Analysis of gut immune-modulating activity of β-1,4-mannobiose using microarray and real-time reverse transcription polymerase chain reaction
Bibliographic record
Abstract
beta-1,4-Mannobiose (MNB) supplementation has been shown to prevent Salmonella Enteritidis infection in broilers by improving Salmonella Enteritidis clearance and increasing IgA production. This study examined in detail the gut immunomodulatory activity of MNB using microarray and real-time quantitative PCR analysis. One-day-old chicks were orally administered 0.1% (wt/wt) MNB 3 times a week for 28 d. Control birds received vehicle alone. Body weights and fecal IgA levels were monitored weekly. On d 28, spleen and bursa of Fabricius were removed and weights were recorded; samples of ileum, jejunum, cecum, spleen, thymus, and bursa of Fabricius were collected for histological examination; and ileum samples were collected for RNA extraction. No significant difference in BW or organ weights was observed between MNB-treated and untreated control birds, and no histological abnormalities were observed in any of the tissues examined. The MNB-treated chickens had significantly higher levels of fecal IgA over all 4 wk when compared with control birds. Microarray and reverse transcription PCR analysis revealed the upregulation of several genes involved in immune responses, including those involved in antigen recognition, processing and presentation (MHC class I and II), interferon-related genes, and genes involved in host defense. These results provide insight into the mechanism of action of dietary MNB in the intestine and confirm that MNB acts as a potent immune-modulating agent, exerting combined effects on the intestinal immune system.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".