Targeting the NLRP3-ROS Axis: Disrupting the Oxidative-Inflammatory Vicious Cycle in Intracerebral Hemorrhage
Bibliographic record
Abstract
Intracerebral hemorrhage (ICH) is a highly fatal disease that currently lacks effective treatment options. However, secondary brain injury has become a key focus in translational research, with oxidative stress (OS) identified as a central factor in ICH pathophysiology. Following ICH, hematoma components and inflammatory factors overwhelm the antioxidant defense system, triggering OS. Concurrently, neuroinflammation arises, driven by activated microglia that adopt a pro-inflammatory phenotype and release cytokines and chemokines. While neuroinflammation may support repair, it can also cause harmful secondary damage. Recent evidence indicates that NLRP3 is an important inflammasome considered a key player in OS and neuroinflammation. OS can activate the NLRP3 inflammasome by producing reactive oxygen species (ROS), further exacerbating the inflammatory response. Additionally, NLRP3 also plays an important role in regulating neuroinflammation. The activation of the NLRP3 inflammasome promotes the release of pro-inflammatory cytokines, further intensifying the neuroinflammatory response. The activation of NLRP3 is closely related to the polarization of microglia, potentially driving microglia to polarize towards the M1 type (pro-inflammatory), thereby exacerbating neuroinflammation. Therefore, we hypothesize that NLRP3 plays a critical regulatory role in OS and neuroinflammation following ICH. This review summarizes the regulatory role of the NLRP3 inflammasome in the interplay between OS and neuroinflammation, as well as its potential therapeutic targets related to ICH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".