Methods for studying MLO-Y4 osteocytes in collagen-hydroxyapatite scaffolds in the rotary cell culture system
Bibliographic record
Abstract
Purpose: The rotary cell culture system (RCCS) is a common clinorotation device for cell culture. It is also used as a low-shear suspension culture bioreactor to form functionalized 3D tissue constructs and to model microgravity. We sought to develop a 3D scaffold composed of type I collagen and hydroxyapatite (collagen-HA) to characterize MLO-Y4 osteocytes following suspension culture or clinorotation.Materials and Methods: MLO-Y4 cells were embedded in collagen-HA. The scaffold was formed into droplets for suspension culture or wall-adhered to the RCCS for clinorotation. AFM, rheometry, immunofluorescence and qRT-PCR were employed to measure the scaffold stiffness, cell viability and gene expression of cells in collagen-HA scaffolds. Dendritic cells were visualized and quantified and gene expression after suspension culture and clinorotation was compared to static controls.Results: The optimized scaffold for the RCCS consisted of collagen with 6 mg/mL HA which had a stiffness of < 1 kPa. MLO-Y4 cell viability was higher in collagen-HA scaffolds, compared to scaffolds without HA. Collagen-HA scaffolds induced higher osteocyte-specific gene expression compared to cells cultured on 2D plastic. Cells in the scaffold downregulated DMP1, E11, IL-6, and RANKL, and had fewer dendritic cells following suspension culture whereas clinorotation downregulated DMP1 and E11 genes, compared to static controls.Conclusions: Suspension culture for 3 days in collagen-HA stimulates growth of osteocytes but may also desensitize them to mechanical cues. Clinorotation for 3 days in collagen-HA does not stimulate proliferation or expression of mechanosensitive genes, indicating that it may be an effective mechanical unloading environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".