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Yoghurt fermentation alters the composition and antiplatelet properties of milk polar lipids

2020· article· en· W3038100976 on OpenAlexafffund
Ronan Lordan, Natalia P. Vidal, Thu Huong Pham, Αλέξανδρος Τσούπρας, Raymond Thomas, Ioannis Zabetakis

Bibliographic record

VenueFood Chemistry · 2020
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsMemorial University of Newfoundland
FundersDirectorate for Biological SciencesMemorial University of NewfoundlandUniversity of LimerickEnterprise Ireland
KeywordsChemistryPhosphatidylethanolamineFermentationFood sciencePhosphatidylcholineSphingomyelinTributyrinLipidomicsMolecular massAntithromboticBiochemistryComposition (language)LipasePhospholipidEnzymeCholesterol

Abstract

fetched live from OpenAlex

Dairy polar lipids (PL) seem to exhibit antiplatelet effects. However, it is not known what molecular species may be responsible. In this study, we confirmed using C30 reversed-phase (C30RP) ultra-high-performance liquid chromatography (UHPLC) coupled to high resolution accurate mass tandem mass spectrometry (HRAM-MS/MS) that fermentation of yoghurts from ovine milk using specific starter cultures altered the PL composition. These lipid alterations occurred concomitant with increased antithrombotic properties of the yoghurts PL fractions against platelet-activating factor (PAF) and thrombin-induced platelet aggregation. Specifically, elevation in phosphatidylethanolamine (PE), sphingomyelin (SM), phosphatidylcholine (PC) and their molecular species were observed following yoghurt fermentation. Furthermore, PC(18:0/18:1), PE(18:1/18:2), SM(d18:0/22:0) and several other molecular species were significantly inversely correlated with the inhibition of PAF and thrombin. These molecular species were abundant in the most bioactive yoghurts fermented by S. thermophilus and L. acidophilus, which suggest that fermentation by these microorganisms increases the antithrombotic properties of ovine milk PL.

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 score0.012
Threshold uncertainty score0.194

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 science0.0000.000
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 head0.033
GPT teacher head0.266
Teacher spread0.232 · 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

Citations46
Published2020
Admission routes2
Has abstractyes

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