<i>In vitro</i> interactions between sensory nerves, epidermis, hair follicles and capillaries in a tissue‐engineered reconstructed skin
Bibliographic record
Abstract
Recent findings have established that cutaneous nerves modulate both skin homeostasis and various skin diseases, by influencing cell growth and differentiation, inflammation and wound healing. In order to study the influence of epidermis, hair follicles and capillaries on sensory neurons, and vice‐versa, we developed a tissue‐engineered model of innervated endothelialized reconstructed skin (MIERS). Mouse dorsal root ganglia neurons were seeded on a collagen sponge populated with human fibroblasts and human endothelial cells. Keratinocytes or mice newborn immature hair follicle buds were then seeded on the opposite side of the MIERS to study their influence on sensory nerves growth, and vice versa. A vigorous neurite elongation was detected inside the reconstructed dermis after 14 and 31 days of neurons culture. The presence of endothelial cells induced a significant increase of the neurite elongation after 14 days of culture. The addition of human keratinocytes totally avoided the twofold decrease in the amount of neurites observed between 14 and 31 days in controls. We have successfully developed the MIERS that allowed us to study the effects of epidermis and capillaries on nerve growth. This model will be a useful tool to study the modulation of sensory nerves on wound healing, angiogenesis, hair growth and neurogenic inflammation in the skin.
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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".