C.02: Whole genome expression profiling of blood-brain barrier endothelial cells after experimental subarachnoid hemorrhage
Bibliographic record
Abstract
Background: The pathophysiology of subarachnoid hemorrhage (SAH) is complex and includes disruption of the blood-brain barrier (BBB). We freshly isolated BBB endothelial cells (BECs) by 2 distinct methods after experimental SAH and then interrogated their gene expression profiles with the goal of uncovering new therapeutic targets. Methods: SAH was induced using the prechiasmatic blood injection mouse model. BBB permeability studies were performed by administering intraperitoneal cadaverine dye injections at 24h and 48h. BECs were isolated either by sequential magnetic-based sorting for CD45-CD31+ cells or by fluorescence-activated cell sorting (FACS) for Tie2+Pdgfrb- cells. Total RNA was extracted and analyzed using Affymetrix Mouse Gene 2.0 ST Arrays. Results: BBB impairment occurred at 24h and resolved by 48h after SAH. Analysis of gene expression patterns in BECs at 24h reveal clustering of SAH and sham samples. We identified 707 (2.8%) significant differentially-expressed genes (403 upregulated, 304 downregulated) out of 24,865 interrogated probe sets. Many significantly upregulated genes were involved in inflammatory pathways. These microarray results were validated with real-time polymerase chain reaction (RT-PCR). Conclusions: This study is the first to investigate in an unbiased manner, whole genome expression profiling of freshly-isolated BECs in an SAH animal model, yielding targets for novel therapeutic intervention.
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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) | 0.003 | 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".