MétaCan
Menu
Back to cohort
Record W2019397900 · doi:10.1089/jmf.2006.9.436

Detection of Isoflavones in Mouse Tibia After Feeding Daidzein

2006· article· en· W2019397900 on OpenAlexafffund
Debbie Fonseca, Wendy E. Ward

Bibliographic record

VenueJournal of Medicinal Food · 2006
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDaidzeinEquolOvariectomized ratIsoflavonesEndocrinologyInternal medicineIn vivoGenisteinChemistryMedicineEstrogenBiology

Abstract

fetched live from OpenAlex

Many studies suggest that diets rich in isoflavones protect against bone loss or slow the loss of bone mass that occurs because of estrogen withdrawal. Although in vitro studies have reported effects of isoflavones on bone cells, the presence of daidzein and/or equol in bone tissue in vivo has not been reported. The objective of this study was to determine if daidzein and equol were present in bone tissue (tibias) after feeding mice a diet containing purified daidzein. Sham mice (n = 9) received control diet, and ovariectomized mice were randomized to control diet (Ovx) (n = 9) or control diet containing 200 mg of daidzein/kg of diet (n = 8) for 12 weeks. At necropsy, tibias and serum were collected. Mice in the Daidzein group had significantly higher (P < .05) levels of both daidzein and equol in tibias than Sham and Ovx mice. Tibia levels of daidzein and equol were approximately five and four times higher, respectively, than the Sham and Ovx groups. Similarly, mice fed daidzein also had significantly higher (P < .05) serum daidzein and equol than the Sham and Ovx mice. In conclusion, feeding a level of daidzein that is attainable by dietary intervention alone results in a high level of both daidzein and equol in tibias. These findings suggest that daidzein and its metabolite, equol, have the potential to act directly on bone cells in vivo.

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 score0.325
Threshold uncertainty score0.250

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 head0.013
GPT teacher head0.281
Teacher spread0.269 · 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

Citations3
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medicinal FoodSame topicPhytoestrogen effects and researchFrench-language works237,207