Characterization of cell proliferation in rat spinal cord following peripheral nerve injury and the relationship with neuropathic pain
Bibliographic record
Abstract
Glial activation is a typical response of the central nervous system to nerve injury. In the current investigation, we characterized the temporal and spatial pattern of glial proliferation, one of the most conspicuous features of glial activation, in relation to nerve injury-induced neuropathic pain. Using bromodeoxyuridine (BrdU) as a mitotic marker, we analyzed cell proliferation in the spinal cord, identified the phenotype of dividing cells, traced their fate, and correlated these phenomena with behavioural assays of the neuropathic pain syndrome. Our results demonstrated that peripheral nerve injury induced an early and transient cell proliferation, on the spinal cord ipsilateral to the nerve lesion which peaked at day 3 post-surgery. The majority of the proliferating cells were Iba-1(+) microglia, together with some NG2(+) oligodendrocyte progenitors, and GFAP(+) astrocytes. These newly generated cells continued to divide over time with the response peaking at day 14 post-injury. Microglia were always the predominant phenotype which made up over 60% of activated microglia derived from this newly generated cell population. There was a close temporal correlation between microglial proliferation in the spinal cord dorsal horn and the abnormal pain responses, suggesting a contribution of the new microglia to the genesis of the neuropathic pain symptoms.
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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 | 0.001 | 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 | 0.000 |
| 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".