Inhibition of protein kinase C reduces ischemia‐induced tyrosine phosphorylation of the <i>N</i>‐methyl‐<scp>d</scp>‐aspartate receptor
Bibliographic record
Abstract
The role of protein kinase C (PKC) in tyrosine phosphorylation of the N-methyl-d-aspartate receptor (NMDAR) following transient cerebral ischemia was investigated. Transient (15 min) cerebral ischemia was produced in adult rats by four-vessel occlusion and animals allowed to recover for 15 or 45 min. Following ischemia, tyrosine phosphorylation of NR2A and NR2B and activated Src-family kinases (SFKs) and Pyk2 were increased in post-synaptic densities (PSDs). Phosphorylation of NR2B on Y1472 by PSDs isolated from post-ischemic forebrains was inhibited by the SFK specific inhibitor PP2, and by the PKC inhibitors GF109203X (GF), Gö6976 and calphostin C. Intravenous injection of GF immediately following the ischemic challenge resulted in decreased phosphorylation of NR1 on PKC phosphorylation sites and reduced ischemia-induced increases in tyrosine phosphorylation of NR2A and NR2B without affecting the increase in total tyrosine phosphorylation of hippocampal proteins. Ischemia-induced increases in activated Pyk2 and SFKs in PSDs, but not the translocation of PKC, Pyk2 or Src to the PSD, were also inhibited by GF. The inactive homologue of GF, bisindolylmaleimide V, had no effect on these parameters. The results are consistent with a role for PKC in the ischemia-induced increase in tyrosine phosphorylation of the NMDAR, via a pathway involving Pyk2 and Src-family kinases.
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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) | 0.000 | 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".