Chicken Egg Yolk Antibodies as Therapeutics in Enteric Infectious Disease: A Review
Bibliographic record
Abstract
Passive immunization by oral administration of specific antibodies has been an attractive approach against gastrointestinal (GI) pathogens in both humans and animals. Recently, laying chickens have attracted considerable attention as an alternative source of antibodies for the prevention and treatment of infectious GI diseases. After immunization, the specific antibodies (called IgY) are transported to the egg yolk, from which the IgY then can be separated without sacrificing chickens. A chicken usually lays about 280 eggs in a year, and egg yolk contains 100-150 mg of IgY per yolk, suggesting that more than 40 g of IgY per year can be obtained from each chicken through eggs. IgY is also an alternative to antibiotics for treatment of enteric antibiotic-resistant pathogens. Oral administration of IgY has proved successful for treatment of a variety of GI infections, such as bovine and human rotaviruses, bovine coronavirus, Yersinia ruckeri, enterotoxigenic Escherichia coli, Salmonella spp., Edwardsiella tarda, Staphylococcus, and Pseudomonas. The IgY technology offers great future opportunities for designing prophylactic strategies against infectious GI diseases in humans and animals. However, there is still controversy regarding the stability of IgY through the GI tract. Finding an effective way to protect the antibodies from degradation in the GI tract would open the door for significant advances in IgY technology and nutraceutical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".