Functional Innervation in Tissue Engineered Models for In Vitro Study and Testing Purposes
Bibliographic record
Abstract
The biotechnology industry is rapidly expanding and the emerging field of tissue engineering is projected to have a high impact in the near future. Recently the field of cellular, drug, and prosthetic delivery has melded with the field of tissue engineering to make simulated tissues. In addition to their roles as tissue substitutes for transplantation, these simulated tissues may provide more accurate models and environments for toxicology testing and the study of peripheral nerves. The current study demonstrates the importance of innervation, in general, for the function of engineered tissues. We observe that the presence of nerves in a tissue engineered (TE) human cornea model enhances the growth of the epithelium and the formation of its protective mucin layer. Innervation also confers protection to the epithelium from chemical insult, as determined by the level of post-treatment epithelial cell death. We demonstrate differential responses of the nerves to chemical stimuli by changes in intracellular sodium as measured by 2-photon microscopy. The 2-photon imaging techniques also allow for the visualization and study of the fine sensory axon fibers within the 3-dimensional tissue. This work demonstrates a role for innervation in the protective quality and function of the engineered tissue, and the potential to use the nerves themselves as indicators of the severity of an insult. These results are important to consider for the development of any optimized TE models for in vitro study and testing 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".