Characterization of vascular protein expression patterns in cerebral ischemia/reperfusion using laser capture microdissection and ICAT‐nanoLC‐MS/MS
Bibliographic record
Abstract
Cerebral ischemia rapidly initiates structural and functional changes in brain vessels, including blood-brain barrier disruption, inflammation, and angiogenesis. Molecular events that accompany these changes were investigated in brain microvessels extracted using laser-capture microdissection (LCM) from Sprague-Dawley rats subjected to a 20 min transient global cerebral ischemia followed by 1, 6, or 24 h reperfusion. Proteins extracted from approximately 300 LCM captured microvessels (20-100 microm) were ICAT-labeled and analyzed by nanoLC-MS. In-house software was used to identify paired ICAT peaks, which were then sequenced by nanoLC-MS/MS. Pattern analyses using k-means clustering method classified 57 differentially expressed proteins in 7 distinct dynamic patterns. Protein function was assigned using Panther Classification system. Early reperfusion (1 h) was characterized by down-regulation of ion pumps, nutrient transporters, and cell structure/motility proteins, and up-regulation of transcription factors, signal transduction molecules and proteins involved in carbohydrate metabolism. The up-regulation of inflammatory cytokines and proteins involved in the extracellular matrix remodeling and anti-oxidative defense was observed in late reperfusion (6-24 h). The up-regulation of IL-1beta and TGF-1beta in ischemic brain vessels was confirmed by ELISA, quantitative PCR, and/or immunohistochemistry. A biphasic postischemic (1 and 24 h) BBB opening for (3)H-sucrose was evident in the same model. Differentially expressed proteins identified in brain vessels during reperfusion are likely involved in orchestrating functional vascular responses to ischemia, including the observed BBB disruption.
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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 | 0.001 | 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 | 0.000 |
| 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".