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Record W4223429727 · doi:10.3390/foods11071035

Label-Free LC-MS/MS Analysis Reveals Different Proteomic Profiles between Egg Yolks of Silky Fowl and Ordinary Chickens

2022· article· en· W4223429727 on OpenAlexaff
Rao Wu, Chen Chen, Xiaoying Zhang

Bibliographic record

VenueFoods · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Guelph
FundersNational Natural Science Foundation of China
KeywordsKEGGYolkFowlProteomeBiologyProteaseTranscriptomeGeneChemistryBiochemistryFood scienceGene expressionEnzyme

Abstract

fetched live from OpenAlex

The proteomic profiles of Silky fowl egg yolk (SFEY) and Leghorn egg yolk (LEY) were analyzed by bottom-up label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS). From a total of 186 identified proteins, 26 proteins were found significantly differentially abundant between two yolks, of which, 19 were up-regulated and 7 were down-regulated in SFEY, particularly, vitelline membrane outer layer protein 1, transthyretin and ovoinhibitor were up-regulated by 26, 25, and 16 times, respectively. In addition, there were 57 and 6 unique proteins in SFEY and LEY, respectively. Gene Ontology (GO) revealed SFEY contained relatively more abundant protease inhibitors and coagulation-related proteins. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed differentially abundant proteins in SFEY may be actively involved in the regulation of the neuroactive ligand-receptor interaction pathway. This study provides a theoretical basis for the understanding of proteomic and biological differences between these two yolks and can guide for further exploration of nutritional and biomedical use of Silky fowl egg.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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 designBench or experimental
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

Citations8
Published2022
Admission routes1
Has abstractyes

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