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Record W1981629638 · doi:10.1159/000224627

Zinc Deficiency Reduces Bone Mineral Density in the Spine of Young Adult Rats: A Pilot Study

2009· article· en· W1981629638 on OpenAlexafffund
Natasha R. Ryz, Hope A. Weiler, Carla G. Taylor

Bibliographic record

VenueAnnals of Nutrition and Metabolism · 2009
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsMcGill UniversityUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsFemurBone mineralZincOsteocalcinInternal medicineEndocrinologyZinc deficiency (plant disorder)DensitometryTibiaBone densityBone remodelingMedicineChemistryOsteoporosisAlkaline phosphataseAnatomySurgeryBiochemistry

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the effects of zinc deficiency initiated during adolescence on skeletal densitometry, serum markers of bone metabolism, femur minerals and morphometry in young adult rats. Ten-week-old male rats were fed a <1-mg Zn/kg diet (9ZD), a 5-mg Zn/kg diet (9MZD) or a 30-mg Zn/kg diet (9CTL) for up to 9 weeks. Analyses included bone mineral density, serum osteocalcin and C-terminal peptides of type I collagen, serum zinc, femur zinc, calcium and phosphorus, and femur morphometry. Bone mineral density was 14% lower in the spine of 9ZD, but was not altered in the whole body, tibia or femur, or in any of the aforementioned sites in 9MZD, compared to 9CTL. When adjusted for size, spine bone mineral apparent density was still 8% lower in 9ZD than 9CTL. Serum osteocalcin, a marker for bone formation, was approximately 33% lower in 9ZD compared to both 9MZD and 9CTL. The 9ZD and 9MZD had 57% lower femur zinc and 56-88% lower serum zinc concentrations compared to 9CTL. These findings indicate that severe zinc deficiency initiated during adolescence may have important implications for future bone health, especially with regards to bone consolidation in the spine.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.383

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 head0.064
GPT teacher head0.359
Teacher spread0.295 · 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 designObservational
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

Citations24
Published2009
Admission routes2
Has abstractyes

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