Multiomic analysis of stretched osteocytes reveals processes and signalling linked to bone regeneration and cancer
Bibliographic record
Abstract
Exercise is a non-pharmacological intervention that can enhance bone regeneration and improve the management of bone conditions like osteoporosis or metastatic bone cancer. Therefore, it is gaining increasing importance in an emerging area of regenerative medicine-regenerative rehabilitation (RR). Osteocytes are mechanosensitive and secretory bone cells that orchestrate bone anabolism and hence postulated to be an attractive target of regenerative exercise interventions. However, the human osteocyte signalling pathways and processes evoked upon exercise remain to be fully identified. Making use of a computer-controlled bioreactor that mimics exercise and the latest omics approaches, RNA sequencing (RNA-seq) and tandem liquid chromatography-mass spectrometry (LC-MS), we mapped the transcriptome and secretome of mechanically stretched human osteocytic cells. We discovered that a single bout of cyclic stretch activated network processes and signalling pathways likely to modulate bone regeneration and cancer. Furthermore, a comparison between the transcriptome and secretome of stretched human and mouse osteocytic cells revealed dissimilar results, despite both species sharing evolutionarily conserved signalling pathways. These findings suggest that osteocytes can be targeted by exercise-driven RR protocols aiming to modulate bone regeneration or metastatic bone cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".