Intestinal Inflammation Induced by Soybean Meal Ingestion Increases Intestinal Permeability and Neutrophil Turnover Independently of Microbiota in Zebrafish
Bibliographic record
Abstract
Intestinal inflammation is a condition shared by several intestinal chronic diseases, such as Crohn's disease and ulcerative colitis, with severely detrimental consequences in the long run. Current mammalian models have considerably increased understanding of this pathological condition, highlighting the fact that, in most of the cases, it is a highly complex and multifactorial problem and difficult to deal with. Thus, there is an increasingly evident need for alternative animal models that could offer complementary approaches that have not been exploited in rodents, thereby contributing to a different view on the disease. Here, we report the effects of a soybean meal-induced intestinal inflammation model on intestinal integrity and function as well as on neutrophil recruitment and microbiota composition in zebrafish. We find that the induced intestinal inflammation process is accompanied by an increase in epithelial permeability in addition to changes in the mRNA levels of different tight junction proteins. Conversely, there was no evidence of damage of epithelial cells nor an increase in their proliferation. Of note, our results show that this intestinal inflammatory model is induced independently of the presence of microbiota. On the other hand, this inflammatory process affects intestinal physiology by decreasing protein absorption, increasing neutrophil replacement, and altering microbiota composition with a decrease in the diversity of cultivable bacteria.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".