Dynamic three-dimensional coculture model: The future of tissue engineering applied to the peripheral nervous system
Bibliographic record
Abstract
Traumatic injuries to the peripheral nervous system (PNI) can lead to severe consequences such as paralysis. Unfortunately, current treatments rarely allow for satisfactory functional recovery. The high healthcare costs associated with PNS injuries, worker disability, and low patient satisfaction press for alternative solutions that surpass current standards. For the treatment of injuries with a deficit of less than 30 mm to bridge, the use of synthetic nerve conduits (NGC) is favored. However, to develop such promising therapeutic strategies, in vitro models that more faithfully mimic nerve physiology are needed. The absence of a clinically scaled model with essential elements such as a three-dimension environment and dynamic coculture has hindered progress in this field. The presented research focuses on the development of an in vitro coculture model of the peripheral nervous system (PNS) involving the use of functional biomaterial which microstructure replicates nerve topography. Initially, the behavior of neuron-derived cell lines (N) and Schwann cells (SC) in contact with a short section of biomaterial (5 mm) was studied. Subsequent investigations, using fluorescent markers and survival assays, demonstrated the synergistic effects of coculture. These optimized parameters were then applied to longer biomaterials (30 mm), equivalent to clinically used NGC. The results obtained demonstrated the possibility of maintaining an extended coculture of SC and N over a 7-day period on a clinically scaled biomaterial, observing some functionality. In the long term, the knowledge gained from this work will contribute to a better understanding of the PNS regeneration process and promote the development of future therapeutic approaches while reducing reliance on animal experimentation. This model can be used for drug screening and adapted for personalized medicine trials. Ultimately, this work fills a critical gap in current research, providing a transformative approach to study and advance treatments for PNS injuries.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".