Abstract Background Progressive remodelling of the left ventricle (LV) following myocardial infarction (MI) is an outcome of spatial-temporal cellular interactions among different cell types that leads to heart failure for a significant number of patients. Cellular populations demonstrate temporal profiles of flux post-MI. However, little is known about the relationship between cell populations and the interaction strength among cells post-MI. The objective of this study was to establish a conceptual cellular interaction model based on a recently established graph network to describe the interaction between two types of cells. Results We performed stability analysis to investigate the effects of the interaction strengths, the initial status, and the number of links between cells on the cellular population in the dynamic network. Our analysis generated a set of conditions on interaction strength, structure of the network, and initial status of the network to predict the evolutionary profiles of the network. Computer simulations of our conceptual model verified our analysis. Conclusions Our study introduces a dynamic network to model cellular interactions between two different cell types which can be used to model the cellular population changes post-MI. The results on stability analysis can be used as a tool to predict the responses of particular cell populations.

Optimal healing of damaged tissue following myocardial infarction (MI) requires a coordinated cellular response that can be divided into three phases: inflammatory, proliferative/reparative, and maturation. The inflammatory phase, characterized by rapid influx of cytokines, chemokines, and immune cells, is critical to the removal of damaged tissue. The onset of the proliferative/reparative phase is marked by increased proliferation of myofibroblasts and secretion of collagen to replace dead tissue. Lastly, crosslinking of collagen fibers and apoptosis of immune cells marks the maturation phase. Excessive inflammation or fibrosis has been linked to increased incidence of arrhythmia and other MI-related pathologies. This review describes the roles of inflammation and fibrosis in arrhythmogenesis and prospective therapies for anti-arrhythmic treatment.

Background

Interleukin 1β (IL-1β) is a key regulator of the inflammatory response after myocardial infarction (MI) by modulating immune cell recruitment, cytokine production, and extracellular matrix turnover. Elevated levels of IL-1β are associated with adverse remodeling, and inhibition of IL-1 signaling after MI results in improved contractile function.

Objective

The goal of this study was to determine whether IL-1 signaling also contributes to post-MI arrhythmogenesis.

Methods

MI was created in 2 murine models of elevated inflammation: atherosclerotic on the Western diet or wild-type with a subseptic dose of lipopolysaccharide. The role of IL-1β was assessed with the IL-1 receptor antagonist anakinra (10 mg/(kg·d), starting 24 hours post-MI).

Results

In vivo and ex vivo molecular imaging showed reduced myocardial inflammation after a 4-day course of anakinra treatment, despite no change in infarct size. At day 5 post-MI, high-speed optical mapping of transmembrane potential and intracellular Ca²⁺ in isolated hearts revealed that IL-1β inhibition improved conduction velocity, reduced action potential duration dispersion, improved intracellular Ca²⁺ handling, decreased transmembrane potential and Ca²⁺ alternans magnitude, and reduced spontaneous and inducible ventricular arrhythmias. These functional improvements were linked to increased expression of connexin 43 and sarcoplasmic reticulum Ca²⁺-ATPase.

Conclusion

This study revealed a novel mechanism for IL-1β in contributing to defective excitation-contraction coupling and arrhythmogenesis in the post-MI heart. Our results suggest that inhibition of IL-1 signaling post-MI may represent a novel antiarrhythmic therapy.

Background

Atherosclerotic animal models show increased recruitment of inflammatory cells to the heart after myocardial infarction (MI), which impacts ventricular function and remodeling.

Objective

The purpose of this study was to determine whether increased myocardial inflammation after MI also contributes to arrhythmias.

Methods

MI was created in 3 mouse models: (1) atherosclerotic (apolipoprotein E deficient [ApoE(-/-)] on atherogenic diet, n = 12); (2) acute inflammation (wild-type [WT] given daily lipopolysaccharide [LPS] 10 μg/day, n = 7); and (3) WT (n = 14). Sham-operated (n = 4) mice also were studied. Four days post-MI, an inflammatory protease-activatable fluorescent probe (Prosense680) was injected intravenously to quantify myocardial inflammation on day 5. Optical mapping with voltage-sensitive dye was performed on day 5 to assess electrophysiology and arrhythmia susceptibility.

Results

Inflammatory activity (Prosense680 fluorescence) was increased approximately 2-fold in ApoE+MI and LPS+MI hearts vs WT+MI (P<.05) and 3-fold vs sham (P<.05). ApoE+MI and LPS+MI hearts also had prolonged action potential duration, slowed conduction velocity, and increased susceptibility to pacing-induced arrhythmias (56% and 71% vs 13% for WT+MI and 0% for sham, respectively, P<.05, for ApoE+MI and LPS+MI groups vs both WT+MI and sham). Increased macrophage accumulation in ApoE+MI and LPS+MI hearts was confirmed by immunofluorescence. Macrophages were associated with areas of connexin43 (Cx43) degradation, and a 2-fold decrease in Cx43 expression was found in ApoE+MI vs WT+MI hearts (P<.05). ApoE+MI hearts also had a 3-fold increase in interleukin-1β expression, an inflammatory cytokine known to degrade Cx43.

Conclusion

Underlying atherosclerosis exacerbates post-MI electrophysiological remodeling and arrhythmias. LPS+MI hearts fully recapitulate the atherosclerotic phenotype, suggesting myocardial inflammation as a key contributor to post-MI arrhythmia.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.