Altered Association of Protein Tyrosine Kinases with Postsynaptic Densities after Transient Cerebral Ischemia in the Rat Brain
Bibliographic record
Abstract
Transient cerebral ischemia results in an increase in the tyrosine phosphorylation of proteins associated with postsynaptic densities (PSDs). The authors investigated the possible mechanisms behind this increase by analyzing isolated PSDs for protein tyrosine kinase activity and for the presence of specific tyrosine kinases. Transient (15 minutes) global ischemia was produced in adult rats by four-vessel occlusion, and PSDs were isolated immediately after ischemia or after 20 minutes or 6 hours of reperfusion. Tyrosine phosphorylation of several PSD proteins, including the N-methyl-D-aspartate (NMDA) receptor subunits NR2A and NR2B, was enhanced relative to shams after 20 minutes of reperfusion and underwent a further increase between 20 minutes and 6 hours. The ability of intrinsic PSD tyrosine kinase to phosphorylate PSD proteins, including the NMDA receptor, increased threefold after ischemia. Whereas PSD-associated proline-rich tyrosine kinase 2 (PYK2) and gp145TrkB were elevated immediately after the ischemic event, increases in Src and Fyn were not apparent until 6 hours of reperfusion. The level of PSD-associated pp125FAK decreased after ischemia. The results demonstrate that ischemia results in selective changes in the association of protein tyrosine kinases with the PSD which may account for ischemia-induced increases in the tyrosine phosphorylation of PSD proteins.
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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) | 0.000 | 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".