Characterization of Dosage Levels for In Ovo Administration of Innate Immune Stimulants for Prevention of Yolk Sac Infection in Chicks
Bibliographic record
Abstract
Innate immune stimulants, especially toll-like receptor (TLR) ligands and agonists, are the main players in the initiation of innate immunity and have been widely studied as alternatives to antibiotics to control infection. In the present study, we characterized the dosage levels of various innate immune stimulants, including unmethylated cytosine-phosphate-guanosine dinucleotide -containing oligodeoxynucleotides (CpG ODN), polyinosinic-polycytidylic acid (poly I:C), cyclic polyphosphazene 75B (CPZ75B), avian beta-defensin 2 (ABD2), and combinations of these reagents given in ovo. Data derived from a series of animal experiments demonstrated that the in ovo administration of 10–50 µg CpG ODN/embryo (on embryonic day 18) is an effective formulation for control of yolk sac infection (YSI) due to avian pathogenic Escherichia coli (E. coli) in young chicks. Amongst the different combinations of innate immune stimulants, the in ovo administration of CpG ODN 10 µg in combination with 15 µg of poly I:C was the most effective combination, offering 100% protection from YSI. It is expected that the introduction of these reagents to management practices at the hatchery level may serve as a potential replacement for antibiotics for the reduction of early chick mortality (ECM) due to YSI/colibacillosis.
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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 | 0.001 |
| 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".