Novel <i>in vitro</i> microfluidic platform for osteocyte mechanotransduction studies
Bibliographic record
Abstract
Osteocytes are the major mechanosensing cells in bone remodeling. Current in vitro bone mechanotransduction research use macroscale devices such as flow chambers; however, in vitro microfluidic devices provide an optimal tool to better understand this biological process with its flexible design, physiologically relevant dimensions and high-throughput capabilities. This project aims to design and fabricate a multi-shear stress, co-culture platform to study the interaction between osteocytes and other bone cells under varying flow conditions. Standard microfluidic design utilizing changing geometric parameters is used to induce different flow rates that are directly proportional to the levels of shear stress, with devices fabricated from standard polydimethylsiloxane (PDMS)-based softlithography processes. Each osteocyte channel (OCY) is connected to an adjacent osteoclast channel (OC) by 20-μm perfusion channels for cellular signaling molecule transport. Significant differences in RANKL levels are observed between channels with different shear stress levels, and we observed that pre-osteoclast differentiation was directly affected by adjacent flow-stimulated osteocytes. Significant decrease in the number of differentiating osteoclasts is observed in the OC channel adjacent to the 2-Pa shear stress OCY channel, while differentiation adjacent to the 0.5-Pa shear stress OCY channel is unaffected compared with no-flow controls. Addition of zoledronic acid showed a significant decrease in osteoclast differentiation, compounding to effect instigated by increasing fluid shear stress. Using this platform, we are able to mimic the interaction between osteocytes and osteoclasts in vitro under physiologically relevant bone interstitial fluid flow shear stress. Our novel microfluidic co-culture platform provides an optimal tool for bone cell mechanistic studies and provides a platform for the discovery of potential drug targets for clinical treatments of bone-related 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) | 0.001 | 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".