Hydromechanical stimulator for chondrocyte-seeded constructs in articular cartilage tissue engineering applications
Bibliographic record
Abstract
Mechanical stimulation is a key technique used for controlling the mechanical properties of tissue engineered articular cartilage constructs proposed for defect repair. The present study introduces a new technical method and device for 'hydromechanical' stimulation of tissue engineered articular cartilage constructs. The stimulation consists of simultaneous cyclic compression, frictional shear from a sliding indenter contact and direct pressurized fluid perfusion. Each of these modes of mechanical loading has been shown by other research groups to effectively stimulate tissue engineered constructs. A device for applying these conditions was designed, developed and tested. Two sets (high and low perfusion flow rates) of three experiments were performed, each with two samples subjected to hydromechanical stimulation conditions (compression and friction forces along with perfusion). Two other samples from each set were subjected to just compression and dynamic frictional shear forces, and two more were used as controls (not stimulated). The average amount of glycosaminoglycan retained in the constructs after 3 weeks ranked from low to high as follows: controls, hydromechanical conditions with the low-flow rate, hydromechanical conditions with the high-flow rate and just compression plus dynamic frictional shear. Statistically significant differences were not detected. However, future studies would focus on glycosaminoglycan production in the superficial zone, measuring the glycosaminoglycan released to the nutrient media, and address altering the hydromechanical stimulation parameters using the results of the present study as guidance, in attempts to achieve statistically significant increases in glycosaminoglycan production compared with the controls.
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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) | 0.002 | 0.001 |
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".