Microfluidic platform for studying osteocyte mechanoregulation of breast cancer bone metastasis
Bibliographic record
Abstract
Bone metastasis is a common, yet serious, complication of breast cancer. Breast cancer cells that extravasate from blood vessels to the bone devastate bone quality by interacting with bone cells and disrupting the bone remodeling balance. Although exercise is often suggested as a cancer intervention strategy and mechanical loading during exercise is known to regulate bone remodeling, its role in preventing bone metastasis remains unknown. We developed a novel in vitro microfluidic tissue model to investigate the role of osteocytes in the mechanical regulation of breast cancer bone metastasis. Metastatic MDA-MB-231 breast cancer cells were cultured inside a 3D microfluidic lumen lined with human umbilical vein endothelial cells (HUVECs), which is adjacent to a channel seeded with osteocyte-like MLO-Y4 cells. Physiologically relevant oscillatory fluid flow (OFF) (1 Pa, 1 Hz) was applied to mechanically stimulate the osteocytes. Hydrogel-filled side channels in-between the two channels allowed real-time, bi-directional cellular signaling and cancer cell extravasation over 3 days. The applied OFF was capable of inducing intracellular calcium responses in osteocytes (82.3% cells responding with a 3.71 fold increase average magnitude). Both extravasation distance and percentage of extravasated side-channels were significantly reduced with mechanically stimulated osteocytes (32.4% and 53.5% of control, respectively) compared to static osteocytes (102.1% and 107.3% of control, respectively). This is the first microfluidic device that has successfully integrated stimulatory bone fluid flow, and demonstrated that mechanically stimulated osteocytes reduced breast cancer extravasation. Future work with this platform will determine the specific mechanisms involved in osteocyte mechanoregulation of breast cancer bone metastasis, as well as other types of cancer metastasis and diseases.
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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 | 0.000 | 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 | 0.000 |
| 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".