Development of paratransgenic <i>Artemia</i> as a platform for control of infectious diseases in shrimp mariculture
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".