COMPETITIVE AND DOUBLE ANTIBODY SANDWICH ELISA FOR THE QUANTIFICATION OF LACTOFERRINS BY USING MONOCLONAL AND CHICKEN EGG YOLK IgY ANTIBODIES
Bibliographic record
Abstract
Two effective competitive and double antibody sandwich ELISA based on monoclonal (MAb) and chicken egg yolk IgY antibodies were developed to determine lactoferrin (LF) content in infant and milk formulas. Leghorn laying hens were immunized with purified bovine and human LFs to produce anti-bovine LF and anti-human LF IgY antibody in the egg yolk. After 5-8 weeks of the immunization, anti-LF IgY was extracted and analyzed by ELISA. Specific IgY antibodies against LFs cross reacted with human and bovine LFs, examined by ELISA and western-blot assay. Such cross-reactivity suggested the presence of common antigenic determinants between human and bovine LFs. An indirect competitive ELISA was preferred to quantify LF in milk and infant formulas, since the range of detection is 3.125-50 μg/mL, which is broader compared to the biotinylated ELISA system (5-50 ng/mL). As per the indirect competitive ELISA system, IgY at a concentration of the 150 μg/mL was incubated with various concentrations of bovine LF ranged from 0.003-100 μg/mL. The immunoassay was used to estimate the total bovine LF content in the milk samples and infant formulas, ranging from 49.28-80.96 μg/mL and 29.92-60.00 μg/mL, respectively.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".