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Record W4379467856 · doi:10.1016/j.psj.2023.102843

Proteomic investigation and understanding on IgY purification and product development

2023· article· en· W4379467856 on OpenAlexaff
Xiaoying Zhang, Rao Wu, Brindha Chelliappan

Bibliographic record

VenuePoultry Science · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistryChromatographyYolkBiochemistryPhosvitinNucleic acidAffinity chromatographyGel electrophoresisGlobulinEnzymeBiologyFood science

Abstract

fetched live from OpenAlex

An increasing demand for the development of immunoglobin Y (IgY) illustrates the necessity of the component analysis in the process of conduction and quality control. This study investigated the proteomic changes in crude IgY extracts and purified IgY products obtained by sequential polyethylene glycol precipitation (PEG) of egg yolks followed by human mycoplasma protein-based affinity chromatography compared with intact egg yolks. After confirming the extraction efficiency and purity by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis, liquid chromatography tandem-mass spectrometry (LC-MS/MS) was performed with samples including fresh yolk, IgY extracted product and purified product. A total of 348 proteins were identified, with 36 proteins deleted and 209 newly detected proteins in the purified product compared to the intact egg yolk. The significantly decreased proteins mainly included phosvitin, albumin, and apolipoprotein B whereas the significantly increased proteins were mainly IgY-related proteins. GO analysis showed that the purified IgY product had ATPase activity and purine ribonucleoside triphosphate binding activity, and was mainly involved in purine and nucleic acid metabolism. This study will inevitably fasten the commercial application of IgY antibodies and is of greater significance for promotion, development and approval for new antibody derived drug products.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.219
GPT teacher head0.360
Teacher spread0.141 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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