Characterisation of a Canine Epithelial Cell Line for Modelling the Intestinal Barrier
Bibliographic record
Abstract
Little is known about how food interacts with the intestinal epithelium during the digestion process. However, it is known that ingredients in food can modulate the intestinal barrier, and have the potential to disrupt homeostasis of the gut. Here, we characterise a conditionally immortalised canine intestinal epithelial cell (cIEC) line for use in in vitro assays, to assess the effect of food ingredients on intestinal barrier function, permeability, cell health, and inflammation. Microscopy and flow cytometry confirmed that cIECs had a phenotype consistent with those of epithelial origin, and were able to differentiate to mature enterocytes. The cIECs also formed a monolayer when grown on Transwell® inserts, producing functional tight junctions between the cells. In contrast to the human-derived Caco-2 cell line, transepithelial electrical resistance (TEER) was increased in cIECs in response to two different raw ingredients. The exposure of cIECs to known inflammatory stimuli and raw ingredients induced the nuclear translocation of nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-?B). This work demonstrates the value of a unique cIEC in vitro model to study the effects of food ingredients on canine intestinal function and health, and supports continued efforts to reduce and refine the use of animals in scientific research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".