TRPA1 activation in a human sensory neuronal model: relevance to cough hypersensitivity?
Bibliographic record
Abstract
The cough reflex becomes hyperresponsive in acute and chronic respiratory diseases, but understanding the underlying mechanism is hampered by difficulty accessing human tissue containing both nerve endings and neuronal cell bodies. We refined an adult stem cell sensory neuronal model to overcome the limited availability of human neurones and applied the model to study transient receptor potential ankyrin 1 (TRPA1) channel expression and activation. Human dental pulp stem cells (hDPSCs) were differentiated towards a neuronal phenotype, termed peripheral neuronal equivalents (PNEs). Using molecular and immunohistochemical techniques, together with Ca 2+ microfluorimetry and whole cell patch clamping, we investigated roles for nerve growth factor (NGF) and the viral mimic poly I:C in TRPA1 activation. PNEs exhibited morphological, molecular and functional characteristics of sensory neurons and expressed functional TRPA1 channels. PNE treatment with NGF for 20 min generated significantly larger inward and outward currents compared to untreated PNEs in response to the TRPA1 agonist cinnamaldehyde (p<0.05). PNE treatment with poly I:C caused similar transient heightened responses to TRPA1 activation compared to untreated cells. Using the PNE neuronal model we observed both NGF and poly I:C mediated sensory neuronal hyperresponsiveness, representing potential neuro-inflammatory mechanisms associated with heightened nociceptive responses recognised in cough hypersensitivity syndrome.
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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 | 0.001 | 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".