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Record W4389318264 · doi:10.5376/jmr.2023.13.0002

The Method and Prospects of Changing Mosquito Genes with CRISPRCas9

2023· article· en· W4389318264 on OpenAlexvenueno aff
Mengyi Xu

Bibliographic record

VenueJournal of Mosquito Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCRISPRGenome editingSustainabilityCas9Gene driveHuman healthBalance of natureEmerging technologiesRisk analysis (engineering)BiologyComputer scienceEcologyBusinessGeneMedicineGeneticsEnvironmental health

Abstract

fetched live from OpenAlex

CRISPR-Cas9 technology, as a precise and efficient gene editing tool, has attracted much attention in the field of mosquito gene editing. The aim of this review is to explore the potential of CRISPR-Cas9 application in mosquito gene editing, the challenges and the importance of environmental health balance. This review discusses how this technology can provide new strategies for controlling mosquito-borne diseases, such as modulating antiviral genes and reproductive capacity, through targeted editing of mosquito genes, and also recognizes technical challenges such as guide RNA design and non-targeted editing, as well as ecological risks that may be triggered by editing. Against this background, this review emphasizes the need to balance scientific and technological development with environmental health to ensure that the application of editing technologies does not cause irreversible impacts on the environment and ecosystems, and that while advancing scientific and technological progress, the balance between technological development and environmental health must be carefully weighed in order to achieve the dual goals of human health and ecological sustainability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.432
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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