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Record W2417007149 · doi:10.1021/acs.jafc.6b01911

Identification and Evaluation of Cryoprotective Peptides from Chicken Collagen: Ice-Growth Inhibition Activity Compared to That of Type I Antifreeze Proteins in Sucrose Model Systems

2016· article· en· W2417007149 on OpenAlexafffund
Lihui Du, Mirko Betti

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Alberta
FundersAlberta Livestock and Meat AgencyUniversity of Alberta
KeywordsAntifreeze proteinIce crystalsAntifreezeChemistrySucrosePeptideBiochemistryAmino acidChromatography

Abstract

fetched live from OpenAlex

The ability of chicken collagen peptides to inhibit the growth of ice crystals was evaluated and compared to that of fish antifreeze proteins (AFPs). This ice inhibition activity was assessed using a polarized microscope by measuring ice crystal dimensions in a sucrose model system with and without collagen peptides after seven thermal cycles. The system was stabilized at -25 °C and cycled between -16 and -12 °C. Five candidate peptides with ice inhibition activity were identified using liquid chromatography and tandem mass spectrometry and were then synthesized. Their ice inhibition capacity was compared to that of type I AFPs in a 23% sucrose model system. Specific collagen peptides with certain amino acid sequences reduced the extent of ice growth by approximately 70% at a relatively low concentration (1 mg/mL). These results suggest that specific collagen peptides may act in a noncolligative manner, inhibiting ice crystal growth like type I AFPs, but less efficiently.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.138

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 head0.058
GPT teacher head0.255
Teacher spread0.197 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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