A novel and quick egg yolk immunoglobulin y antibody extraction method leveraging the protein liquid-liquid phase separation principle
Bibliographic record
Abstract
This study presents a novel and efficient method for extracting immunoglobulin Y (IgY) antibodies from egg yolk based on the principle of liquid-liquid phase separation (LLPS) induced by polyethylene glycol 8000 (PEG 8000). Initial delipidation of egg yolk samples with varying PEG 8000 concentrations demonstrated optimal delipidation efficiency and protein recovery at 2.5 % PEG 8000 concentration. Subsequent IgY extraction involved inducing LLPS by raising PEG 8000 concentration to 6.5 %, resulting in turbid solutions and the formation of globular droplet-like condensates observed under a microscope. Unlike the PEG 6000 method that induced aggregation, the method developed here using PEG 8000 does not lead to the appearance of aggregates of IgY. SDS-PAGE analysis confirmed that IgY extracted was no different from the conventional PEG 6000 method, with similar purity levels (77 % vs 79 %). Enzyme-linked immunosorbent assay and western blot analysis confirmed the antigen recognition properties of the isolated IgY. This method significantly reduces the amount of PEG used, leading to substantial cost savings compared to PEG 6000. The method can be completed within one hour. Despite a slightly lower IgY yield by the method, the time- and cost-saving advantages of this method make it a promising alternative for IgY extraction in research. This proposed IgY extraction technique utilizing protein LLPS has the potential to improve the study of the physicochemical properties of IgY and optimized production, while offering a quicker and cost-effective solution for various applications in biomedical research.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".