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An investigation of extracellular vesicles in bovine colostrum, first milk and milk over the lactation curve

2022· article· en· W4294957639 on OpenAlexaff
Jessie Santoro, Anindya Mukhopadhya, Charlotte Oliver, André Brodkorb, Linda Giblin, Lorraine O’Driscoll

Bibliographic record

VenueFood Chemistry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsTrinity College
FundersTeagascScience Foundation IrelandDepartment of Agriculture, Food and the Marine, Ireland
KeywordsColostrumLactationNanoparticle tracking analysisExtracellular vesiclesBovine milkFlow cytometryChemistryFood scienceBiologyAndrologyMicrovesiclesMolecular biologyImmunologyMedicineBiochemistryCell biologyAntibodyPregnancy

Abstract

fetched live from OpenAlex

Extracellular vesicles (EVs) in milk have claimed benefits ranging from conveying immunological privilege to infants to being suitable as natural delivery vehicles for therapeutic drugs. However, a longitudinal study of bovine EVs quantities and characteristics in colostrum (COL), first milk (FM) and throughout the lactation curve of mature milk (MM) had never been performed and so was our aim. COL, FM and 9 months of MM samples were collected. Caseins -overlapping size with EVs- were removed. EVs were collected by density gradient ultracentrifugation and characterised by SDS-PAGE, Bradford assay, nanoparticle tracking analysis, immunoblotting, imaging flow cytometry analysis, and transmission electron microscopy. COL and FM had substantially more EVs than MM, with COL enriched in small EVs. No significant differences were observed between months 1-9 of MM. Altogether, although COL and FM are particularly rich sources of EVs, mature milk throughout the lactation curve is also an abundant source of intact EVs.

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 score0.018
Threshold uncertainty score0.535

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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

Citations44
Published2022
Admission routes1
Has abstractyes

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