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Record W4401763915 · doi:10.3354/dao03817

PirA- or PirB-binding nanobodies can protect whiteleg shrimp from the acute hepatopancreatic necrosis disease toxin

2024· article· en· W4401763915 on OpenAlexaff
Sarah Cheung, SA Loutet, Sarah J. M. Zaytsoff, Filip Van Petegem, LH Tran, Hamlet Abnousi

Bibliographic record

VenueDiseases of Aquatic Organisms · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyToxinNecrosisPenaeidaeShrimpDecapodaVirologyMicrobiologyFisheryGeneticsCrustacean

Abstract

fetched live from OpenAlex

Acute hepatopancreatic necrosis disease (AHPND) is a devastating shrimp disease caused by a binary toxin, PirAB, produced by Vibrio parahaemolyticus and other closely related bacteria. To address AHPND, over 300 unique single-domain antibodies (also known as nanobodies) derived from the VHH domains of Lama glama heavy-chain-only antibodies were raised against either PirA or PirB and characterized. Nanobodies were shortlisted based on their affinities for either PirA or PirB, their relative stability in intestinal fluids, and their ability to reduce PirAB-induced death in brine shrimp Artemia salina. From these data, a subset of nanobodies was tested for their ability to reduce AHPND in whiteleg shrimp Penaeus vannamei, and nanobodies targeting either PirA or PirB provided significant disease protection to whiteleg shrimp. These results show that nanobodies can be a new option for shrimp farmers to reduce or eliminate the impact of AHPND on their operations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.236
Teacher spread0.223 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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