Improving a fish intestinal barrier model by combining two rainbow trout cell lines: epithelial RTgutGC and fibroblastic RTgutF
Bibliographic record
Abstract
An in vitro model of the fish intestine is of interest for research and application in diverse fields such as fish physiology, aquaculture and chemical risk assessment. The recently developed epithelial barrier model of the fish intestine relies on the RTgutGC cell line from rainbow trout (Oncorhynchus mykiss), cultured in inserts on permeable membranes. Our aim was to extend the current system by introducing intestinal fibroblasts as supportive layer in order to reconstruct the epithelial-mesenchymal interface as found in vivo. We therefore initiated and characterized the first fibroblast cell line from the intestine of rainbow trout, which has been termed RTgutF. Co-culture studies of RTgutGC and RTgutF were performed on commercially available electric cell substrate for impedance sensing (ECIS) and on newly developed ultrathin, highly porous alumina membranes to imitate the cellular interaction with the basement membrane. Cellular events were examined with non-invasive impedance spectroscopy to distinguish between barrier tightness and cell density in the ECIS system and to determine transepithelial electrical resistance for cells cultured on the alumina membranes. We highlight the relevance of the piscine intestinal fibroblasts for an advanced intestinal barrier model, particularly on ultrathin alumina membranes. These membranes enable rapid crosstalk of cells cultured on opposite sides, which led to increased barrier tightening in the fish cell line-based epithelial-mesenchymal model.
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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) | 0.000 | 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 | 0.001 |
| 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".