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Record W3049168681 · doi:10.1139/cjpp-2019-0632

Blockade of IL-6 alleviates bone loss induced by modeled microgravity in mice

2020· article· en· W3049168681 on OpenAlexvenueno aff
Baorong He, Xinhua Yin, Dingjun Hao, Bin Zhang, Zhen Zhang, Ke Zhang, Xiaobin Yang

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2020
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsnot available
Fundersnot available
KeywordsEndocrinologyInternal medicineOsteopontinOsteoprotegerinDeoxypyridinolineChemistryOsteocalcinAcid phosphataseRANKLAlkaline phosphataseDownregulation and upregulationBone resorptionCathepsin KOsteoclastReceptorActivator (genetics)MedicineBiochemistry

Abstract

fetched live from OpenAlex

This study investigated the effects of blockade of IL-6 on bone loss induced by modeled microgravity (MG). Adult male mice were exposed to hind-limb suspension (HLS) and treated with IL-6-neutralizing antibody (IL-6 nAb) for 4 weeks. HLS in mice led to upregulation of IL-6 expression in both sera and femurs. IL-6 nAb treatment in HLS mice significantly alleviated bone loss, evidenced by increased bone mineral density of whole tibia, trabecular thickness and number, bone volume fraction of proximal tibiae, and ultimate load and stiffness of femoral diaphysis. IL-6 nAb treatment in HLS mice significantly enhanced levels of osteocalcin in sera and reduced levels of deoxypyridinoline. In MC3T3-E1 cells exposed to MG in vitro, IL-6 nAb treatment increased mRNA expression and activity of alkaline phosphatase, mRNA expression of osteopontin and runt-related transcription factor 2, and protein levels of osteoprotegerin and decreased protein levels of receptor activator of the NF-κB ligand. In RAW254.7 cells exposed to MG, IL-6 nAb treatment downregulated mRNA expression of cathepsin K and tartrate-resistant acid phosphatase (TRAP) and reduced numbers of TRAP-positive multinucleated osteoclasts. In conclusion, blockade of IL-6 alleviated the bone loss induced by MG.

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)0.000
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 score0.015
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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