Comprehensive Analysis of Genes Associated With Sudden Infant Death Syndrome

Background: Sudden infant death syndrome (SIDS) is a tragic incident which remains a mystery even after post-mortem investigation and thorough researches.Methods: This comprehensive review is based on the genes reported in the molecular autopsy studies conducted on SIDS so far. A total of 20 original studies and 7 case reports were identified and included in this analysis. The genes identified in children or adults were not included. Most of the genes reported in these studies belonged to cardiac channel and cardiomyopathy. Cardiac channel genes in SIDS were scrutinized for further analysis.Results: After screening and removing the duplicates, 42 unique genes were extracted. When the location of these genes was assessed, it was observed that most of these belonged to Chromosomes 11, 1 and 3 in sequential manner. The pathway analysis shows that these genes are involved in the regulation of heart rate, action potential, cardiac muscle cell contraction and heart contraction. The protein-protein interaction network was also very big and highly interactive. SCN5A, CAV3, ALG10B, AKAP9 and many more were mainly found in these cases and were regulated by many transcription factors such as MYOG C2C1 and CBX3 HCT11. Micro RNA, “hsa-miR-133a-3p” was found to be prevalent in the targeted genes.Conclusions: Molecular and computational approaches are a step forward toward exploration of these sad demises. It is so far a new arena but seems promising to dig out the genetic cause of SIDS in the years to come.

Cardiac-Specific Overexpression of ERRγ in Mice Induces Severe Heart Dysfunction and Early Lethality

Proper cardiac function depends on the coordinated expression of multiple gene networks related to fuel utilization and mitochondrial ATP production, heart contraction, and ion transport. Key transcriptional regulators that regulate these gene networks have been identified. Among them, estrogen-related receptors (ERRs) have emerged as crucial modulators of cardiac function by regulating cellular metabolism and contraction machinery. Consistent with this role, lack of ERRα or ERRγ results in cardiac derangements that lead to functional maladaptation in response to increased workload. Interestingly, metabolic inflexibility associated with diabetic cardiomyopathy has been recently associated with increased mitochondrial fatty acid oxidation and expression of ERRγ, suggesting that sustained expression of this nuclear receptor could result in a cardiac pathogenic outcome. Here, we describe the generation of mice with cardiac-specific overexpression of ERRγ, which die at young ages due to heart failure. ERRγ transgenic mice show signs of dilated cardiomyopathy associated with cardiomyocyte hypertrophy, increased cell death, and fibrosis. Our results suggest that ERRγ could play a role in mediating cardiac pathogenic responses.

The Heart

This chapter covers the creation of a bioprinted heart (cardiac) patch that might help to restore functional heart tissue after a heart attack. The chapter tells of the discovery that calcium ions carry the signal that initiates heart contraction. It also explains research on a pre-vascularized bioprinted heart patch that uses decellularized extracellular matrix and cardiac progenitor cells in its preparation. This study showed that of the various bioprinted constructs tested, the one that placed cells in an ordered arrangement (rather than as a random mix) gave the best results when transplanted into mice or rats. The experimenters performed echocardiograms on rats with a myocardial infarction (heart attack) and which had received a heart patch. Another study measures the contraction force exerted by bioprinted beating heart tissue.

Predicting heart failure using a wrapper-based feature selection

In the current health system, it is very difficult for medical practitioners/physicians to diagnose the effectiveness of heart contraction. In this research, we proposed a machine learning model to predict heart contraction using an artificial neural network (ANN). We also proposed a novel wrapper-based feature selection utilizing a grey wolf optimization (GWO) to reduce the number of required input attributes. In this work, we compared the results achieved using our method and several conventional machine learning algorithms approaches such as support vector machine, decision tree, K-nearest neighbor, naïve bayes, random forest, and logistic regression. Computational results show not only that much fewer features are needed, but also higher prediction accuracy can be achieved around 87%. This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

Aortic flow below and visceral circulation during aortic counterpulsation: Evaluation of an in vitro model

Introduction: This study aimed to test a computer-driven cardiovascular model for the evaluation of the visceral flow during intra-aortic balloon pump (IABP) assistance. Methods: The model includes a systemic and pulmonary circulation as well as a heart contraction model. The straight polyurethane tube aorta had a single visceral while four windkessel components mimicked resistance compliance of the brachiocephalic, renal and sub-mesenteric, pulmonary, and systemic circulation. Twelve flow probes were placed in the circuit to measure pressures and flows with the IABP on and off. Results: With the balloon off, the meantime to reach the steady state was 48 ± 16 s; with the balloon on, this figure was 178 ± 20 s. The stability of pressure and flow signals was obtained after 72 ± 11 min. The number of cycles of stability of the system was 93 [86–103]. Measurements were reliable either with samples of 10 or 20 beats. Bland Altman method demonstrated the reliability of measurements. Finally, all measurements were comparable to published in vivo data. Conclusion: The presented mock circulation was reliable and gave values with high accuracy both at baseline and during mechanical assistance. This system allows evaluation of the mesenteric flow during IABP, under different clinical/hemodynamic conditions. Nonetheless, its translational potential needs to be further evaluated

