Trigger-dependent Gene Expression Profiles in Cardiac Preconditioning
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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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 | 0.000 | 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) | 0.001 | 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".