Evidence map›Paper›PMID 38901927›Full record

ArticleThe Journal of molecular diagnostics : JMD2024

CRISPR-Based Assays for Point-of-Need Detection and Subtyping of Influenza.

Yibin B Zhang, Jon Arizti-Sanz, A'Doriann Bradley, Yujia Huang, Tinna-Solveig F Kosoko-Thoroddsen, Pardis C Sabeti, Cameron Myhrvold

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of molecular diagnostics : JMD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Towards deployable CRISPR-based nucleic acid detection.Progress in biomedical engineering (Bristol, England) · 2026
    Review
  9. Article
  10. Review
  11. Article
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yibin B ZhangBroad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, Massachusetts; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts; Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts.
Jon Arizti-SanzBroad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, Massachusetts; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts.
A'Doriann BradleyBroad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, Massachusetts.
Yujia HuangDepartment of Molecular Biology, Princeton University, Princeton, New Jersey.
Tinna-Solveig F Kosoko-ThoroddsenBroad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, Massachusetts.
Pardis C SabetiBroad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, Massachusetts; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts; Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Howard Hughes Medical Institute, Chevy Chase, Maryland.
Cameron MyhrvoldDepartment of Molecular Biology, Princeton University, Princeton, New Jersey; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey; Omenn-Darling Bioengineering Institute, Princeton University, Princeton, New Jersey; Department of Chemistry, Princeton University, Princeton, New Jersey. Electronic address: cmyhrvol@princeton.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The high disease burden of influenza virus poses a significant threat to human health. Optimized diagnostic technologies that combine speed, sensitivity, and specificity with minimal equipment requirements are urgently needed to detect the many circulating species, subtypes, and variants of influenza at the point of need. Here, we introduce such a method using Streamlined Highlighting of Infections to Navigate Epidemics (SHINE), a clustered regularly interspaced short palindromic repeats (CRISPR)-based RNA detection platform. Four SHINE assays were designed and validated for the detection and differentiation of clinically relevant influenza species (A and B) and subtypes (H1N1 and H3N2). When tested on clinical samples, these optimized assays achieved 100% concordance with quantitative RT-PCR. Duplex Cas12a/Cas13a SHINE assays were also developed to detect two targets simultaneously. This study demonstrates the utility of this duplex assay in discriminating two alleles of an oseltamivir resistance (H275Y) mutation as well as in simultaneously detecting influenza A and human RNAse P in patient samples. These assays have the potential to expand influenza detection outside of clinical laboratories for enhanced influenza diagnosis and surveillance.

Indexed as

CRISPR-Cas SystemsInfluenza, HumanClustered Regularly Interspaced Short Palindromic RepeatsHumansInfluenza A virusInfluenza A Virus, H1N1 SubtypeInfluenza A Virus, H3N2 SubtypeMolecular Diagnostic TechniquesRNA, ViralSensitivity and SpecificityRNA, Viral

Identifiers

PMID38901927
PMCPMC12178390

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.