Mir-33: miR-acles in cardiac fibrosis?
Bibliographic record
Abstract
Heart diseases (HDs) are the primary cause of death in the world (1). An estimated 17.3 million people died from HDs in 2015 (1), representing about 30% of all global deaths. Heart failure is the clinical manifestation of numerous forms of HDs. It is a destructive disorder characterized by ventricular remodeling and reduced compliance. In nearly all etiologies of HDs, the progression toward failure is accelerated by fibrosis, i.e., the improper deposition of extracellular matrix (ECM) proteins by cardiac fibroblasts (CFs) resulting in the reduction of tissue compliance. Physiologically, fibroblasts are the major cell type implicated in the construction and maintenance of connective tissue. The ECM, a highly organized collagenrich meshwork, provides a structural and flexible scaffold for cardiac cells populations, dispenses mechanical forces through the myocardium, and mediates mechanical conduction of cells in the environment (2-4). CFs are fundamentally involved into the heart response to injury and tackle the limited regenerative capacity of the heart after injury. Fibrotic scar tissues preserve cardiac tissue structure and function. Upon injury, CFs within the connective tissue are activated, and secrete high levels of ECM to generate a pro-fibrotic environment. This environment enhances stiffness of the cardiac tissue and inhibits ventricular contraction and relaxation, which lead to abnormal heart architecture and function. Excessive ECM deposition and fibrosis have been clearly associated with myocardial diastolic and systolic dysfunctions (5). Inhibiting or reversing fibrosis and its damaging repercussions is an established strategy used in many clinical interventions aiming to treat HDs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".