Spatial and temporal relationship between monocyte chemoattractant protein‐1 expression and spinal glial activation following peripheral nerve injury
Bibliographic record
Abstract
Peripheral nerve injury can induce spinal microglial/astrocyte activation. Substances released by activated glial cells excite spinal nociceptive neurons. Pharmacological disruption of glial activation or antagonism of substances released by activated glia prevent or reverse pain hypersensitivity. It is not known, however, what causes spinal cord glia to shift from a resting to an activated state. In an attempt to understand the potential role of monocyte chemoattractant protein-1 (MCP-1) in triggering spinal glial activation and its contribution to the development of neuropathic pain, we investigated the effect of peripheral nerve injury on MCP-1 expression in dorsal root ganglia (DRG) and the spinal cord, and established its temporal relationship with activation of spinal microglia and astrocytes. We observed that MCP-1 was induced by chronic constriction of the sciatic nerve in DRG sensory neurons, spinal cord motor neurons and in the superficial dorsal horn, ipsilateral to the injury. Neuronal MCP-1 induction was followed by surrounding microglial activation. After peaking at day 7 after injury, MCP-1 levels began to decline rapidly and had returned to baseline by day 150. In contrast, microglial activation peaked by day 14 and declined afterwards to reach a lower, yet significantly raised level beyond day 22 and remained increased until the end of the test period. Astrocyte activation became detectable later, progressed more slowly and also remained increased until the end of the test period, in parallel with a decreased nociceptive threshold. Our results suggest that neuronal MCP-1 may serve as a trigger for spinal microglial activation, which participates in the initiation of neuropathic pain. Delayed, sustained astrocyte activation may participate with microglia in the persistent phase of pain hypersensitivity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".