Expression of antioxidant enzymes in lesions of multiple sclerosis and its models
Bibliographic record
Abstract
Abstract Oxidative stress promotes tissue injury in the central nervous system in neurological disorders such as multiple sclerosis (MS). To protect against this, antioxidant enzymes including superoxide dismutase-1 (SOD1), heme oxygenase-1 (HO-1), peroxiredoxin-5 (PRDX5) and glutathione peroxidase-4 (GPX4) may be upregulated. However, whether antioxidant enzyme elevation in mouse models of neurodegeneration corresponds to their expression in human diseases such as MS requires investigation. Here, we analyzed and compared the expression of SOD1, HO-1, PRDX5 and GPX4 in the murine spinal cord of three models of MS: focal lesions induced by (1) oxidized phosphatidylcholine or (2) lysophosphatidylcholine (lysolecithin), and (3) diffuse lesions of experimental autoimmune encephalomyelitis. Notably, CD68 + microglia/macrophages were the predominant cellular populations that expressed the highest levels of the detected antioxidant enzymes. Overall, the expression patterns of antioxidant enzymes across the models were similar. The increase of these antioxidant enzymes was corroborated in MS brain tissue using spatial RNA sequencing. Collectively, these results show that antioxidant capacity is relatively conserved between mouse models and MS lesions, and suggest a need to investigate whether the antioxidant elevation in microglia/macrophages is a protective response during oxidative injury, neurodegeneration, and MS.
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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".