Innervated Coculture Device to Model Peripheral Nerve-Mediated Fibroblast Activation
Bibliographic record
Abstract
Cutaneous wound healing is a complex process involving various cellular and molecular interactions, resulting in the formation of a collagen-rich scar with imperfect function and morphology. Dermal fibroblasts are crucial to successful wound healing, migrating to the wound site where they are activated to provide extracellular matrix remodeling and wound closure. Peripheral nerves have been shown to play an important role in wound healing, with loss or damage to these nerves often leading to impaired healing and the formation of chronic nonhealing wounds. Previous research has suggested that sensory nerves secrete trophic factors that can regulate wound healing, including fibroblast activation; however, the direct cell–cell interaction between nerves and fibroblasts has not been extensively studied. To address this knowledge gap, we developed an in vitro co-culture model using a device called the IFlowPlate. This model supports the long-term viability of multiple cell types while allowing for direct contact between sensory nerve cells and dermal fibroblasts. Using the IFlowPlate, we demonstrate that co-culture of dorsal root ganglia with dermal fibroblasts increases fibroblast proliferation, collagen and α-smooth muscle actin expression, and secretion of pro-wound healing factors, suggesting that nerves can promote wound healing by modulating fibroblast activation. The IFlowPlate offers a user-friendly and high-throughput platform to study the in vitro interactions between nerves and a variety of cell types that can be applied to wound healing and other important biological processes.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".