Automated production of nerve repair constructs containing endothelial cell tube-like structures
Bibliographic record
Abstract
Abstract Engineered neural tissue (EngNT) is a stabilised aligned cellular hydrogel that offers a potential alternative to the nerve autograft for the treatment of severe peripheral nerve injury. This work aimed to automate the production of EngNT, to improve the feasibility of scalable manufacture for clinical translation. Endothelial cells were used as the cellular component of the EngNT, with the formation of endothelial cell tube-like structures mimicking the polarised vascular structures formed early on in the natural regenerative process. Gel aspiration-ejection for the production of EngNT was automated by integrating a syringe pump with a robotic positioning system, using software coded in Python to control both devices. Having established the production method and tested mechanical properties, the EngNT containing human umbilical vein endothelial cells (EngNT-HUVEC) was characterised in terms of viability and alignment, compatibility with neurite outgrowth from rat dorsal root ganglion neurons and formation of endothelial cell networks in vitro. EngNT-HUVEC manufactured using the automated system contained viable and aligned endothelial cells, which developed into a network of multinucleated endothelial cell tube-like structures inside the constructs and an outer layer of endothelialisation. The EngNT-HUVEC constructs were made in various sizes within minutes. Constructs provided support and guidance to regenerating neurites in vitro. This work automated the formation of EngNT, facilitating high throughput manufacture at scale. The formation of endothelial cell tube-like structures within stabilised hydrogels provides an engineered tissue with potential for use in nerve repair.
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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".