Differential Effects of Natriuretic Peptide Stimulation on Tissue-Engineered Cartilage
Bibliographic record
Abstract
Tissue engineering is a promising approach for articular cartilage repair; however, it still has proven a challenge to produce substantial quantities of tissue from the limited number of cells that can be extracted from a single individual. Although several approaches have been investigated to enhance the production of cartilaginous tissue in vitro, relatively few techniques exist to reliably increase the population of cells needed for this approach. Alternatively, a single modulator of chondrocyte function, such as the C-type natriuretic peptide (CNP), may serve to address both of these issues. CNP is expressed in the growth plate and regulates cartilage growth through chondrocyte proliferation and differentiation. Thus, the purpose of this study was to determine the effects of CNP stimulation on tissue-engineered cartilage. Isolated bovine articular chondrocytes were seeded on Millicell filters and cultured in the presence of CNP (10 pM to 10 nM) for 4 weeks. Stimulation with CNP resulted in differential effects depending on the dose of the peptide. Low doses of CNP (10 to 100 pM) elicited chondrocyte proliferation with a maximal response observed at 100 pM (43% increase in cellularity). However, high doses of CNP (10 nM) stimulated matrix deposition (36% and 137% increase in proteoglycans and collagen) without an associated change in tissue cellularity. CNP stimulation also downregulated the expression of type X collagen, an early hypertrophic marker associated with endochondral ossification. Thus, by regulating the dose of CNP, it may be possible to produce engineered tissue from the limited number of cells that can be reasonably extracted from a single individual for therapeutic purposes.
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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 | 0.001 |
| 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".