Crosstalk between myocardial autophagy and sterile inflammation in the development of heart failure
Bibliographic record
Abstract
Heart failure, a leading driver of global mortality, remains a topic of intense contemporary research interest due to the prevailing unmet need in cardiometabolic therapeutics. Numerous mechanisms with the potential to influence the onset and development of heart failure remain incompletely understood. Firstly, myocardial autophagy, which involves lysosomal degradation of damaged cellular components, confers context-dependent beneficial and detrimental effects. Secondly, sterile inflammation may arise following cardiac stress and exacerbate the progression of heart failure. Inflammation changes in a temporal manner and its onset must be adequately resolved to limit progression of heart failure. Mitochondria are an important factor in contributing to sterile inflammation by releasing damage associated molecular patterns (DAMPs) including mitochondrial DNA (mtDNA). Accordingly, this is one reason why the selective autophagy of mitochondria to maintain optimal function is important in determining cardiac function. In this review, we examine the increasing evidence suggesting crosstalk between autophagy and sterile inflammation together with their role in the development of heart failure. In particular, this is exemplified in the preclinical models of ischaemia/reperfusion injury and pressure overload induced heart failure. We also highlight potential therapeutic approaches focusing on autophagy and addressing sterile inflammation, aiming to enhance outcomes in heart failure.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".