Hypoxia signalling manipulation for bone regeneration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".