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Record W4411929105 · doi:10.1021/acs.jafc.4c13286

Enhanced Bioavailability of Ferric Pyrophosphate Delivery System Constructed with β-Glucan and Casein Phosphopeptide

2025· article· en· W4411929105 on OpenAlexaff
Yiqiao Pei, Ye Zhang, Fang Tian, Yanrong Zhao, Xuguang Zhang, Steve W. Cui, Huali Wang, Jianbo Zhang, Hao Wang

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsAgriculture and Agri-Food Canada
FundersTianjin University of Science and Technology
KeywordsBioavailabilityPolydextroseChemistryPyrophosphatePhosphopeptideFood scienceCaseinSolubilityFortificationChromatographyBiochemistryPharmacologyMedicine

Abstract

fetched live from OpenAlex

Iron supplementation has been an important and complicated health topic. Available iron preparations generally have issues such as instability, inadequate absorption, and low consumer acceptability. This study provided a novel ferric pyrophosphate (FePP) delivery system, the FePP-casein-phosphopeptide-dextran (FCD) complex, with sodium pyrophosphate (NaPP) as a solvent promoter and casein-phosphopeptide (CPP) and β-glucan as wall materials. Compared to FePP, FCD showed better properties, including good water solubility (95% resolubilization), color stability (pH 3.5–6.5), compatibility with sensitive nutrients like vitamin C (VC), and lipid oxidation stability (delayed by approximately 50%). In vitro, FCD demonstrated gradual iron release in the gut with a release rate exceeding 80%. In vivo, FCD effectively alleviated iron deficiency anemia (IDA) symptoms in mice, with superior overall efficacy to FePP. Therefore, this novel FCD iron delivery system had considerable potential to be applied in nutritional food systems or iron supplements.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.003
GPT teacher head0.187
Teacher spread0.183 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food Chemistry→Same topicIron Metabolism and Disorders→French-language works237,207→