Intracortical injection of endothelin-1 induces cortical infarcts in mice: effect of neuronal expression of an adenosine transporter
Bibliographic record
Abstract
BACKGROUND: Activation of adenosine A1 receptors has neuroprotective effects in animal stroke models. Adenosine levels are regulated by nucleoside transporters. In vitro studies showed that neuron-specific expression of human equilibrative nucleoside transporter 1 (hENT1) decreases extracellular adenosine levels and adenosine A1 receptor activity. In this study, we tested the effect of hENT1 expression on cortical infarct size following intracerebral injection of the vasoconstrictor endothelin-1 (ET-1) or saline. METHODS: Mice underwent stereotaxic intracortical injection of ET-1 (1 μl; 400 pmol) or saline (1 μl). Some mice received the adenosine receptor antagonist caffeine (25 mg/kg, intraperitoneal) 30 minutes prior to ET-1. Perfusion and T2-weighted magnetic resonance imaging (MRI) were used to measure cerebral blood flow (CBF) and subsequent infarct size, respectively. RESULTS: ET-1 reduced CBF at the injection site to 7.3 ± 1.3% (n = 12) in hENT1 transgenic (Tg) and 12.5 ± 2.0% (n = 13) in wild type (Wt) mice. At 48 hours following ET-1 injection, CBF was partially restored to 35.8 ± 4.5% in Tg and to 45.2 ± 6.3% in Wt mice; infarct sizes were significantly greater in Tg (9 ± 1.1 mm3) than Wt (5.4 ± 0.8 mm3) mice. Saline-treated Tg and Wt mice had modest decreases in CBF and infarcts were less than 1 mm3. For mice treated with caffeine, CBF values and infarct sizes were not significantly different between Tg and Wt mice. CONCLUSIONS: ET-1 produced greater ischemic injury in hENT1 Tg than in Wt mice. This genotype difference was not observed in mice that had received caffeine. These data indicate that hENT1 Tg mice have reduced ischemia-evoked increases in adenosine receptor activity compared to Wt mice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".