The Regulation of Reactive Changes Around Multiple Sclerosis Lesions by Phosphorylated Signal Transducer and Activator of Transcription
Bibliographic record
Abstract
Activation of signal transducer and activator of transcription 3 (STAT3) by phosphorylation is thought to mediate anti-inflammatory responses to CNS injury. Several studies have reported an increase in phosphorylated STAT3 (pSTAT3) in peripheral T cells and monocytes from patients with multiple sclerosis (MS) during relapses, suggesting that pSTAT3 might represent an inflammatory marker. Here, we examined immunoreactivity for pSTAT3 in brain tissue samples from MS patients and controls. Phosphorylated STAT3 immunoreactivity was sparse within lesions, with no difference between active and inactive lesions. It was, however, significantly greater in white matter (WM) adjacent to active and inactive lesions; moreover, it was significantly greater in WM adjacent to active versus inactive lesions. Phosphorylated STAT3-positive cells were identified as astrocytes and macrophages/microglia. Phosphorylated STAT3 expression was also detected by Western blotting in WM of patients with MS. In comparison, pSTAT3 immunoreactivity was either rare or found focally in brain tissue samples from patients with other neurologic diseases. Our findings show that pSTAT3 does not correlate with inflammatory activity in MS lesions, but that it may play an important role in regulating reactive changes proximal to MS lesions.
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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".