Neuroproteome Changes after Ischemia/Reperfusion Injury and Tissue Plasminogen Activator Administration in Rats: A Quantitative iTRAQ Proteomics Study
Bibliographic record
Abstract
The thrombolytic, recombinant tissue plasminogen activator (rt-PA) is the only approved therapy for acute ischemic stroke (AIS). When administered after AIS, rt-PA has many adverse pleiotropic actions, which are currently poorly understood. The identification of proteins showing differential expression after rt-PA administration may provide insight into these pleiotropic actions. In this study we used a 2D-LC MS/MS iTRAQ proteomic analysis, western blotting, and pathway analysis to analyze changes in protein expression 24-hours after rt-PA administration in the cortical brain tissue of 36 rats that underwent a sham or transient middle cerebral artery occlusion surgery. After rt-PA administration we reported alterations in the expressions of 18 proteins, many of which were involved in excitatory neurotransmitter function or cytoskeletal structure. The expression changes of GAD2 and EAAT1 were validated with western blot. The interactions between the identified proteins were analyzed with the IPA pathway analysis tool and three proteins: DPYSL2, RTN4, and the NF-kB complex, were found to have characteristics of being key proteins in the network. The differential protein expressions we observed may reflect pleiotropic actions of rt-PA after experimental stroke, and shine light on the mechanisms of rt-PA's adverse effects. This may have important implications in clinical settings where thrombolytic therapy is used to treat 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".