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Record W2520184529 · doi:10.1039/c6fo00706f

Prospects of enhancing dietary zinc bioavailability with food-derived zinc-chelating peptides

2016· review· en· W2520184529 on OpenAlexafffund
M. Chinonye Udechukwu, Stephanie A. Collins, Chibuike C. Udenigwe

Bibliographic record

VenueFood & Function · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of OttawaDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioavailabilityZincChelationFood scienceChemistryPharmacologyMedicineOrganic chemistry

Abstract

fetched live from OpenAlex

Zinc is an essential micronutrient that strongly influences human health and nutrition through its involvement in several biological processes. Zinc functions as structural and functional component of many transcription factors and enzymes that regulate cell growth, gene expression, and immune response, and its deficiency can lead to retarded growth and impaired immune functions. The physiological functions of zinc are dependent on its bioavailability in tissues, which in turn depends on intestinal absorption of dietary zinc. The presence of dietary fibre and phytates impedes intestinal zinc absorption, as they can form insoluble complexes with zinc, decreasing its bioavailability. Peptides derived from food proteins can enhance zinc absorption and bioavailability. Peptides that contain amino acid residues such as cysteine, histidine, serine, aspartate and glutamate can chelate divalent metals, including zinc, forming soluble metal coordinate complexes. The structure-function relationship of zinc-chelating peptides and the stability of the peptide-metal complexes to gastrointestinal digestion are critical to their relevance in human nutrition and health promotion.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations128
Published2016
Admission routes2
Has abstractyes

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