Translation regulation in the spinal dorsal horn – A key mechanism for development of chronic pain
Bibliographic record
Abstract
Chronic pain is a pathological condition characterized by long-lasting pain after damaged tissue has healed. Chronic pain can be caused and maintained by changes in various components of the pain pathway, including sensory neurons, spinal cord and higher brain centers. Exaggerated sensitivity and responsiveness of spinal nociceptive circuits, representing maladaptive plasticity, play key roles in the amplification of peripheral signals in chronic pain conditions. This spinal amplification mechanism profoundly contributes to the development and maintenance of chronic pain hypersensitivity in response to peripheral injury, and in some cases occurs independently of the peripheral stimulus. Long-lasting changes in the activity of spinal neurons are caused by alterations in their cellular proteome, which relies on de novo gene expression. Recent evidence indicates that translational control of gene expression plays a major role in determining protein levels, and is intricately involved in different forms of intrinsic and synaptic plasticity. In this review, we summarize findings supporting a key role for translational control in spinal cord-dependent mechanisms of chronic pain, and present recent approaches to reverse persistent pain by targeting these mechanisms.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".