Deferoxamine alleviates brain ischemia/reperfusion injury through decreasing LAG-3 and α-Syn expression
Bibliographic record
Abstract
Ischemic stroke is characterized by brain tissue iron accumulation. Alpha-synuclein (α-Syn) is a neuronal protein, its overexpression in ischemic stroke triggers apoptosis. Lymphocyte activation gene-3 (LAG-3), a receptor for α-Syn, enhances its neurotoxic effects. It is split from the cell membrane forming soluble LAG-3 (sLAG-3) in the bloodstream. The expression of LAG-3 in the brain, its relation to iron and α-Syn, as well as the association between serum sLAG-3 levels, iron, α-Syn, and stroke severity remains poorly understood. A case-control study was generated involving 24 patients with acute ischemic stroke and 24 healthy controls. In addition, an experimental study was designed involving 24 Wistar-albino rats. We randomly assigned rats to three groups: sham-operated, brain ischemia, and deferoxamine-treated ischemic rats. Ischemia decreased serum levels of iron, while increased serum levels of α-Syn and sLAG-3. Significant diagnostic performance of serum α-Syn and sLAG-3 was determined using the ROC curve (AUC = 0.962, 83.33% sensitivity, and 95.83% specificity for α-Syn; AUC = 0.755 with 62.50% sensitivity and 87.50% specificity for LAG-3). In rats, ischemia elevated brain tissue iron, α-Syn, and LAG-3 which were reduced following deferoxamine treatment. In conclusion, brain ischemia is associated with iron accumulation that promotes α-Syn expression and aggregation, potentially through increasing LAG-3 expression which improved after deferoxamine injection. In addition, this study illuminates the future beneficial targeting of LAG-3 in brain ischemia.
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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) | 0.000 | 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".