Signalling in cardiac disease: the molecular deficit at the heart of the problem
Bibliographic record
Abstract
See article by Yoshida et al. [3] (pages 34–45) in this issue. Events leading to heart failure are characterized by a prolonged action potential and a deficit in cardiac performance which is paralleled by ionic remodelling and a loss of contractile function at the level of the single cardiomyocyte (see Ref. [1] for review). Increased sympathetic stimulation as well as a number of paracrine and autocrine factors lead to cardiac hypertrophy and an eventual decompensated failing phenotype [2]. However, several issues remain to be resolved in terms of a molecular mechanism for these events. First, is there a deficit in the contractile apparatus of the failing myocyte per se? Alternatively, are signalling pathways which modulate contractility and are known to be altered in the failing heart the trigger for these events? Further, the focus of work on whole cardiac tissue to date has not resolved the role of non-myocyte cells in the disease process. Finally, the subcellular localization and compartmentalization of signalling pathways and the proteins they modulate may be of critical importance as well. The study by Yoshida et al. [3] in this issue of Cardiovascular Research begins to address some of these important issues. The authors demonstrate that although contractile function is clearly maintained in isolated cardiomyocytes from animals with coronary artery ligation-induced congestive heart failure (CHF), their regulation by the β-adrenergic system is significantly altered resolving a dilemma in the literature about the actual deficit in failing hearts. Further, they show alterations in levels of Gsα but not Giα subunits in cardiomyocytes, while in non-myocytes there are significant alterations in both subtypes. These results are important as they address the actual site of the molecular deficit in cardiac tissue.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".