Generation of Porcine and Rainbow Trout 3D Intestinal Models and Their Use to Investigate Astaxanthin Effects In Vitro
Bibliographic record
Abstract
Astaxanthin (AST) is a natural compound derived from shellfish, microorganisms, and algae, with several healthy properties. For this reason, it is widely used in the diet of humans and animals, such as pigs, broilers, and fish, where its addition is related to its pigmenting properties. Moreover, AST's ability to reduce free radicals and protect cells from oxidative damage finds application during the weaning period, when piglets are exposed to several stressors. To better elucidate the mechanisms involved, here we generate ad hoc pig and rainbow trout in vitro platforms able to mimic the intestinal mucosa. The morphology is validated through histological and molecular analysis, while functional properties of the newly generated intestinal barriers, both in porcine and rainbow trout models, are demonstrated by measuring trans-epithelial electrical resistance and analyzing permeability with fluorescein isothiocyanate-dextran. Exposure to AST induced a significant upregulation of antioxidative stress markers and a reduction in the transcription of inflammation-related interleukins. Altogether, the present findings demonstrate AST's ability to interact with the molecular pathways controlling oxidative stress and inflammation both in the porcine and rainbow trout species and suggest AST's positive role in prevention and health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".