Neuroprotective Effects of Chlorogenic Acid in a Mouse Model of Intracerebral Hemorrhage Associated with Reduced Extracellular Matrix Metalloproteinase Inducer
Bibliographic record
Abstract
Chlorogenic acid (CGA) has been reported to have various biological activities, such as anti-inflammatory, anti-oxidant and anti-apoptosis effects. However, the role of CGA in intracerebral hemorrhage (ICH) and the underlying mechanisms remain undiscovered. The current study aims to investigate the effect of CGA on neuroinflammation and neuronal apoptosis after inhibition of EMMPRIN in a collagenase-induced ICH mouse model. Dose optimization data showed that intraperitoneal administration of CGA (30 mg/kg) significantly attenuated neurological impairments and reduced brain water content at 24 h and 72 h compared with ICH mice given vehicle. Western blot and immunofluorescence analyses revealed that CGA remarkably decreased the expression of extracellular matrix metalloproteinase inducer (EMMPRIN) in perihematomal areas at 72 h after ICH. CGA also reduced the expression of matrix metalloproteinases-2/9 (MMP-2/9) at 72 h after ICH. CGA diminished Evans blue dye extravasation and reduced the loss of zonula occludens-1 (ZO-1) and occludin. CGA-treated mice had fewer activated Iba-1-positive microglia and MPO-positive neutrophils. Finally, CGA suppressed cell death around the hematoma and reduced overall brain injury. These outcomes highlight that CGA treatment confers neuroprotection in ICH likely by inhibiting expression of EMMPRIN and MMP-2/9, and alleviating neuroinflammation, blood-brain barrier (BBB) disruption, cell death and brain injury.
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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 | 0.000 | 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".