Bibliographic record
Abstract
PURPOSE OF REVIEW: The complexity of pain processing in clinical pain conditions and in animal models has revealed many time-related changes and an abundance of molecular drug targets. There continues to be insecurity, however, about new target validation in clinical pain and thus most analgesia development is of high risk for evolving new pain therapies. The present review highlights a number of molecular targets being pursued for pain control. RECENT FINDINGS: Many pain targets are critically dependent on the pain model/lesion type. Neural and glial plasticity, ranging from changes in molecular expression and receptor phosphorylation to profound morphological reorganization, has been described under these conditions. Pain modulation has been shown to involve all major families of regulatory proteins such as the G-protein coupled receptors, ion channels, regulatory enzymes, neurotrophins, and kinases, offering an abundance of targets and therapeutic opportunities for symptomatic pain relief. SUMMARY: Many molecular targets have been highlighted with some being the focus of current analgesia research. Some of these (e.g. vanilloid receptor 1, cannabinoid receptor 1, sodium channel NaV 1.8) have been evaluated in animal studies and in preliminary clinical studies, but others are highly novel and riskier analgesia pain targets (e.g. metabotropic glutamate receptors, sensory neurone specific receptors, kinase inhibitors).
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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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".