Astrocytes show increased levels of Ero1α in multiple sclerosis and its experimental autoimmune encephalomyelitis animal model
Bibliographic record
Abstract
Multiple sclerosis (MS) and its animal models are characterized by cellular inflammation within the central nervous system (CNS). The sources and consequences of this inflammation are currently not completely understood. Critical signs and mediators of CNS inflammation are reactive oxygen species (ROS) that promote inflammation. ROS originate from a variety of redox-reactive enzymes, one class of which catalyses oxidative protein folding within the endoplasmic reticulum (ER). Here, the unfolded protein response and other signalling mechanisms maintain a balance between ROS producers such as ER oxidoreductin 1α (Ero1α) and antioxidants such as glutathione peroxidase 8 (GPx8). The role of ROS production within the ER has so far not been examined in the context of MS. In this manuscript, we examined how components of the ER redox network change upon MS and experimental autoimmune encephalomyelitis (EAE). We found that unlike GPx8, Ero1α increases within both MS and EAE astrocytes, in parallel with an imbalance of other oxidases such of GPx7, and that no change was observed within neurons. This imbalance of ER redox enzymes can reduce the lifespan of astrocytes, while neurons are not affected. Therefore, Ero1α induction makes astrocytes vulnerable to oxidative stress in the MS and EAE pathologies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".