Cellular Signalling Pathways of Spinal Pain Neuroplasticity as Targets for Analgesic Development
Bibliographic record
Abstract
Nociceptive inputs from primary afferents are primarily mediated at fast glutamatergic synapses onto second order neurons in the dorsal horn of the spinal cord through activation of AMPA/kainate and NMDA receptor subtypes of ionotropic glutamate receptors. At these glutamatergic synapses several forms of short-lasting and long-lasting enhancement of synaptic transmission are known. Enhancement of excitatory synaptic transmission in nociceptive pathways is thought to be a key neural substrate underlying chronic pain, and thus the cellular and molecular mechanisms producing this enhancement represent potential targets for developing novel forms of therapeutics. Central to the mechanisms for pain hypersensitivity is the NMDA receptor, the activity of which is facilitated by convergent intracellular biochemical cascades in dorsal horn neurons. Cellular changes are not restricted to neurons in the dorsal horn, however, and there is growing evidence for involvement of glia, and of glia-neuronal signaling, in initiating and sustaining enhancement of nociceptive transmission. In particular, a role has emerged for microglia in pain hypersensitivity following nerve injury. This expanded understanding of cellular and molecular signalling mechanisms in the dorsal horn, that includes both neurons and glia, provides a basis of creating new types of strategies for management, and also for diagnosis, of chronic pain.
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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 | 0.002 | 0.002 |
| 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) | 0.004 | 0.003 |
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".