Keratinocyte–TRPV1 sensory neuron interactions in a genetically controllable mouse model of chronic neuropathic itch
Bibliographic record
Abstract
Our understanding of neural circuits that respond to skin dysfunction, triggering itch, and pathophysiological scratching remains incomplete. Here, we describe a profound chronic itch phenotype in transgenic mice expressing the tetracycline transactivator (tTA) gene within the Phox2a lineage. Phox2a; tTA mice exhibit intense, localized scratching and regional skin lesions, controllable by the tTA inhibitor, doxycycline. As gabapentin and the kappa opioid receptor agonist, nalfurafine, but not morphine, significantly reduce scratching, this phenotype has a pharmacological profile of neuropathic pruritus. Importantly, the Phox2a; tTA expression occurs in a spatially restricted population of skin keratinocytes that overlaps precisely with the skin area that is scratched. Localized G i -DREADD-mediated inactivation of these Phox2a-keratinocytes completely reverses the skin lesions, while inducible tTA activation of keratinocytes initiates the condition. Notably, ablation of TRPV1-expressing primary afferent neurons also reduces scratching and skin lesions, but this occurs slowly, over a course of two months. In contrast denervation induced loss of all cutaneous input rapidly blocks scratching. These findings identify the cellular, molecular, and topographic basis of a robust and chronic sensory neuron–dependent and gabapentin-responsive neuropathic itch that is initiated by genetic factors within keratinocytes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".