Serum amyloid A-dependent inflammasome activation and acute injury in a mouse model of experimental stroke
Bibliographic record
Abstract
BACKGROUND Serum amyloid A (SAA) proteins increase dramatically in the blood following inflammation. Recently, SAAs are increased in humans following stroke and in ischemic animal models. However, the impact of SAAs on whether this signal is critical in the ischemic brain remains unknown. Therefore, we investigated the role of SAA and SAA signaling in the ischemic brain. METHODS Wildtype and SAA deficient mice were exposed to middle cerebral artery occlusion and reperfusion, examined for the impact of infarct volumes, behavioral changes, inflammatory markers, TUNEL staining, and BBB changes. The underlying mechanisms were investigated using SAA deficient mice, transgenic mice and viral vectors. RESULTS SAA levels were significantly increase following MCAo and mice deficient in SAAs showed reduced infarct volumes and improved behavioral outcomes. SAA deficient mice showed a reduction in TUNEL staining, inflammation and decreased glial activation. Mice lacking acute phase SAAs demonstrated a reduction in expression of the NLRP3 inflammasome and SAA/NLRP3 KO mice showed improvement. Restoration of SAA expression via SAA tg mice or adenoviral expression reestablished the detrimental effects of SAA. A reduction in BBB permeability was seen in the SAA KO mice and anti-SAA antibody treatment reduced the effects on ischemic injury. CONCLUSIONS SAA signaling plays a critical role in regulating NLRP3-induced inflammation and glial activation in the ischemic brain. Blocking this signal will be a promising approach for treating ischemic stroke. GRAPHIC ABSTRACT A graphic abstract is available for this article.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".