Time-Warped Comparison of Gene Expression in Adaptive and Maladaptive Cardiac Hypertrophy
Bibliographic record
Abstract
BACKGROUND: Cardiac hypertrophy is classically regarded as a compensatory response, yet the active tissue remodeling processes triggered by various types of mechanical stress can enhance or diminish the function of the heart. Despite the disparity in outcomes, there are similarities in the hypertrophic responses. We hypothesized that a generic genetic response that is not dependent on the particular nature of the hypertrophic stimulus exists. To test our hypothesis, we compared the temporal evolution of transcriptomes measured in hearts subjected to either adaptive (exercise-induced) or maladaptive (aortic banding-induced) hypertrophy. METHODS AND RESULTS: Generic hypertrophy-associated genes were identified and distinguished from stimulus-dependent transcripts by coupling a metric of cardiac growth with a dynamic time-warping algorithm to align transcriptome changes with respect to the hypertrophy response. The major differences in expression between the adaptive and maladaptive hypertrophy models were centered around the genes involved in metabolism, fibrosis, and immune response. Conversely, transcripts with common expression patterns in both hypertrophy models were associated with signal transduction, cytoskeletal development, and muscle contraction. Thus, despite the apparent differences in the expression response of the heart to either athletic conditioning or pressure overload, there is a set of genes that displays similar expression profiles. CONCLUSIONS: This finding lends support to the notion of a generalized cardiac growth mechanism that is activated in response to mechanical perturbation. The common and unique genetic signatures of adaptive and maladaptive hypertrophy may be useful in the diagnosis and treatment of pathological myocardial remodeling.
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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 | 0.001 | 0.001 |
| 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".