Bibliographic record
Abstract
The aim of this chapter is to introduce the role of miRNAs in the pathological process of cardiac hypertrophy/heart failure. Cardiovascular disease is among the main causes of morbidity and mortality in developed countries. In response to stress, the adult heart undergoes remodelling process and hypertrophic growth to adapt to altered workloads and to compensate for the impaired cardiac function. Pathological hypertrophy results in loss of cardiac function and is the major predictor of heart failure and sudden death. Recent studies have established the role of miRNAs in cardiac hypertrophy/heart failure as causal factors or important regulators. miRNAs are aberrantly expressed in various animal models and in patients with heart failure. The miRNAs involved in cardiac hypertrophy/heart failure can in general be divided into two categories: anti-hypertrophic and pro-hypertrophic miRNAs. This chapter introduces the roles of miRNAs in experimental and clinical cardiac hypertrophy/heart failure. Detailed description is given of the well-studied pro-hypertrophic miRNAs miR-195, miR-208 and miR-23a, and anti-hypertrophic miRNAs miR-1, miR-133 and miR-9.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".