AD16 attenuates neuroinflammation induced by cerebral ischemia through down-regulating astrocytes A1 polarization
Bibliographic record
Abstract
A1 polarization of astrocytes mediated prolonged inflammation contributing to brain injury in ischemic stroke. We have previously shown that AD16 protects against neonatal hypoxic-ischemic brain damage in vivo and oxygen-glucose deprivation in vitro. More recently, AD16 has demonstrated safety, tolerability, and favorable pharmacokinetics in a randomized controlled phase I trial. In this study, we utilized a rat model of transient middle cerebral artery occlusion (tMCAO) to explore whether the anti-inflammatory compound AD16 protects against ischemic brain injury by regulating A1 polarization and its underlying mechanisms. Our results showed that AD16 treatment significantly reduced the brain infarcted volume and improved neurological function in tMCAO rats. GO analysis results show that differential genes among the Sham, tMCAO and AD16 treatment groups are involved in the regulation of cytokine and inflammatory response. KEGG enrichment pathways analysis mainly enriched in cytokine-cytokine receptor interaction, viral protein interaction with cytokine-cytokine receptor, TNF, chemokine, NF-κB and IL-17 signaling pathway. Furthermore, AD16 treatment decreased the permeability of the blood-brain barrier and suppressed neuroinflammation. AD16 treatment also significantly reduced the polarization of A1 and inhibited NF-κB and JAK2/STAT3 signaling pathways. This study demonstrates that AD16 protects against brain injury in ischemic stroke by reducing A1 polarization to suppress neuroinflammation through downregulating NF-κB and JAK2/STAT3 signaling. Our findings uncover a potential molecular mechanism for AD16 and suggest that AD16 holds promising therapeutic potential against cerebral 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".