Collagen Deposition, Cellular and Molecular characterisation of self-assembled tendon constructs grown in vitro from embryonic chick tenocyte cells
Bibliographic record
Abstract
Tendons are fibrous connective tissues that connect and transmit forces between muscle and bone. Following injury, they are very difficult to repair surgically, often resulting in tissue that is not comparable to native tendons in structural integrity and mechanical strength and are therefore prone to re-injury. A tissue engineered approach that would provide tendon tissue regeneration would be enormously valuable. A step toward this goal would be establishment of an in vitro system that could replicate tendon development and maturation from progenitor cells, generating tissue for transplantation capable of integrating and repairing damaged tendons. Limited progress in this area has been made to date and no system can as yet support tendon tissue maturation. In this study, I have focused on the characterisation and optimisation of a protocol using tissue engineered constructs designed to mimic tendon development, monitoring cell self-assembly and collagen deposition. Primary embryonic chick tendon cells (tenocytes) isolated at embryonic day 14 (E14), from the metatarsal tendons plated within a fibrin gel, that provides a natural substrate for the cells to self-assemble into tendon-like constructs. The cell-gel mixture was plated in between two sets of pins and suture and left in culture for up to 28- days. I show that regions of high collagen deposition are present in distinct regions within tendon constructs, centrally and peripherally. Further analysis of these regions of high collagen deposition was a focus with the goal of understanding and replicating these conditions throughout constructs. Alignment of pins in the experimental set up was important for consistent central deposition of collagen with fibres that were aligned along the long axis of the construct. While improvement of the culture system is required to produce more homogeneous tendon-like tissue, it provides an opportunity to examine the patterns of collagen deposition and uncover the conditions that best support collagen. The establishment and characterisation of these mini-tendon constructs provides an in vitro system to further our understanding of the self-assembly process, to work toward improving tendon-like tissue generation, as well as providing a tool for in vitro testing of molecular pathways and processes required.
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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) | 0.001 | 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".