Peptide-mediated transdermal delivery of botulinum neurotoxin type A reduces neurogenic inflammation in the skin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 it