Stochastic resonance is a method to improve the biosynthetic response of chondrocytes to mechanical stimulation
Bibliographic record
Abstract
Cellular mechanosensitivity is an important factor during the mechanical stimulation of tissue engineered cartilage. While the application of mechanical stimuli improves tissue growth and properties, chondrocytes also rapidly desensitize under prolonged loading thereby limiting its effectiveness. One potential method to mitigate load-induced desensitization is by superimposing noise on the loading waveforms ("stochastic resonance"). Thus, the purpose of this study was to investigate the effects of stochastic resonance on chondrocyte matrix metabolism. Chondrocyte-seeded agarose gels were subjected to dynamic compressive loading, with or without, superimposed vibrations of different amplitudes and frequency bandwidths. Changes in matrix biosynthesis were determined by radioisotope incorporation and subsequent effects on intracellular calcium signaling were evaluated by confocal microscopy. Although dependent on the duration of loading, superimposed vibrations improved cellular sensitivity to mechanical loading by further increasing matrix synthesis between 20-60%. Stochastic resonance also appeared to limit load-induced desensitization by maintaining sensitivity under desensitized loading conditions. While superimposed vibrations had little effect on the magnitude of intracellular calcium signaling, recovery of mechanosensitivity after stimulation was achieved at a faster rate suggesting that less time may be required between successive loading applications. Thus, stochastic resonance appears to be a valuable tool during the mechanical stimulation of cartilage constructs, even when suboptimal stimulation conditions are used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| 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 teacher head, 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".