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Record W2344352746 · doi:10.3390/ijerph13050460

Supraphysiological Levels of Quercetin Glycosides are Required to Alter Mineralization in Saos2 Cells

2016· article· en· W2344352746 on OpenAlexafffund
Leslie A. Nash, Sandra J. Peters, Philip Sullivan, Wendy E. Ward

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHyperosideRutinQuercetinChemistryOsteoblastGlycosideOsteopontinAlkaline phosphataseBone mineralMineralization (soil science)PharmacologyFlavonoidInternal medicineEndocrinologyBiochemistryBiologyMedicineAntioxidantStereochemistryOsteoporosisEnzyme

Abstract

fetched live from OpenAlex

Flavonoid intake is positively correlated to bone mineral density (BMD) in women. Flavonoids such as quercetin exhibit strong anti-oxidant and anti-inflammatory activity that may be beneficial for bone health. Quercetin, previously shown to positively influence osteoblasts, is metabolized into glycosides including rutin and hyperoside. We compared the effects of these glycosides on mineralization in human osteoblast (Saos2) cells. Administration of rutin (≥25 µM) and hyperoside (≥5 µM) resulted in higher mineral content, determined using the alizarin red assay. This was accompanied by higher alkaline phosphatase activity with no cell toxicity. The expression of osteopontin, sclerostin, TNFα and IL6, known stimuli for decreasing osteoblast activity, were reduced with the addition of rutin or hyperoside. In summary, rutin and hyperoside require supraphysiological levels, when administered individually, to positively influence osteoblast activity. This information may be useful in developing nutraceuticals to support bone health.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.365
Teacher spread0.300 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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Same venueInternational Journal of Environmental Research and Public HealthSame topicBone Metabolism and DiseasesFrench-language works237,207