Augmented reality-based learning for the comprehension of cardiac physiology in undergraduate biomedical students

The integrated mechanisms of heart contraction are some of the most complex processes for undergraduate biomedical students to understand. Visual models have the potential to enhance learning environments by providing visual representations of complex mechanisms. Despite their benefits, the use of visual models in undergraduate classrooms is still limited. For this study, we tested the effect of a learning sequence of activities related to the cardiac cycle using an augmented reality (AR) application for smartphones and tablets. We were interested in understanding the ability of students to draw and label figures reflecting cardiac function after experiencing the learning sequence using AR. Undergraduate students of the biomedical sciences (control n = 43, experimental n = 58) were enrolled in the course, and their drawings were evaluated using multiple levels of complexity (1 = basic to 5 = complex) through a pre-/posttest structure that included a learning sequence based on AR in the experimental group and regular lecture-based activities in the control group. The complexity of students’ drawings was evaluated on the anatomical, physiological, and molecular aspects of heart contraction. We used Cohen’s kappa index for interrater reliability when determining the complexity of drawings. Control and experimental groups showed no differences in baseline knowledge (preexamination quiz). The students who experienced the AR activities showed an increase in the complexity of representation levels in posttest results and also showed a significant difference in scores for the final exam in the heart physiology course. Our results indicate that using AR enhances the comprehension of anatomical and physiological concepts of the cardiac cycle for undergraduate biomedical students.

Application of Cardio-Forecasting for Evaluation of Human—Operator Performance

The paper presents the results of the development of the cardio-forecasting technology, which introduces a new method to monitor the state of human-operator, which is characteristic for the given production conditions and for individual operators, to predict the moment of exhaustion of his/her working capacity. The work aims to demonstrate the unique, distinctive features of the cardio-forecasting technology for predicting an individual limit of his/her working capacity for each person. A unique methodology for predicting individually for each person the moment when he/she reaches the limit of his/her working capacity is based on a spectral analysis of a human phonocardiogram in order to isolate the frequency component located at the heart contraction frequency. The trend of the amplitude of this component is approximated by its model; consequently, the coefficients of the trend model are determined. They include the operator’s operating time until his/her working capacity is exhausted. A methodology for predicting the moment when he/she reaches the limit of his/her working capacity for each person individually and assessment based on this degree of criticality of their condition will be realized as a software application for smartphones using the Android operating system.

Calorimetry of students’ heart rate during exercises of various intensity

The purpose of the study: based on data analysis, to develop a formula for calorimetry of students’ heart rate during physical activity and experimentally prove the effectiveness of its application in the training process. The study participants (n=98) were divided by body weight into groups (n=7), regardless of gender and age (20-25 years). Various mobile devices with the function of heart rate calculation and monitoring of kilocalories burning were used in the implementation of control physical activities at different levels of intensity. Analysis of the obtained calorimetric data for each group and the level of training intensity allowed us to identify patterns and develop a formula based on them for an affordable and simple calculation of kilocalories. N=0.00168-0.098/P, where N is the number of kilocalories burned for 1 heart contraction per 1 kilogram of body weight, P is the heart rate in physical training (beats per minute). An experimental study confirmed the effectiveness of using the author’s formula of heart rate calorimetry for the accuracy and uniformity of kilocalories burning in students when they exercise aerobic physical activity.

Sarcomeres regulate cardiomyocyte maturation through MRTF-SRF signaling

Cardiomyocyte maturation is essential for robust heart contraction throughout life. The signaling networks governing cardiomyocyte maturation remain poorly defined. Our prior studies established the transcription factor SRF as a key regulator of the assembly of sarcomeres, the contractile unit of cardiomyocytes. Whether sarcomeres regulate other aspects of maturation remains unclear. Here we generated mice with cardiomyocyte specific, mosaic mutation of α-actinin-2 (Actn2), a key organizer of sarcomeres, to study its cell-autonomous role in cardiomyocyte maturation. In addition to the expected structural defects, Actn2 mutation triggered dramatic transcriptional dysregulation, which strongly correlated with transcriptional changes observed in SRF-depleted cardiomyocytes. Actn2 mutation increased monomeric actin, which perturbed the nuclear localization of the SRF cofactor MRTFA. Overexpression of a dominant-negative MRTFA mutant was sufficient to recapitulate the transcriptional and morphological defects in Actn2 and Srf mutant cardiomyocytes. Together, we demonstrate that ACTN2-based sarcomere assembly and MRTF-SRF signaling establish a positive feedback loop that promotes cardiomyocyte maturation.