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Record W4403990713 · doi:10.5376/gab.2024.15.0028

Harnessing Gene Editing Tools to Study ASFV Pathogenesis

2024· article· en· W4403990713 on OpenAlexvenueno aff
Xiaofang Lin

Bibliographic record

VenueGenomics and Applied Biology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsGenome editingPathogenesisBiologyGeneticsGeneComputational biologyComputer scienceCRISPRImmunology

Abstract

fetched live from OpenAlex

The study utilizes advanced gene editing tools, specifically the CRISPR/Cas9 system, to investigate the pathogenesis of African Swine Fever Virus (ASFV) by creating recombinant virus strains with targeted gene deletions. The study successfully demonstrated the application of CRISPR/Cas9 to delete key immune response modulation genes (A238L, EP402R, and 9GL) in ASFV. The reconstituted virus exhibited similar replication kinetics to the parent virus, indicating that these genes can be modified with low frequency. Additionally, the use of CRISPR/Cas9 significantly accelerated the production of recombinant ASFV strains, reducing the time required from several months to less than two months. The study also highlighted the potential of CRISPR/Cas12a for sensitive and specific detection of ASFV, which could be crucial for on-site diagnostics and control of ASF outbreaks. The findings underscore the utility of CRISPR/Cas9 and CRISPR/Cas12a systems in both the study of ASFV pathogenesis and the development of rapid diagnostic tools. These advancements could pave the way for more effective control measures and the potential development of live-attenuated vaccines for ASFV.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.259
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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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