NLRP3 Inflammasome: A Potential Target in Isoflurane Pretreatment Alleviates Stroke-Induced Retinal Injury in Diabetes
Bibliographic record
Abstract
Ischemic stroke remains a devastating disease which is the leading cause of death worldwide. Visual impairment after stroke is a common complication which may lead to vision loss, greatly impacting life quality of patients. While ischemic stroke is traditionally characterized by a blockage of blood flow to the brain, this may coincide with reduced blood flow to the eye, resulting in retinal ischemia and leading to visual impairment. Diabetes increases the risk of ischemic stroke and induces diabetic retinopathy; the latter may be more sensitive to the ischemic retinal injury. In diabetic status, the underlying mechanism in stroke-induced retinal injury has not been fully clarified. The NLR pyrin domain containing 3 (NLRP3) inflammasome is an important activator of inflammation, which may play a critical role in catalyzing and forming certain pro-inflammatory cytokines in both cerebral and retinal ischemia. Isoflurane has been demonstrated to inhibit the activation of the NLRP3 inflammasome and show neuroprotective effects. In this study, we established a diabetic mouse model and performed the middle cerebral artery occlusion procedure to induce ischemic stroke. Our results revealed that cerebral ischemia-induced retinal injury in the diabetic model. Isoflurane pretreatment alleviated the cerebral and retinal injury after ischemic stroke. Of note, isoflurane pretreatment inhibited the NLRP3 inflammasome activation in the retina, indicating that isoflurane pretreatment may provide substantial retinal protection in stroke-induced retinal injury in diabetes.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".