The timecourse of neurogenic inflammation and the effect of modulatory agents.
Bibliographic record
Abstract
Activation of nociceptors causes them to secrete neuropeptides, such as substance P (SP) and calcitonin-gene related peptide (CGRP). By reacting with receptors on blood vessels these peptides contribute to inflammation by evoking vasodilation and increasing vascular permeability that allows proteins and fluid to leave the blood vessels (plasma extravasation, PE). These substances can also lead to the sensitization of nociceptors and the resulting positive feedback thereby prolongs inflammation and pain. Thus, blocking the release of neuropeptides may have important therapeutic value in pain conditions where neuropeptides have been implicated. Therefore, the aim of this study was to define the time course of changes in vascular permeability and to test the ability of novel agents to modulate this response. PE and vasodilation was evoked by stimulating the saphenous nerve or by injecting the chemical irritant capsaicin into the rat hindpaw. Changes in blood flow were evaluated with a laser Doppler and digitized image analysis was used to measure changes in reflectance of the skin due to accumulation of extravasated (EB) dye. Analysis of the change in pixel intensity in the digitized images revealed that the magnitude of PE was dependent on the stimulus pulse number. Moreover, the time course of enhanced vascular permeability produced by electrical stimulation was a transient event compared to a much longer response with capsaicin. It was also demonstrated that sumatriptan, (a 5-HT1B/D receptor agonist) and botulinum neurotoxin type-A were effective treatments for capsaicin and saphenous nerve induced vasodilation and PE. Neither drug interfered with activation of the SP or CGRP receptor, which may suggest that both drugs work by inhibiting neuropeptide release. Therefore, this study has described the time course of vascular permeability evoked by two different stimuli and has demonstrated the ability of two novel agents to attenuate these responses.
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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) | 0.004 | 0.001 |
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".