Apoptosis-related genes expressed in cardiovascular development and disease: an EST approach
Bibliographic record
Abstract
Apoptosis (programmed cell death) is an important process which, in conjunction with cell proliferation, maintains cell number homeostasis. Although apoptosis has been more extensively investigated in other tissues [1,2], only recently has this process been suspected as a significant contributor to both disease and normal development of the cardiovascular system [3-6]. Grasping a comprehension of the underlying genetic mechanisms of apoptosis is especially crucial considering that cardiac myocytes irreversibly exit the cell cycle and thus fail to proliferate during pathological conditions. Despite great strides in understanding the molecular pathways of apoptosis, there still remain numerous questions to be answered. Identifying key genes that are involved in the regulatory process of apoptosis in the cardiovascular system will serve as a basis for creating more effective therapeutic treatments in cardiovascular disease and provide an understanding of how cardiac development is modulated. This review provides a brief summary of recent data implicating genes that may be involved in apoptosis in the cardiovascular system. It also outlines the continued usefulness of large-scale generation of expressed sequence tags (ESTs) to establish expression profiles from the cardiovascular system and as a means of identifying potentially significant apoptotic regulators previously characterized in other tissues but not as yet in the cardiovascular system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".