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Oral dosing of vitamins K1 and K2 in ovariectomized rats: Effects on bone loss and serum/bone levels

2010· article· en· W2296212865 on OpenAlexaff
Judith Moreines, Xueyan Fu, Sarah L. Booth

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsWomen's Health Research Institute
FundersWyeth
KeywordsOvariectomized ratInternal medicineEndocrinologyOsteocalcinBone mineralChemistryBone densityOsteopeniaVitaminVitamin K2OsteoporosisMedicineEstrogenAlkaline phosphataseBiochemistry

Abstract

fetched live from OpenAlex

Vitamin K (VK) exists in multiple forms that share common biochemical structures to support VK‐dependent protein carboxylation. Vitamin K may benefit bone by carboxylating the bone protein, osteocalcin. The unique side chains of the menaquinones (MKn) may further enhance K bone benefits. Despite plausible biological mechanisms, a causal role of vitamin K in bone and differential abilities of MKn and VK1 to benefit bone remains unclear. To assess this, ovariectomized (OVX) rats were randomized to 6 dosing groups of 16/group [Sham OVX (SOVX); OVX; OVX + Bisphosphonate (BP) (100ìg/kg/100 ìg/mL saline sc); OVX + VK1; OVX + MK4; and OVX + MK7] for 12 weeks. Equimolar doses of 107 mg K1/kg, 147 mg MK4/kg, and 201 mg/Kg MK7 were added to vitamin K deficient diets daily. As expected, OVX significantly increased weight and decreased bone strength vs. SOVX (p<0.05). OVX failed to significantly alter serum or bone vitamin K concentrations vs. SOVX. BP significantly increased bone strength and bone mineral density (BMD) vs. OVX (p<0.05). However, VK failed to prevent bone loss. VK1 supplementation significantly increased serum/bone concentrations of VK 1 vs. the concentrations of MK 4 or MK 7 following their supplementation (p<0.05). Unexpectedly, an MK7 epoxide was found in serum, not bone. Further studies evaluating the safety of MK 7 and potential efficacy of other doses of K vitamers are warranted. (Funding: Wyeth)

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)0.002
metaresearch head score (Gemma)0.001
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 score0.304
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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
Published2010
Admission routes1
Has abstractyes

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