IM-12 activates the Wnt–β-catenin signaling pathway and attenuates rtPA-induced hemorrhagic transformation in rats after acute ischemic stroke
Bibliographic record
Abstract
Hemorrhagic transformation (HT) is a devastating complication for patients with acute ischemic stroke (AIS) who are treated with tissue plasminogen activator (tPA). HT is associated with high morbidity and mortality, but no effective treatments are currently available to reduce the risk of HT. Therefore, methods to prevent HT are urgently needed. In this study, we used IM-12, an inhibitor of glycogen synthase kinase 3β (GSK-3β), to evaluate the role of the Wnt-β-catenin signaling pathway in recombinant tPA (rtPA)-induced HT. Sprague-Dawley rats were subjected to a middle cerebral artery occlusion (MCAO) model of ischemic stroke, and then were either administered rtPA, rtPA combined with IM-12, or the vehicle at 4 h after stroke was induced. Our results indicate that rats subjected to HT had more severe neurological deficits, brain edema, and blood-brain barrier (BBB) breakdown, and had a greater infarction volume than the control group. Rats treated with IM-12 had improved outcomes compared with those of rats treated with rtPA alone. Moreover, IM-12 increased the protein expression of β-catenin and downstream proteins while suppressing the expression of GSK-3β. These results suggest that IM-12 reduces rtPA-induced HT and attenuates BBB disruption, possibly through activation of the Wnt-β-catenin signaling pathway, and provides a potential therapeutic strategy for preventing tPA-induced HT after AIS.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".