Spinal microglia and neuropathic pain in young rats
Bibliographic record
Abstract
Neuropathic pain behaviour is not observed in neonatal rats and tactile allodynia does not develop in the spared nerve injury (SNI) model until rats are 4 weeks of age at the time of surgery. Since activated spinal microglia are known to play a key role in neuropathic pain, we have investigated whether the microglial response to nerve injury in young rats differs from that in adults. Here we show that dorsal horn microglial activation, visualised with IBA-1 immunostaining, is significantly less in postnatal day (P) 10 rat pups than in adults, 7 days after SNI. This was confirmed by qPCR analysis of IBA-1 mRNA and mRNA of other microglial markers, integrin-alpha M, MHC-II DMalpha and MHC-II DMbeta. Dorsal horn IBA-1+ve microglia could be activated, however, by intraspinal injections of lipopolysaccharide (LPS) or N-methyl-d-aspartate (NMDA) at P10, although the increase in the levels of mRNA for all microglial markers was less than in the adult rat. In addition, P10 rats developed a small but significant mechanical allodynia in response to intrathecal LPS. Intrathecal injection of cultured ATP-activated microglia, known to cause mechanical allodynia in adult rats, had no behavioural effect at P10 and only began to cause allodynia if injections were performed at P16. The results clearly demonstrate immaturity of the microglial response triggered by nerve injury in the first postnatal weeks which may explain the absence of tactile allodynia following peripheral nerve injury in young rats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".