Microfluidics approach to investigate the role of dynamic similitude in osteocyte mechanobiology
Bibliographic record
Abstract
Fluid flow is an important regulator of cell function and metabolism in many tissues. Fluid shear stresses have been used to level the mechanical stimuli applied in vitro with what occurs in vivo. However, these experiments often lack dynamic similarity, which is necessary to ensure the validity of the model. For interstitial fluid flow, the major requirement for dynamic similarity is the Reynolds number (Re), the ratio of inertial to viscous forces, is the same between the system and model. To study the necessity of dynamic similarity for cell mechanotransduction studies, we investigated the response of osteocyte-like MLO-Y4 cells to different Re flows at the same level of fluid shear stress. Osteocytes were chosen for this study as flows applied in vitro and in vivo have Re that are orders of magnitude different. We hypothesize that osteocytes' response to fluid flow is Re dependent. We observed that cells exposed to lower and higher Re flows developed rounded and triangular morphologies, respectively. Lower Re flows also reduced apoptosis rates compared to higher Re flows. Furthermore, MLO-Y4 cells exposed to higher Re flows had stronger calcium responses compared to lower Re flows. However, by also controlling for flow rate, the lower Re flows induced a stronger calcium response; while degradation of components of the osteocyte glycocalyx reversed this effect. This work suggests that osteocytes are highly sensitive to differences in Re, independent of just shear stresses, supporting the need for improved in vitro flow platforms that better recapitulate the physiological environment. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:663-671, 2018.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".