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Record W4409677781 · doi:10.1111/1750-3841.70210

Canola Protein Hydrolysates Show Osteogenic Activity in MC3T3‐E1 Cells

2025· article· en· W4409677781 on OpenAlexafffund
Ilekuttige Priyan Shanura Fernando, Amir Vahedifar, Supratim Ghosh, Jianping Wu

Bibliographic record

VenueJournal of Food Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersGovernment of Canada
KeywordsRANKLOsteoprotegerinOsteoblastChemistryOsteoporosisRUNX2HydrolysateCytotoxicityActivator (genetics)Internal medicineIn vitroReceptorBiochemistryMedicine

Abstract

fetched live from OpenAlex

Osteoporosis, the most prevalent metabolic bone disorder, is a major public health issue. Previous studies indicated the potential of food components in mitigating the risks of osteoporosis. The study aimed to evaluate the potential of canola protein hydrolysates (CPH) on osteoclastogenesis using a pre-osteoblast cell MC3T3-E1. Twenty-two CPHs were prepared by 12 different proteases, either individually or in combination. Three CPHs, prepared by trypsin (CPH-T), Protex 6L (CPH-P), and the combination of Protex 6L and thermoase (CPH-PT) showed promising activity in promoting in vitro bone formation. CPH-T and CPH-PT improved cell proliferation at a concentration of 10 ug/ml, while all three hydrolysates exhibited cytotoxicity at 1000 ug/ml. All three hydrolysates promoted the level of runt-related transcription factor 2 (RUNX2) and type I collagen, and mineralization in osteoblast cells, in a dose-dependent manner. Additionally, these three hydrolysates elevated the osteoprotegerin (OPG) level and reduced the level of receptor activator of nuclear factor kappa-B ligand (RANKL). This study indicated the activity of CPHs in the promotion of bone formation and prevention of osteoclastogenesis, suggesting the potential of CPHs as a promising functional food ingredient against osteoporosis.

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)0.001
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 score0.011
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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