Evidence map›Paper›PMID 37439822›Full record

ArticleThe CRISPR journal2023

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning.

Benjamín Durán-Vinet, Karla Araya-Castro, Anastasija Zaiko, Xavier Pochon, Susanna A Wood, Jo-Ann L Stanton, Gert-Jan Jeunen, Michelle Scriver, Anya Kardailsky, Tzu-Chiao Chao and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The CRISPR journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. CRISPR/Cas system and its application in the diagnosis of animal infectious diseases.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2024
    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

14 authors.

Benjamín Durán-VinetDepartment of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand; Berkeley, Berkeley, California, USA.ORCID 0000-0003-1859-9894
Karla Araya-CastroScientific and Technological Bioresource Nucleus (BIOREN-UFRO), Universidad de La Frontera, Temuco, Chile; Berkeley, Berkeley, California, USA.
Anastasija ZaikoCawthron Institute, Nelson, New Zealand; Berkeley, Berkeley, California, USA.
Xavier PochonCawthron Institute, Nelson, New Zealand; Berkeley, Berkeley, California, USA.
Susanna A WoodCawthron Institute, Nelson, New Zealand; Berkeley, Berkeley, California, USA.
Jo-Ann L StantonDepartment of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand; Berkeley, Berkeley, California, USA.
Gert-Jan JeunenDepartment of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand; Berkeley, Berkeley, California, USA.
Michelle ScriverCawthron Institute, Nelson, New Zealand; Berkeley, Berkeley, California, USA.
Anya KardailskyDepartment of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand; Berkeley, Berkeley, California, USA.
Tzu-Chiao ChaoInstitute of Environmental Change and Society, Department of Biology, University of Regina, Regina, Canada; Berkeley, Berkeley, California, USA.
Deependra K BanKeck Graduate Institute, The Claremont Colleges, Claremont, California, USA; Berkeley, Berkeley, California, USA.
Maryam MoarefianKeck Graduate Institute, The Claremont Colleges, Claremont, California, USA; Berkeley, Berkeley, California, USA.
Kiana AranKeck Graduate Institute, The Claremont Colleges, Claremont, California, USA; Berkeley, Berkeley, California, USA.
Neil J GemmellDepartment of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand; Berkeley, Berkeley, California, USA.

Funding

CRISPR-based transistors for high throughput multiplexed monitoring of CRISPR-based editing efficiency for Sickle cells diseaseR01HL161361 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ARAN, KIANA · 2022 to 2025
$1.7M
NHLBI NIH HHS R01 HL161361
6 · The paper itself

Abstract

Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect marine systems and the ecosystem services they provide. Molecular tools have emerged as a promising solution for marine biomonitoring. One of the latest advancements involves utilizing CRISPR-Cas technology to build programmable, rapid, ultrasensitive, and specific diagnostics. CRISPR-based diagnostics (CRISPR-Dx) has the potential to allow robust, reliable, and cost-effective biomonitoring in near real time. However, several challenges must be overcome before CRISPR-Dx can be established as a mainstream tool for marine biomonitoring. A critical unmet challenge is the need to design, optimize, and experimentally validate CRISPR-Dx assays. Artificial intelligence has recently been presented as a potential approach to tackle this challenge. This perspective synthesizes recent advances in CRISPR-Dx and machine learning modeling approaches, showcasing CRISPR-Dx potential to progress as a rising molecular tool candidate for marine biomonitoring applications.

Indexed as

CRISPR-Cas SystemsDeep LearningArtificial IntelligenceBiological MonitoringEcosystemGene EditingRNARNA

Identifiers

PMID37439822
PMCPMC10494903

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.