Guanosine protects against reperfusion injury in rat brains after ischemic stroke
Bibliographic record
Abstract
After ischemic stroke, early thrombolytic therapy to reestablish tissue perfusion improves outcome but triggers a cascade of deleterious cellular and molecular events. Using a collaborative approach, our groups examined the effects of guanosine (Guo) in response to ischemic reperfusion injury in vitro and in vivo. In a transient middle cerebral artery occlusion (MCAO) in rats, Guo significantly reduced infarct volume in a dose-dependent manner when given systemically either immediately before or 30 min, but not 60 min, after the onset of the 5.5-hr reperfusion period. In a separate experiment, Guo significantly reduced infarct volume after 24 hr of reperfusion when administered 5 min before reperfusion. Western blot analysis did not reveal any significant changes either in endoplasmic reticulum (ER) stress proteins (GRP 78 and 94) or HSP 70 or in levels of m-calpain. In vitro oxygen and glucose deprivation (OGD) significantly increased production of both reactive oxygen species (ROS) and interleukin-8 (IL-8) in the primary astrocytes. Guo did not alter ROS or IL-8 production when given to the astrocytes before OGD. However, Guo when added to the cells prior to or 30 min after reperfusion significantly reduced IL-8 release but not ROS formation. Our study revealed a dose- and time-dependent protective effect of Guo on reperfusion injury in vitro and vivo. The mechanisms by which Guo exerts its effect are independent of unfolded proteins in ER or the level of intracellular calcium or ROS formation. However, the effect may be induced, at least partially, by inhibiting IL-8, a marker of reperfusion-triggered proinflammatory events.
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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 | 0.000 | 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".