Influence of injury and cytokines on synthesis of monocyte chemoattractant protein‐1 mRNA in peripheral nervous tissue
Bibliographic record
Abstract
The signals and the source of the signals for monocyte/macrophage entry into the injured peripheral nervous tissue are not yet defined. This study was undertaken to determine the distribution of the chemokine monocyte chemoattractant protein-1 mRNA in injured rat and mouse nerves and to investigate the mechanisms that regulate its synthesis in rat Schwann cells. Results from RNase protection assays showed that, following sciatic nerve transection in rats, mRNA for monocyte chemoattractant protein-1 was induced at the site of lesion within 3 h of surgery and in more distal segments from 24 h for at least 8 days. In cultured Schwann cells, tumour necrosis factor-alpha but not interleukin-1 beta, interleukin-6, transforming growth factor-beta 1, platelet-derived growth factor-BB or nerve growth factor induced monocyte chemoattractant protein-1 mRNA in a time- and dose-dependent fashion. The induction of monocyte chemoattractant protein-1 mRNA in Schwann cells treated with tumour necrosis factor-alpha was reduced by inhibitors of nuclear factor-kappa B and the p38 mitogen-activated protein kinase. In mice that lack the two receptors for tumour necrosis factor, the message for JE, a murine homologue of monocyte chemoattractant protein-1, was still induced within 6 h of injury at the lesion site. However, in more distal segments 4 days after transection the concentration of JE mRNA was lower than that of control mice. Tumor necrosis factor-alpha is the only cytokine that was shown to induce monocyte chemoattractant protein-1 mRNA in cultured Schwann cells and is one of the factors that regulate the synthesis of monocyte chemoattractant protein-1 in injured nerves.
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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 | 0.000 |
| 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".