Partial sciatic nerve ligation induces increase in the phosphorylation of extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) in astrocytes in the lumbar spinal dorsal horn and the gracile nucleus
Bibliographic record
Abstract
The activation of glial cells in the spinal dorsal horn and the gracile nucleus by inflammation and nerve injury has been suggested to be involved in neuronal plasticity and central sensitization, hence contributing to tactile allodynia. The aim of this study was to determine the possible intracellular signal transduction pathway associated with glial cells, which have been activated by partial sciatic nerve ligation (PSNL), a well-characterized rat model of neuropathic pain. At 3 weeks post-lesion, PSNL markedly increased glia fibrillary acidic protein (GFAP) immunoreactive (IR) astrocytes in both the L4-5 spinal dorsal horn and the gracile nucleus. Moreover, PSNL increased the phosphorylation of mitogen activated protein (MAP) kinases, including the extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK), but not p38, in glia-like cells in these same areas. Both phosphorylated (p) ERK- and JNK-IR cells were co-localized with GFAP, suggesting their expression in reactive astrocytes. In summary, our data indicate that PSNL activates ERK/MAP and JNK/MAP kinase pathways in astrocytes in the dorsal horn and the gracile nucleus, these events possibly being involved in the pathogenesis of neuropathic pain.
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 | 0.000 | 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 | 0.000 |
| 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".