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Record W4404427765 · doi:10.5376/gab.2024.15.0013

Advancements in Gene Editing Technologies for Mosquito Research

2024· article· en· W4404427765 on OpenAlexvenueno aff
Xiaojie Liu, Kai Chen, Zhongqi Wu

Bibliographic record

VenueGenomics and Applied Biology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGenome editingBiologyComputational biologyComputer scienceGeneGeneticsCRISPR

Abstract

fetched live from OpenAlex

The rapid advancements in gene editing technologies, particularly CRISPR/Cas9, have significantly impacted mosquito research, offering novel strategies for vector control and disease prevention. This study explores the latest developments in gene editing techniques applied to various mosquito species, including Anopheles , Aedes , and Culex . Key innovations such as the ReMOT Control method for efficient gene editing without embryo injections, the design and validation of guide RNAs, and the assessment of off-target effects in gene drives are discussed. Additionally, this study highlights the application of CRISPR/Cas9 in manipulating mosquito symbionts and developing gene drive systems to combat insecticide resistance. These advancements not only enhance our understanding of mosquito biology but also pave the way for sustainable and effective vector control strategies.

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)0.000
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 score0.224
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.379
Teacher spread0.360 · 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
Published2024
Admission routes1
Has abstractyes

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