DNA Microarray Analysis of Hippocampal Gene Expression Measured Twelve Hours after Hypoxia-Ischemia in the Mouse
Bibliographic record
Abstract
Cell death from cerebral ischemia is a dynamic process. In the minutes to days after an ischemic insult, progressive changes in cellular morphology occur. Associated with these events is the regulation of competing programs of gene expression; some are protective against ischemic insult, and others contribute to delayed cell death. Many genes involved in these processes have been identified, but individually, these findings have provided only limited insight into the systems biology of cerebral ischemia. Attempts to characterize the coordinated expression of large numbers of genes in cerebral ischemia has only recently become possible. Today, DNA microarray technology provides a powerful tool for investigating parallel expression changes for thousands of genes at one time. In this study, adult mice were subjected to 30 minutes of hypoxia-ischemia (HI), and the hippocampus was examined 12 hours later for differential gene expression using a 15K high-density mouse EST array. The genomic response to HI is complex, affecting approximately 7% of the total number of ESTs examined. Assigning differentially expressed ESTs to molecular functional groups revealed that HI affects many pathways including the molecular chaperones, transcription factors, kinases, and calcium ion binding genes. A comprehensive list of regulated genes should prove valuable in advancing our understanding of the pathogenesis of cerebral ischemia.
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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".