Targeting cell surface trafficking of pain-facilitating receptors to treat chronic pain conditions
Bibliographic record
Abstract
INTRODUCTION: Chronic pain conditions are serious clinical concerns. Its genesis is closely associated with sensitization of nociceptive primary sensory neurons (nociceptors) and dorsal horn neurons by various pain mediators produced during inflammation and tissue injury. Growing evidence showed that increasing cell surface trafficking of pain-facilitating receptors is an important mechanism underlying the peripheral and central sensitization. AREAS COVERED: We summarized the progress of this area over the past decade by showing that inflammation, tissue damage or pain mediators facilitate cell surface trafficking of pain-facilitating receptors such as transient receptor potential vanilloid-1, transient receptor potential ankyrin-1, voltage-gated sodium channel 1.8, P2X3 and EP4 in primary sensory neurons, GluR1 and GluR2 of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors, NR1 and NR2 of N-methyl-d-aspartate receptors and acid-sensing ion channels 1 in dorsal horn neurons and P2X4 in spinal microglia. The anti-allodynic effects of gabapentin was mediated by blocking surface trafficking of α2δ1 and α2δ2 subunits of voltage-gated calcium channels in primary sensory and dorsal horn neurons. EXPERT OPINION: Pain mediators stimulate forward surface trafficking of their own and/or other pain-facilitating receptors to amplify pain intensity and duration. Enhancing surface abundance of pain-facilitating receptors in nociceptors and dorsal horn neurons is an important mechanism underpinning chronic pain states. Targeting specific trafficking events of pain-facilitating receptors may open a novel therapeutic avenue to more efficiently treat chronic pain conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".