Signaling cascades in the failing heart and emerging therapeutic strategies
Bibliographic record
Abstract
Chronic heart failure is the end stage of cardiac diseases. With a high prevalence and a high mortality rate worldwide, chronic heart failure is one of the heaviest health-related burdens. In addition to the standard neurohormonal blockade therapy, several medications have been developed for chronic heart failure treatment, but the population-wide improvement in chronic heart failure prognosis over time has been modest, and novel therapies are still needed. Mechanistic discovery and technical innovation are powerful driving forces for therapeutic development. On the one hand, the past decades have witnessed great progress in understanding the mechanism of chronic heart failure. It is now known that chronic heart failure is not only a matter involving cardiomyocytes. Instead, chronic heart failure involves numerous signaling pathways in noncardiomyocytes, including fibroblasts, immune cells, vascular cells, and lymphatic endothelial cells, and crosstalk among these cells. The complex regulatory network includes protein-protein, protein-RNA, and RNA-RNA interactions. These achievements in mechanistic studies provide novel insights for future therapeutic targets. On the other hand, with the development of modern biological techniques, targeting a protein pharmacologically is no longer the sole option for treating chronic heart failure. Gene therapy can directly manipulate the expression level of genes; gene editing techniques provide hope for curing hereditary cardiomyopathy; cell therapy aims to replace dysfunctional cardiomyocytes; and xenotransplantation may solve the problem of donor heart shortages. In this paper, we reviewed these two aspects in the field of failing heart signaling cascades and emerging therapeutic strategies based on modern biological techniques.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".