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Record W2296146383 · doi:10.1088/1748-6041/11/2/025004

Heparin-immobilized hydroxyapatite nanoparticles as a lactoferrin delivery system for improving osteogenic differentiation of adipose-derived stem cells

2016· article· en· W2296146383 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomedical Materials · 2016
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsWestern University
Fundersnot available
KeywordsLactoferrinOsteopontinAlkaline phosphataseOsteocalcinHeparinIn vitroNanoparticleChemistryAdipose tissueMaterials scienceBiochemistryNanotechnologyBiologyEnzymeImmunology

Abstract

fetched live from OpenAlex

The aim of this study is to fabricate lactoferrin (LF)-carrying hydroxyapatite nanoparticles (HAp NPs) to enhance osteogenic differentiation of rabbit adipose-derived stem cells (rADSCs). HAp NPs were modified with heparin-dopamine (Hep-DOPA) (Hep-HAp) and further immobilized with LF (LF/Hep-HAp). Heparin immobilization on HAp NPs prevented aggregation of HAp NPs in aqueous solution and prolonged the release of LF from LF/Hep-HAp NPs. In vitro studies of rADSCs have demonstrated that LF-Hep/HAp NPs significantly increase alkaline phosphatase (ALP) activity, calcium deposition, and both mRNA expression of osteocalcin (OCN) and osteopontin (OPN) in comparison with HAp and Hep-HAp NPs. These results suggest that LF/Hep-HAp NPs can effectively induce osteogenic differentiation of rADSCs.

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.

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 score0.006
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.253
Teacher spread0.236 · 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