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Abstract MicroRNA (miRNA), crucial non‐coding RNAs, have emerged as key biomarkers in molecular diagnostics, prognosis, and personalized medicine due to their significant role in gene expression regulation. Salivary miRNA, in particular, stands out for its non‐invasive collection method and ease of accessibility, offering promising avenues for the development of point‐of‐care diagnostics for a spectrum of diseases, including cancer, neurodegenerative disorders, and infectious diseases. Such development promises rapid and precise diagnosis, enabling timely treatment. Despite significant advancements in salivary miRNA‐based testing, challenges persist in the quantification, multiplexing, sensitivity, and specificity, particularly for miRNA at low concentrations in complex biological mixtures. This work delves into these challenges, focusing on the development and application of salivary miRNA tests for point‐of‐care use. We explore the biogenesis of salivary miRNA and analyze their quantitative expression and their disease relevance in cancer, infection, and neurodegenerative disorders. We also examined recent progress in miRNA extraction, amplification, and multiplexed detection methods. This study offers a comprehensive view of the development of salivary miRNA‐based point‐of‐care testing (POCT). Its successful advancement could revolutionize the early detection, monitoring, and management of various conditions, enhancing healthcare outcomes. This article is categorized under:Diagnostic Tools > BiosensingDiagnostic Tools > Diagnostic Nanodevices
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Designing sensitive viral diagnostics with machine learning
Abstract Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome’s conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic–target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus’s genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT’s designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus’s variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.
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- Award ID(s):
- 2101140
- PAR ID:
- 10369033
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Biotechnology
- Volume:
- 40
- Issue:
- 7
- ISSN:
- 1087-0156
- Page Range / eLocation ID:
- p. 1123-1131
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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