Transmission of mechanical information by purinergic signaling
Bibliographic record
Abstract
1 Abstract The human skeleton constantly interacts and adapts to the physical world. We have previously reported that physiologically-relevant mechanical forces lead to small, repairable membrane injuries in bone-forming osteoblasts, resulting in the release of ATP and stimulation of purinergic (P2) calcium responses in neighbouring cells. The goal of this study was to develop a theoretical model describing injury-related ATP and ADP release, extracellular diffusion and degradation, and purinergic responses in neighboring cells. The model was validated using experimental data obtained by measuring intracellular free calcium ([Ca 2+ ] i ) elevations following mechanical stimulation of a single osteoblast. The validated single-cell injury model was then scaled to a tissue-level injury to investigate how purinergic responses communicate information about injuries with varying geometries. We found that total ATP released, peak extracellular ATP concentration and the ADP-mediated signaling component contributed complementary information regarding the mechanical stimulation event. The total amount of ATP released governed the maximal distance from the injury at which purinergic responses were stimulated, as well as the overall number of responders. The peak ATP concentration reflected the severity of an individual cell injury and determined signal propagation velocity and temporal synchrony of responses. Peak ATP concentrations also discriminated between minor and severe injuries that led to the release of similar total amounts of ATP due to differences in injury repair dynamics. The third component was ADP-mediated signaling which became relevant only in larger tissue-level injuries, and it conveyed information about the distance to the injury site and its geometry. Taken together, this study identified specific features of extracellular ATP/ADP spatiotemporal signals that encode the severity of the mechanical stimulus, the distance from the stimulus, as well as the mechano-resilient status of the tissue.
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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 | 0.001 | 0.001 |
| 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".