Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

BackgroundEpilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy.MethodsBy analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model.ResultsEpilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3. The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup.ConclusionWe believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway, JAK-STAT signaling pathway), and finally localized their key cellular subgroups (T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions

Mechanisms of Myocardial Stunning in Stress-Induced Cardiomyopathy

Stress-induced cardiomyopathy, in contrast to acute myocardial infarction, is a type of acute heart failure characterized by reversible left ventricular dysfunction. Cardiac imaging primarily reveals left ventricle myocardial stunning, 81.7% of which is apical type. Emotional or psychological stress usually precedes the onset of stress-induced cardiomyopathy, which is increasingly being recognized as a unique neurogenic myocardial stunning disease. To distinguish between acute myocardial infarction and acute viral or auto-immune myocarditis, this review summarizes specific mechanisms of myocardial stunning in stress-induced cardiomyopathy, such as calcium disorders, metabolic alterations, anatomical and histological variations in different parts of the left ventricle, and microvascular dysfunction

The Role of Sleep Deprivation in Arrhythmias

Sleep is essential to the normal psychological and physiological activities of the human body. Increasing evidence indicates that sleep deprivation is associated with the occurrence, development, and poor treatment effects of various arrhythmias. Sleep deprivation affects not only the peripheral nervous system but also the central nervous system, which regulates the occurrence of arrhythmias. In addition, sleep deprivation is associated with apoptotic pathways, mitochondrial energy metabolism disorders, and immune system dysfunction. Although studies increasingly suggest that pathological sleep patterns are associated with various atrial and ventricular arrhythmias, further research is needed to identify specific mechanisms and recommend therapeutic interventions. This review summarizes the findings of sleep deprivation in animal experiments and clinical studies, current challenges, and future research directions in the field of arrhythmias

Minimal conditions for collectivity in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mi>e</mml:mi><mml:mo>+</mml:mo></mml:msup><mml:msup><mml:mi>e</mml:mi><mml:mo>−</mml:mo></mml:msup></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi><mml:mo>+</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:math> collisions

Signatures of collective behavior have been measured in highly relativistic p+p collisions, as well as in p+A, d+A, and 3He+A collisions. Numerous particle correlation measurements in these systems have been successfully described by calculations based on viscous hydrodynamic and transport models. These observations raise the question of the minimum necessary conditions for a system to exhibit collectivity. Recently, numerous scientists have raised the question of whether the quarks and gluons generated in e+e- collisions may satisfy these minimum conditions. In this paper we explore possible signatures of collectivity, or lack thereof, in e+e- collisions utilizing A Multi-Phase Transport (AMPT) framework which comprises melted color strings, parton scattering, hadronization, and hadron re-scattering.Comment: 7 pages, 6 figures, submitted for publicatio

Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

BackgroundEpilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy.MethodsBy analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model.ResultsEpilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3. The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup.ConclusionWe believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway, JAK-STAT signaling pathway), and finally localized their key cellular subgroups (T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.</jats:sec

Identification of Potential Targets of Stress Cardiomyopathy by a Machine Learning Algorithm

Background: Stress cardiomyopathy (SCM) is a reversible, self-limiting condition that manifests as left ventricular insufficiency. The incidence of stress cardiomyopathy has increased because of increasing mental and social stress, but the exact pathophysiological mechanisms remain unclear. Methods: To elucidate the critical molecules in the pathogenesis of SCM and the functional changes that they mediate, we downloaded data for a healthy control group and stress cardiomyopathy (SCM) group from the Gene Expression Omnibus database, performed differential analysis, and analyzed the results of GO and KEGG enrichment analysis to describe SCM-associated genes and functions. Lasso, random forest, SVM-RFM, and Friends analysis were used to screen hub genes; CIBERSORT and MCPcounter were used to explore the relationship between SCM and immunity; and an animal model of SCM was constructed to conduct bidirectional verification of the obtained results. Results: In total, 21 samples (6 healthy, 15 SCM) were used in this study. Overall, 39 DEGs (absolute fold change ≥ 1; P < 0.05), including 23 upregulated and 16 downregulated genes in SCM, were extracted. Three common hub genes ( PLAT , SEMA6B , and CRP ) were finally screened. We further confirmed that functional changes in SCM were concentrated in immunity and coagulation functions. Conclusion: Three key genes (PLAT, SEMA6B, and CRP) in SCM were identified by machine learning, and the major functional changes leading to SCM, and relationships of SCM with immunity, were identified