Background: Cardiovascular risk in diabetes remains elevated despite glucose lowering therapies. We hypothesised that hyperglycaemia induces trained immunity in macrophages, promoting persistent pro-atherogenic characteristics. Methods: Bone marrow derived macrophages from control and mice with diabetes were grown in physiological glucose (5 mM) and subject to RNA-sequencing (n=6), ATAC-sequencing (n=6) and ChIP-sequencing (n=6) for determination of hyperglycaemia-induced trained immunity. Bone marrow transplantation from mice with (n=9) or without (n=6) diabetes into [normoglycaemic] Ldlr -/- mice was used to assess its functional significance in vivo . Evidence of hyperglycaemia-induced trained immunity was sought in human peripheral blood mononuclear cells (PBMCs) from patients with diabetes (n=8) compared with case controls (n=16) and in human atherosclerotic plaque macrophages excised by laser capture microdissection. Results: In macrophages, high extracellular glucose promoted pro-inflammatory gene expression and pro-atherogenic functional characteristics, through glycolysis-dependent mechanisms. Bone marrow-derived macrophages (BMDM) from diabetic mice, retained these characteristics, even when cultured in physiological glucose, indicating hyperglycaemia-induced trained immunity. Bone marrow transplantation from diabetic mice into [normoglycaemic] Ldlr -/- mice increased aortic root atherosclerosis, confirming a disease-relevant and persistent form of trained innate immunity. Integrated ATAC-seq, ChIP-seq and RNA-seq analyses of haematopoietic stem cells and BMDM revealed a pro-inflammatory "priming effect" in diabetes. The pattern of open chromatin implicated transcription factor, RUNX1, while transcriptomes of atherosclerotic plaque macrophages and peripheral leukocytes in patients with type 2 diabetes were enriched for RUNX1 targets, consistent with a potential role in human disease. Pharmacological inhibition of RUNX1 in vitro inhibited the trained phenotype. Conclusions: Hyperglycaemia-induced trained immunity may explain why targeting elevated glucose is ineffective in reducing macrovascular risk in diabetes and suggests new targets for disease prevention and therapy.

Transcriptionally activated monocytes are recruited to the heart after acute myocardial infarction (AMI). After AMI in mice and humans, the number of extracellular vesicles (EVs) increased acutely. In humans, EV number correlated closely with the extent of myocardial injury. We hypothesized that EVs mediate splenic monocyte mobilization and program transcription following AMI. Some plasma EVs bear endothelial cell (EC) integrins, and both proinflammatory stimulation of ECs and AMI significantly increased VCAM-1–positive EV release. Injected EC-EVs localized to the spleen and interacted with, and mobilized, splenic monocytes in otherwise naive, healthy animals. Analysis of human plasma EV-associated miRNA showed 12 markedly enriched miRNAs after AMI; functional enrichment analyses identified 1,869 putative mRNA targets, which regulate relevant cellular functions (e.g., proliferation and cell movement). Furthermore, gene ontology termed positive chemotaxis as the most enriched pathway for the miRNA-mRNA targets. Among the identified EV miRNAs, EC-associated miRNA-126-3p and -5p were highly regulated after AMI. miRNA-126-3p and -5p regulate cell adhesion– and chemotaxis-associated genes, including the negative regulator of cell motility, plexin-B2. EC-EV exposure significantly downregulated plexin-B2 mRNA in monocytes and upregulated motility integrin ITGB2. These findings identify EVs as a possible novel signaling pathway by linking ischemic myocardium with monocyte mobilization and transcriptional activation following AMI.

High basophil allergen CD63 sensitivity phenotype was a major indicator of severe adverse SRs during the build-up phase of honeybee VIT. Possibly role was also showed for short latency to filed sting reaction and low sIgE to rApi m1. Before honeybee VIT, measurement of basophil allergen sensitivity should be used to identify patients with a high risk for severe side-effects.

Although the initiation, development, and maintenance of atrial fibrillation (AF) have been linked to alterations in myocyte redox state, the field lacks a complete understanding of the impact these changes may have on cellular signalling, atrial electrophysiology, and disease progression. Recent studies demonstrate spatiotemporal changes in reactive oxygen species production shortly after the induction of AF in animal models with an uncoupling of nitric oxide synthase activity ensuing in the presence of long-standing persistent AF, ultimately leading to a major shift in nitroso–redox balance. However, it remains unclear which radical or non-radical species are primarily involved in the underlying mechanisms of AF or which proteins are targeted for redox modification. In most instances, only free radical oxygen species have been assessed; yet evidence from the redox signalling field suggests that non-radical species are more likely to regulate cellular processes. A wider appreciation for the distinction of these species and how both species may be involved in the development and maintenance of AF could impact treatment strategies. In this review, we summarize how redox second-messenger systems are regulated and discuss the recent evidence for alterations in redox regulation in the atrial myocardium in the presence of AF, while identifying some critical missing links. We also examine studies looking at antioxidants for the prevention and treatment of AF and propose alternative redox targets that may serve as superior therapeutic options for the treatment of AF.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia, with an estimated prevalence of around 1.6% in the adult population. The analysis of the Electrocardiogram (ECG) data acquired in the UK Biobank represents an opportunity to screen for AF in a large sub-population in the UK.The main objective of this paper is to assess ten machine-learning methods for automated detection of subjects with AF in the UK Biobank dataset. Six classical machine-learning methods based on Support Vector Machines are proposed and compared with state-of-the-art techniques (including a deep-learning algorithm), and finally a combination of a classical machine-learning and deep learning approaches. Evaluation is carried out on a subset of the UK Biobank dataset, manually annotated by human experts.The combined classical machine-learning and deep learning method achieved an F1 score of 84.8% on the test subset, and a Cohen's Kappa coefficient of 0.83, which is similar to the inter-observer agreement of two human experts.The level of performance indicates that the automated detection of AF in patients whose data have been stored in a large database, such as the UK Biobank, is possible. Such automated identification of AF patients would enable further investigations aimed at identifying the different phenotypes associated with AF.