ClC-3 chloride channel functions as a mechanically sensitive channel in osteoblasts
Bibliographic record
Abstract
Mechanical stimulation usually causes the volume changes of osteoblasts. Whether these volume changes could be sensed by the ClC-3 chloride channel, a volume-sensitive ion channel, and further promote the osteodifferentiation in osteoblasts has not been determined. In this study, we applied persistent static compression on MC3T3-E1 cells to detect the expression changes of ClC-3, osteogenic markers, as well as some molecules related with signaling transduction pathway. We tested the key role of ClC-3 in transferring the mechanical signal to osteoinduction by ClC-3 overexpressing and siRNA technique. We found that ClC-3 level was up-regulated by mechanical stimulation in MC3T3-E1 cells. Mechanical force also up-regulated the mRNA level of osteogenic markers such as alkaline phosphatase (Alp), bone sialoprotein (Bsp), and osteocalcin (Oc), which could be blocked or strengthened by Clcn3 siRNA or overexpressing, and Alp expression was more sensitive to the changes of ClC-3 level. We also found that runt-related transcription factor 2 (Runx2), transforming growth factor beta 1 (TGF-β1), and Wnt pathway might be involved in ClC-3 mediated mechanical transduction in osteoblasts. The data from the current study suggest that the ClC-3 chloride channel acts as a mechanically sensitive channel to regulate osteodifferentiation in osteoblasts.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".