Crosstalk between the heart and peripheral organs in heart failure
Bibliographic record
Abstract
Mediators from peripheral tissues can influence the development and progression of heart failure (HF). For example, in obesity, an altered profile of adipokines secreted from adipose tissue increases the incidence of myocardial infarction (MI). Less appreciated is that heart remodeling releases cardiokines, which can strongly impact various peripheral tissues. Inflammation, and, in particular, activation of the nucleotide-binding oligomerization domain-like receptors with pyrin domain (NLRP3) inflammasome are likely to have a central role in cardiac remodeling and mediating crosstalk with other organs. Activation of the NLRP3 inflammasome in response to cardiac injury induces the production and secretion of the inflammatory cytokines interleukin (IL)-1β and IL-18. In addition to having local effects in the myocardium, these pro-inflammatory cytokines are released into circulation and cause remodeling in the spleen, kidney, skeletal muscle and adipose tissue. The collective effects of various cardiokines on peripheral organs depend on the degree and duration of myocardial injury, with systematic inflammation and peripheral tissue damage observed as HF progresses. In this article, we review mechanisms regulating myocardial inflammation in HF and the role of factors secreted by the heart in communication with peripheral tissues. Heart failure triggers an inflammatory chain reaction that can severely damage various organ systems throughout the body. During a heart attack, the initial deprivation and subsequent abrupt return of oxygen triggers considerable structural and functional changes in the heart. Gary Sweeney and colleagues at York University, Toronto, Canada, have reviewed contemporary research into the physiological impact of heart failure. A protein complex including NLRP3 plays a prominent role in this process, stimulating production of cellular factors that promote inflammatory tissue damage. In addition to local effects on the heart, these factors circulate elsewhere, resulting in functional impairment in the kidney, spleen and skeletal muscle. Conversely, disorders like chronic kidney disease can lead to worse outcomes after a heart attack. Therapies that selectively neutralize these inflammatory factors could limit the damage and facilitate patient recovery.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".