Mechanical Loading of Osteocytes via Oscillatory Fluid Flow Regulates Early‐Stage PC‐3 Prostate Cancer Metastasis to Bone
Bibliographic record
Abstract
Bone metastasis is a devastating complication for advanced-stage prostate cancer patients. Osteocytes, as the primary mechanosensors in bone, have been recently investigated for their role in prostate cancer bone metastasis. In vivo findings show potential benefits of exercise as a preventative intervention strategy for bone metastasis. In contrast, in vitro studies indicate direct prostate cancer-osteocyte interactions under mechanical loading promote prostate cancer growth and migration. These findings are not consistent with in vivo results and may be more reflective of late-stage metastatic colonization. Here, the role of flow-stimulated osteocytes during early-stage bone metastasis, particularly prostate cancer-endothelial interactions, is examined. Flow-stimulated osteocytes reduce PC-3 prostate cancer cell adhesion and trans-endothelial migration by 32.3% and 40% compared to static controls. Both MLO-Y4 and primary murine osteocytes under mechanical loading regulate the extravasation distance and frequency of PC-3 cells in a microfluidic tissue model. Application of vascular cellular adhesion molecule 1 (VCAM-1) neutralizing antibody abolishes the difference in cancer cell adhesion, extravasation frequency, and number of extravasated PC-3 cells between static and flow-stimulated groups. Taken together, the role of osteocytes in early-stage bone metastasis using PC-3 cells as a model is demonstrated here, bridging the gap between in vitro and in vivo findings.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".