A Humanized In Vitro Model of Innervated Skin for Transdermal Analgesic Testing
Bibliographic record
Abstract
Sensory innervation of the skin is essential for its function, homeostasis, and wound healing mechanisms. Thus, to adequately model the cellular microenvironment and function of native skin, in vitro human skin equivalents (hSE) containing a sensory neuron population began to be researched. In this work, a fully human 3D platform of hSE innervated by induced pluripotent stem cell-derived nociceptor neurospheres (hNNs), mimicking the native mode of innervation, is established. Both the hSE and nociceptor population exhibit morphological and phenotypical characteristics resembling their native counterparts, such as epidermal and dermal layer formation and nociceptor marker exhibition, respectively. In the co-culture platform, neurites develop from the hNNs and navigate in 3D to innervate the hSE from a distance. To probe both skin and nociceptor functionality, a clinically available capsaicin patch (Qutenza) is applied directly over the hSE section and neuron reaction is analyzed. Application of the patch causes an exposure time-dependent neurite regression and degeneration. In platforms absent of hSE, axonal degeneration is further increased, highlighting the role of the skin construct as a barrier. In sum, an in vitro tool of functional innervated skin with high interest for preclinical research is established.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".