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Record W4404427615 · doi:10.5376/gab.2024.15.0011

Efficient Gene Transfer Techniques in Shrimp and Their Cellular Applications

2024· article· en· W4404427615 on OpenAlexvenueno aff
Fei Zhao, Fan Wang

Bibliographic record

VenueGenomics and Applied Biology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsShrimpGene transferTransfer (computing)GeneComputational biologyComputer scienceBiologyGeneticsFisheryOperating system

Abstract

fetched live from OpenAlex

This study compared the efficiency of microinjection, electroporation, and transfection methods for gene transfer into shrimp zygotes. Transfection using the jetPEI reagent demonstrated the highest efficiency, with hatching rates of 50%-60% and gene expression rates of 40%-60%. Additionally, a VP28-pseudotyped baculovirus system achieved up to 100% infection efficiency in adult shrimp tissues, although it exhibited tissue-specific tropism. A triple-pseudotyped retroviral system also showed promise, particularly in shrimp primary lymphoid cells, with infection efficiencies of 20%-30%. Furthermore, the inclusion of shrimp-specific promoters and viral envelope proteins significantly enhanced the tropism and infectivity of lentiviral vectors in shrimp cells. The findings indicate that transfection with jetPEI and the use of pseudotyped viral systems are highly effective for gene transfer in shrimp. These methods hold significant potential for advancing genetic manipulation and cellular studies in shrimp, which could lead to improved disease resistance and other desirable traits in aquaculture.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.209
Teacher spread0.204 · 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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