A gene expression programme induced by bovine colostrum whey promotes growth and wound-healing processes in intestinal epithelial cells
Bibliographic record
Abstract
Bovine colostrum is well known for its beneficial properties on health and development. It contains a wide variety of bioactive ingredients that are known to promote a number of cellular processes. Therefore the use of colostrum whey as a feed additive to promote intestinal health has been proposed, yet little is known about mechanisms implicated in its beneficial properties on intestinal epithelial cells. In the present paper, casein were removed from bovine colostrum and the remaining liquid, rich in bioactive compounds, was evaluated for its capacity to modulate cellular processes in porcine intestinal epithelial cell line IPEC-J2 and human colon adenocarcinoma cell line Caco-2/15. First, we verified the effect of colostrum whey and cheese whey on processes involved in intestinal wound healing, including cell proliferation, attachment, morphology and migration. Our results showed that colostrum whey promoted proliferation and migration, and decreased specifically the attachment of Caco-2/15 cells on the culture dish. On the other hand, cheese whey induced proliferation and morphological changes in IPEC-J2 cells, but failed to induce migration. The gene expression profile of IPEC-J2 cells following colostrum whey treatment was evaluated by microarray analysis. Results revealed that the expression of a significant number of genes involved in cell migration, adhesion and proliferation was indeed affected in colostrum whey-treated cells. In conclusion, colostrum specific bioactive content could be beneficial for intestinal epithelial cell homoeostasis by controlling biological processes implicated in wound healing through a precise gene expression programme.
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".