Direct neuronal protection by the protease‐activated receptor PAR4 antagonist ML354 after experimental stroke in mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Thrombo-inflammation is a key feature of stroke pathophysiology and provides multiple candidate drug targets. Thrombin exerts coagulation-independent actions via protease-activated receptors (PAR), of which PAR1 has been implicated in stroke-associated neuroinflammation. The role of PAR4 in this context is less clear. This study examined if the selective PAR4 antagonist ML354 provides neuroprotection in experimental stroke and explored the underlying mechanisms. EXPERIMENTAL APPROACH: -imaging was performed upon acute stimulation with a PAR4 activating peptide or glutamate. Male mice underwent sham operation or transient middle cerebral artery occlusion (tMCAO), with ML354 or vehicle treatment beginning at recanalization. A subset of mice received a platelet-depleting antibody. Stroke size and functional outcomes were assessed. Abundance of target genes, proteins, and cell markers was determined in cultured cells and tissues by qPCR, immunoblotting, and immunofluorescence. KEY RESULTS: Stroke up-regulated PAR4 expression in cortical neurons in vitro and in vivo. OGD augments spontaneous and PAR4-mediated neuronal activity; ML354 suppresses OGD-induced neuronal excitotoxicity and apoptosis. ML354 applied in vivo after tMCAO reduced infarct size, apoptotic markers, macrophage accumulation, and interleukin-1β expression. Platelet depletion did not affect infarct size in mice with tMCAO ± ML354. CONCLUSIONS AND IMPLICATIONS: Selective PAR4 inhibition during reperfusion improves infarct size and neurological function after experimental stroke by blunting neuronal excitability, apoptosis, and local inflammation. PAR4 antagonists may provide additional neuroprotective benefits in patients with acute stroke beyond their canonical antiplatelet action.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".