Proinflammatory cytokines promote glial heme oxygenase‐1 expression and mitochondrial iron deposition: implications for multiple sclerosis
Bibliographic record
Abstract
Proinflammatory cytokines, pathological iron deposition, and oxidative stress have been implicated in the pathogenesis of multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). HO-1 mRNA levels and mitochondrial uptake of [(55)Fe]Cl(3)-derived iron were measured in rat astroglial cultures exposed to interleukin-1beta (IL-1beta) or tumor necrosis factor-alpha (TNF-alpha) alone or in combination with the heme oxygenase-1 (HO-1) inhibitors, tin mesoporphyrin (SnMP) or dexamthasone (DEX), or interferon beta1b (INF-beta). HO-1 expression in astrocytes was evaluated by immunohistochemical staining of spinal cord tissue derived from MS and control subjects. IL-1beta or TNF-alpha promoted sequestration of non-transferrin-derived (55)Fe by astroglial mitochondria. HO-1 inhibitors, mitochondrial permeability transition pore (MTP) blockers and antioxidants significantly attenuated cytokine-related mitochondrial iron sequestration in these cells. IFN-beta decreased HO-1 expression and mitochondrial iron sequestration in IL-1beta- and TNF-alpha-challenged astroglia. The percentage of astrocytes coexpressing HO-1 in affected spinal cord from MS patients (57.3% +/- 12.8%) was significantly greater (p < 0.05) than in normal spinal cord derived from controls subjects (15.4% +/- 8.4%). HO-1 is over-expressed in MS spinal cord astroglia and may promote mitochondrial iron deposition in MS plaques. In MS, IFN-beta may attenuate glial HO-1 gene induction and aberrant mitochondrial iron deposition accruing from exposure to proinflammatory cytokines.
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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".