Elevated ATG5 expression in autoimmune demyelination and multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is an inflammatory central nervous system (CNS) disorder characterized by T cell-mediated demyelination. In MS, prolonged T cell survival and increased T cell proliferation have been linked to disease relapse and progression. Recently, the autophagy-related gene 5 (Atg5) has been shown to modulate T cell survival. In this study, we examined the expression of Atg5 using both a mouse model of autoimmune demyelination as well as blood and brain tissues from MS cases. Quantitative real-time PCR analysis of RNA isolated from blood samples of experimental autoimmune encephalomyelitis (EAE) mice revealed a strong correlation between Atg5 expression and clinical disability.Analysis of protein extracted from these cells confirmed both upregulation and post-translational modification of Atg5, the latter of which was positively correlated with EAE severity. Analysis of RNA extracted from T cells isolated by negative selection indicated that Atg5 expression was significantly elevated in individuals with active relapsing-remitting MS compared to non-diseased controls. Brain tissue sections from relapsing-remitting MS cases examined by immunofluorescent histochemistry suggested that encephalitogenic T cells are a source of Atg5 expression in MS brain samples. Together these data suggest that increased T cell expression of Atg5 may contribute to inflammatory demyelination in 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".