The Impact of Diet on Expression of Genes Involved in Innate Immunity in Goat Blood
Bibliographic record
Abstract
Sericea Lespedeza (SL), is a high-quality, low input forage that suppresses gastro-intestinal parasites in goats. The effect of dietary SL on the expression of genes involved in innate immunity in goats has not been established. The objective of this study was to evaluate the impact of a diet containing SL on the expression of genes involved in innate immunity in goat blood. Blood was collected by jugular venipuncture from goats fed a diet of 75% SL (n = 9) and a control group (n = 7), fed a SL free diet. Blood was used to evaluate expression of (CD-14, TLR-2, TLR-4, IL-10, IL-8, IL-2, INF-r, and TNF-a). Serum was extracted and used for evaluation of the secretion of pro-inflammatory cytokines (TNF-a, IFNr, granulocyte colony stimulating factor (GCSF), granulocyte-macrophage colony-stimulating factor (GMCSF), IL-1a, IL-8, IP-10 and RANTES) using a commercial ELISA kit. The level of gene expression of CD-14, TLR-2, TLR-4, IL-10, IL-8, IL-2, INF-r, and TNF-a was higher in treated animals compared to control. The Sericea Lespedeza diet affected the secretion of pro-inflammatory cytokines by increasing the serum levels of TNF-a, IFNr, GCSF, GMCSF, IL-1a, IP-10 (P < 0.0002), and by decreasing (P < 0.0001) IL-8 and RANTES in blood from goats fed SL. This suggests that dietary tannins modulate gene expression and may affect the goat's innate immune response in blood. Further research is needed to understand and harness the effect of dietary condensed tannins to modulate innate immunity in goats.
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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 | 0.000 | 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".