The use of a whole gut sac perfusion model to study the ex vivo inflammatory response of rainbow trout (Oncorhynchus mykiss) to lipopolysaccharide
Bibliographic record
Abstract
Ex vivo studies provide a valuable opportunity to study organ-level effects of a compound in an isolated system. As all vertebrate cells can mount an innate immune response, it is important to study the cumulative immune response of multiple cell types that remain in their natural conformation, proportions, and polarity; something that in vitro studies cannot fully provide. This study proposes a simplified static “tie-bag” perfusion model to study the short-term immunological stimulation of rainbow trout middle and distal intestine by Fluorescein isothiocyanate (FITC) tagged lipopolysaccharide (LPS), a component of the gram-negative bacteria cell wall. Middle intestinal segments perfused with 200µg/ml FITC-LPS demonstrated significant increases in the expression of type-I interferon 1 (IFN-1) and tumor necrosis factor alpha (TNF-α) transcripts, while distal intestinal perfusions demonstrated significant increases in TNF-α and interleukin 1β (IL-1β) transcript levels. While FITC-LPS distribution could not be visualized due to overlap with tissue autofluorescence, tissues appeared to only experience minor epithelial sloughing. Overall, this method can be used to evaluate organ-level immune stimulation of antigens while maintaining tissue composition that permits histological examination.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".