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Development of paratransgenic <i>Artemia</i> as a platform for control of infectious diseases in shrimp mariculture

2009· article· en· W2049228616 on OpenAlexaff
Bindu Subhadra, Ivy Hurwitz, Annabeth Fieck, D. V. Subba Rao, G. Subba Rao, Ravi Durvasula

Bibliographic record

VenueJournal of Applied Microbiology · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsBedford Institute of Oceanography
FundersNational Center for Research ResourcesNational Cancer InstituteHealth Sciences Center, University of New Mexico
KeywordsLibrary scienceDisease controlMedicineComputer science

Abstract

fetched live from OpenAlex

AIM: To study the accumulation and retention of recombinant proteins in Artemia gut for optimizing paratransgenic disease control in shrimp aquaculture. METHODS AND RESULTS: Transgenic Escherichia coli expressing fluorescent marker proteins and the transgenic cyanobacterium Synechococcus bacillarus expressing a functional murine single chain antibody, DB3, were fed to Artemia franciscana. Stable expression and retention of several marker molecules (e.g. GFP, DS Red and DB3) up to 10 h after of feeding with E. coli were evident within the gut of Artemia. Engineered strains of S. bacillarus expressing DB3 accumulated within the gut of Artemia with detectable antibody activity for 8-10 h of feeding via ELISA, coincident with the time period of the highest density of transgenic S. bacillarus in the Artemia gut. CONCLUSIONS: Artemia fed transgenic bacteria or algae accumulated recombinant proteins for up to 10 h that retained biological activity. Co-delivery of multiple recombinant proteins simultaneously in the gut of Artemia was also demonstrated. SIGNIFICANCE AND IMPACT OF THE STUDY: Expression of molecules that target infectious agents of mariculture in shrimp via commonly deployed feed organisms such as Artemia could potentially offer powerful new tools in the ongoing global effort to increase food supply.

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 score0.010
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

Explore more

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