Cutting the molecular brakes to achieve cardiac regeneration
Bibliographic record
Abstract
It has been estimated that nearly 1 billion cardiomyocytes (CM) die in a typical acute myocardial infarction (MI). There is an ongoing search for safe, reliable, sufficient replacement strategy for the lost CM to restore functional mass. All organs possess some regenerative capacity but in a spectrum of ability. In some organs, this is an integral part of their continuous physiological self-renewal (e.g., blood, skin, gut). In other organs, a mixture of cell-cycle re-entry, hyperplasia, differentiation, and hypertrophy compensate for lost cell mass (e.g., liver, skeletal muscle). In the adult heart, regeneration is currently inconsequential after injury. In the absence of CM renewal, remodeling is the default (eventually leading to decompensatory heart failure). The intense proliferation of fibroblasts and scarring secure the cardiac structure but at the risk of diastolic dysfunction, dilatation, or arrhythmia on top of systolic impairment from lost contractile tissue. Fibrotic remodelling also occurs in the peri-infarct and, to a lesser degree, in remote regions of the heart. Further, the residual CM undergo concentric hypertrophy as they adapt to the increased workload leftover by lost CM, a process necessary to restore cardiac output towards normal levels. The “holy-grail” of therapy would be to survive this redistribution of hemodynamic load after injury and outrace scar formation with functional contractile capacity. A race regrettably lost far too often in the clinic.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.058 | 0.059 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".