Modulating the activity of human nociceptors with a SCN10A promoter-specific viral vector tool
Bibliographic record
Abstract
Despite the high prevalence of chronic pain as a disease in our society, there is a lack of effective treatment options for patients living with this condition. Gene therapies using recombinant AAVs are a direct method to selectively express genes of interest in target cells with the potential of, in the case of nociceptors, reducing neuronal firing in pain conditions. We designed a recombinant AAV vector expressing cargos whose expression was driven by a portion of the SCN10A (NaV1.8) promoter, which is predominantly active in nociceptors. We validated its specificity for nociceptors in mouse and human dorsal root ganglia and showed that it can drive the expression of functional proteins. Our viral vector and promoter package drove the expression of both excitatory or inhibitory DREADDs in primary human DRG cultures and in whole cell electrophysiology experiments, increased or decreased neuronal firing, respectively. Taken together, we present a novel viral tool that drives expression of cargo specifically in human nociceptors. This will allow for future specific studies of human nociceptor properties as well as pave the way for potential future gene therapies for chronic pain.
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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 | 0.001 |
| 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".