Trigger-dependent Gene Expression Profiles in Cardiac Preconditioning
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Bibliographic record
Abstract
BACKGROUND: DNA chips facilitate genomic-wide exploration of gene expression. The authors hypothesized that ischemic (IPC) and anesthetic preconditioning (APC) would differentially modulate gene expression in hearts. METHODS: Affymetrix rat U34A gene chips were used to explore the transcriptional response to IPC and APC, sustained ischemia (110 min) without reperfusion, and time-matched perfusion in isolated rat hearts. IPC was induced by three cycles of 5 min of ischemia, and APC was induced by 1.5 minimum alveolar concentration isoflurane (110 min). For each heart, a separate chip was used for hybridization. Data were analyzed for significant > or = 2.0-fold changes in gene expression. Microarray results were confirmed by quantitative real-time reverse-transcription polymerase chain reaction. RESULTS: Of the 8,799 genes represented on U34A, 217 transcripts in the APC group, 234 in the IPC group, and 29 in the ischemia group displayed significant > or = 2.0-fold up-regulation in messenger RNA levels, and 185 transcripts in the APC group, 55 in the IPC group, and 49 in the ischemia group displayed significant > or = 2.0-fold down-regulation. Many of these transcripts were unknown genes. A high number of commonly regulated genes were found in IPC and APC (39 up-regulated, 17 down-regulated). Genes commonly regulated included those associated with cell defense (heat shock protein 10, aldose reductase, Bcl-xS). Conversely, a pool of protective and antiprotective genes was differentially regulated in APC versus IPC (heat shock protein 27/70, programmed cell death 8), suggesting trigger-dependent transcriptome variability. CONCLUSIONS: The novel microarray technology provides evidence for distinct cardioprotective phenotypes in IPC and APC. The observed transcriptional changes raise the possibility of a second window of protection by volatile anesthetics. The authors' molecular portraits are the first global genomic comparison between IPC and APC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it