The Role of Gap Junctions and Mechanical Loading on Mineral Formation in a Collagen-I Scaffold Seeded with Osteoprogenitor Cells
Bibliographic record
Abstract
Fracture nonunions represent one of many large bone defects where current treatment strategies fall short in restoring both form and function of the injured tissue. In this case, the use of a tissue-engineered scaffold for promoting bone healing offers an accessible and easy-to-manipulate environment for studying bone formation processes in vitro. We have previously shown that mechanical prestimulation using confined compression of differentiating osteoblasts results in an increase in mineralization formed in a 3D collagen-I scaffold. This study builds on this knowledge by evaluating the short and long-term effects of blocking gap junction-mediated intercellular communication among osteogenic cells on their effectiveness to mineralize collagen-I scaffolds in vitro, and in the presence and absence of mechanical stimulation. In this study, confined compression was applied in conjunction with octanol (a general communication blocker) or 18-α-glycerrhetinic acid (AGA, a specific gap junction blocker) using a modified FlexCell plate to collagen-I scaffolds seeded with murine embryonic stem cells stimulated toward osteoblast differentiation using beta-glycerol phosphate. The activity, presence, and expression of osteoblast cadherin, connexin-43, as well as various pluripotent and osteogenic markers were examined at 5-30 days of differentiation. Fluorescence recovery after photobleaching, immunofluorescence, viability, histology assessments, and reverse-transcriptase polymerase chain reaction assessments revealed that inhibiting communication in this scaffold altered the lineage and function of differentiating osteoblasts. In particular, treatment with communication inhibitors caused reduced mineralization in the matrix, and dissociation between connexin-43 and integrin α5β1. This dissociation was not restored even after long-term recovery. Thus, in order for this scaffold to be considered as an alternative strategy for the repair of large bone defects, cell-cell contacts and cell-matrix interactions must remain intact for osteoblast differentiation and function to be preserved. This study shows that within this 3D scaffold, gap junctions are essential in osteoblast response to mechanical loading, and are essential structures in producing a significant amount and organization of mineralization in the matrix.
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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) | 0.000 | 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".