Mechanisms of osteoblastic MG‐63 cell contraction and mRNA expression in stress‐relaxed collagen gels
Bibliographic record
Abstract
Culture of osteoblast‐like MG‐63 cells within collagen gels results in the generation of intrinsic stress. Release of the gels from attachment results in contraction and significantly enhanced MMP‐1, MMP‐3, and α2 integrin mRNA levels. To understand the role of cytoskeletal elements and signaling pathways involved in contraction and gene expression, MG‐63 cells were cultured in collagen gels for 24 hours, released, and then immediately treated with cytoskeletal depolymerization agents or kinase inhibitors. Contraction was measured, RNA isolated, and real‐time PCR analysis was performed. The results showed that: (1) cytochalasin D treatment (microfilaments) inhibited contraction and depressed MMP‐1, MMP‐3, and α2 mRNA levels; (2) Nocodazole treatment (microtubules) enhanced early contraction and elevated mRNA levels for MMP‐3; (3) ROCK inhibitor treatment (Y27632) inhibited contraction and depressed MMP‐3 and α2 integrin mRNA levels; (4) the ERK1/2 inhibitor, U0126, did not affect contraction, but depressed MMP‐1, MMP‐3, and α2 mRNA mRNA levels; and (5) treatment with the p38 MAP kinase inhibitor SB203580 was ineffective. These results suggest that collagen gel contraction and gene expression is dependent on the tensegrity balance between microfilaments and microtubules, and the ROCK pathway, while gene expression but not contraction is also dependent on the ERK1/2 MAP kinase pathway.
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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".