Peptide-mediated transdermal delivery of botulinum neurotoxin type A reduces neurogenic inflammation in the skin
Bibliographic record
Abstract
Release of inflammatory pain mediators from peripheral sensory afferent endings contributes to the development of a positive feedback cycle resulting in chronic inflammation and pain. Botulinum neurotoxin type A (BoNT-A) blocks exocytosis of neurotransmitters and may therefore block the release of pain modulators in the periphery. Subcutaneous administration of BoNT-A (2.5, 5 and 10U) reduced plasma extravasation (PE) caused by electrical stimulation of the saphenous nerve or capsaicin in the rat hindpaw skin (ANOVA, Post hoc Tukey, p<0.05, n=6). Subcutaneous BoNT-A also reduced blood flow changes evoked by saphenous nerve stimulation (ANOVA, Post hoc Tukey, p<0.05, n=6). Subcutaneous BoNT-A had no effect on PE induced by local injection of substance P (SP) or vasodilation induced by local CGRP injection. Although BoNT-A is an effective treatment for a wide range of painful conditions, the toxin's large size necessitates that it be injected at numerous sites. We found that a short synthetic peptide (TD-1) can facilitate effective transdermal delivery of BoNT-A through intact skin. Coadministration of TD-1 and BoNT-A to the hindpaw skin resulted in a significant reduction in PE evoked by electrical stimulation. The findings show that BoNT-A can be administered subcutaneously or topically with a novel transdermal delivery peptide to reduce inflammation produced by activating nociceptors in the skin. Peptide-mediated delivery of BoNT-A is an easy and non-invasive way of administering the toxin that may prove to be useful in clinical practice.
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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".