Microarray gene expression profiles in dilated and hypertrophic cardiomyopathic end-stage heart failure
Bibliographic record
Abstract
Despite similar clinical endpoints, heart failure resulting from dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM) appears to develop through different remodeling and molecular pathways. Current understanding of heart failure has been facilitated by microarray technology. We constructed an in-house spotted cDNA microarray using 10,272 unique clones from various cardiovascular cDNA libraries sequenced and annotated in our laboratory. RNA samples were obtained from left ventricular tissues of precardiac transplantation DCM and HCM patients and were hybridized against normal adult heart reference RNA. After filtering, differentially expressed genes were determined using novel analyzing software. We demonstrated that normalization for cDNA microarray data is slide-dependent and nonlinear. The feasibility of this model was validated by quantitative real-time reverse transcription-PCR, and the accuracy rate depended on the fold change and statistical significance level. Our results showed that 192 genes were highly expressed in both DCM and HCM (e.g., atrial natriuretic peptide, CD59, decorin, elongation factor 2, and heat shock protein 90), and 51 genes were downregulated in both conditions (e.g., elastin, sarcoplasmic/endoplasmic reticulum Ca2+-ATPase). We also identified several genes differentially expressed between DCM and HCM (e.g., alphaB-crystallin, antagonizer of myc transcriptional activity, beta-dystrobrevin, calsequestrin, lipocortin, and lumican). Microarray technology provides us with a genomic approach to explore the genetic markers and molecular mechanisms leading to heart failure.
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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 | 0.001 |
| 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".