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Record W2083500461 · doi:10.1017/erm.2015.4

Hypoxia signalling manipulation for bone regeneration

2015· review· en· W2083500461 on OpenAlexaff
Justin Drager, Edward J. Harvey, Jake E. Barralet

Bibliographic record

VenueExpert Reviews in Molecular Medicine · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAngiogenesisVascularityBone healingCell biologyTranscription factorHypoxia-inducible factorsHypoxia (environmental)Regeneration (biology)Signal transductionIn vivoBiologyChemistryCancer researchMedicinePathologyBiotechnologyAnatomyBiochemistryGeneOxygen

Abstract

fetched live from OpenAlex

Hypoxia-inducible factor (HIF) signalling is intricately involved in coupling angiogenesis and osteogenesis during bone development and repair. Activation of HIFs in response to a hypoxic bone micro-environment stimulates the transcription of multiple genes with effects on angiogenesis, precursor cell recruitment and differentiation. Substantial progress has been made in our understanding of the molecular mechanisms by which oxygen content regulates the levels and activity of HIFs. In particular, the discovery of the role of oxygen-dependent hydroxylase enzymes in modulating the activity of HIF-1α has sparked interest in potentially promising therapeutic strategies in multiple clinical fields and most recently bone healing. Several small molecules, termed hypoxia mimics, have been identified as activators of the HIF pathway and have demonstrated augmentation of both bone vascularity and bone regeneration in vivo. In this review we discuss key elements of the hypoxic signalling pathway and its role in bone regeneration. Current strategies for the manipulation of this pathway for enhancing bone repair are presented with an emphasis on recent pre-clinical in vivo investigations. These findings suggest promising approaches for the development of therapies to improve bone repair and tissue engineering strategies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.078
GPT teacher head0.377
Teacher spread0.299 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2015
Admission routes1
Has abstractyes

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