Protein kinase C modulation of thermo-sensitive transient receptor potential channels: Implications for pain signaling
Bibliographic record
Abstract
A variety of molecules are reported to be involved in chronic pain. This review outlines the specifics of protein kinase C (PKC), its isoforms and their role in modulating thermo-sensitive transient receptor potential (TRP) channels TRPV1-4, TRPM8, and TRPA1. Anatomically, PKC and thermo-sensitive TRPs are co-expressed in cell bodies of nociceptive dorsal root ganglion (DRG) neurons, which are used as physiological correlates of peripheral and central projections involved in pain transmission. In the past decade, modulation of painful heat-sensitive TRPV1 by PKC has received the most attention. Recently, PKC modulation of other newly discovered thermo-sensitive pain-mediating TRPs has come into focus. Such modulation may occur under conditions of chronic pain resulting from nerve damage or inflammation. Since thermo-TRPs are primary detectors of acute pain stimuli, their modulation by PKC can severely alter their function, resulting in chronic pain. Comprehensive knowledge of pain signaling involving interaction of specific isoforms of PKC with specific thermo-sensitive TRP channels is incomplete. Such information is necessary to dissect out modality specific mechanisms to better manage the complex polymodal nature of chronic pain. This review is an attempt to update the readers on current knowledge of PKC modulation of thermo-sensitive TRPs and highlight implications of such modulation for pain signaling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".