbreak predictions to effictively mitigate their impact. This study applies advanced machine learning techniques to forecast outbreaks of Dengue, Chikungunya, and Zika, utilizing a comprehensive dataset comprising climate and socioeconomic data. Spanning the years 2007 to 2017, the dataset includes 1716 instances characterized by 27 distinct features.
View Article and Find Full Text PDFUnsupervised learning analysis on the proteomes of Zika virus.
PeerJ Comput Sci
November 2024
Laboratorio de Biotecnología Genómica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, México.
Article Synopsis
- The Zika virus (ZIKV), primarily spread by mosquitoes, poses significant health challenges, especially in impoverished tropical areas; understanding its spread is crucial for control efforts.
- This study utilized unsupervised machine learning techniques, particularly Random Forest and various dimensional reduction methods, to analyze ZIKV protein data and identify patterns without prior training information.
- Results revealed distinct host and geographical clusters for ZIKV, with UMAP being particularly effective; these methods could help track viral pathogens and could be adapted for studying other viruses like dengue.
Postnatal Zika and Dengue Infection and their Effects on Neurodevelopment Among Children Living in Rural Guatemala.
Pediatr Infect Dis J
December 2024
Department of Molecular Virology & Microbiology, Baylor College of Medicine, Houston, Texas.
Background: Prenatal Zika virus (ZIKV) infection leads to microcephaly and adverse neurodevelopment. The effects of postnatal ZIKV infection on the developing brain are unknown. We assessed the neurodevelopmental outcomes of children exposed postnatally during the ZIKV epidemic.
View Article and Find Full Text PDFRepurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction.
Interdiscip Sci
December 2024
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, USA.
Article Synopsis
- - This study focuses on using computational systems biology to quickly develop effective treatments for emerging infectious diseases by repurposing existing drugs based on diverse data like gene expression and molecular interactions.
- - Researchers introduce a new method for sensitivity analysis to improve predictions in drug targeting, employing Graph Convolutional Networks to identify significant genes and pathways related to diseases like Zika and COVID-19.
- - The approach integrates a wide range of proteins and drugs to identify top candidates for treatment, validating results through existing research, showcasing a scalable method for addressing urgent healthcare needs.
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