Heart Rate Signals Research Articles

IR-UWB Radar-Based Robust Heart Rate Detection Using a Deep Learning Technique Intended for Vehicular Applications

This paper deals with robust heart rate detection intended for the in-car monitoring of people. There are two main problems associated with radar-based heart rate detection. Firstly, the signal associated with the human heart is difficult to separate from breathing harmonics in the frequency domain. Secondly, the vital signal is affected by any interference signal from hand gestures, lips motion during speech or any other random body motions (RBM). To handle the problem of the breathing harmonics, we propose a novel algorithm based on time series data instead of the conventionally used frequency domain technique. In our proposed method, a deep learning classifier is used to detect the pattern of the heart rate signal. To deal with the interference mitigation from the random body motions, we identify an optimum location for the radar sensor inside the car. In this paper, a commercially available Novelda Xethru X4 radar is used for signal acquisition and vital sign measurement of 5 people. The performance of the proposed algorithm is compared with and found to be superior to that of the conventional frequency domain technique.

Abstract P1128: Myocardial Delivery Of Modified Mrna For Exosomal Protein Induces Cardiac Regeneration

Permanent loss of cardiomyocytes (CM) after myocardial infarction (MI) and limited cardiac regenerative capacity leads to heart failure. Recent studies from zebrafish, newt, and even 1-day old neonatal mice showed that they could regenerate their heart through inducing existing CM proliferation. Attempts have been made to transiently reconstitute embryonic signaling in adult hearts, including overexpression of cell cycle activating genes with limited success. iPSC-derived extracellular vesicles (EV)/exosomes have been shown to improve cardiac function and some degree of CM renewal. However, the iPSC-EVs-mediated cardiac regeneration mechanism remains unclear and largely pertains to microRNAs and other RNAs, but not the proteins. Our hypothesis is the myocardial delivery of iPSC-EV-specific protein improves cardiac function and remodeling post-MI by activating pro-proliferative and anti-oxidative stress molecular pathways. Our preliminary studies showed that hiPSC-EVs induced the CM cell cycle in mice post-MI, and by employing a proteomic approach, we found a novel protein exclusively expressed in iPSC-EVs. The overexpression of hiPSC-EV enriched protein in the form of modRNA (modified mRNA) induces a robust CM cell cycle in rat neonatal CMs and in heart post-MI. By employing cardiomyocyte specific MADM (Mosaic Analysis of Double Markers) mice we showed that EV-protein induced robust cardiomyocyte division post-MI. This increase in the CMs proliferation by the modRNA was associated with reduced scar size, improved cardiac function, and mice survival 28 days post-MI. Moreover, we show that the YAP, a master regulator of CM proliferation and cardiac regeneration, binds to the promoter of the protein and induces its expression. Furthermore, using the siRNA and modRNA (inhibition and over-expression respectively) approach, we found that the protein-induced Yap1-β-catenin molecular pathway stimulates CM proliferation. Furthermore, the overexpression protein inhibited the oxidative stress or ROS analyzed by HPLC and DNA damage response post-MI. As a result of it, we have seen a significant reduction in CM apoptosis post-MI. Taken together we see the myocardial injection of iPSC-EV specific protein through a highly therapeutic modRNA tool improve cardiac function by inducing CMs proliferation, inhibiting oxidative stress, and reactivating cardiac regeneration post-injury.

Letter by Li and Nie Regarding Article, “Inflammatory Glycoprotein 130 Signaling Links Changes in Microtubules and Junctophilin-2 to Altered Mitochondrial Metabolism and Right Ventricular Contractility”

HomeCirculation: Heart FailureVol. 15, No. 8Letter by Li and Nie Regarding Article, “Inflammatory Glycoprotein 130 Signaling Links Changes in Microtubules and Junctophilin-2 to Altered Mitochondrial Metabolism and Right Ventricular Contractility” Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBLetter by Li and Nie Regarding Article, “Inflammatory Glycoprotein 130 Signaling Links Changes in Microtubules and Junctophilin-2 to Altered Mitochondrial Metabolism and Right Ventricular Contractility” Ruopu Li, MD, PhD and Yu Nie, PhD Ruopu LiRuopu Li https://orcid.org/0000-0002-7724-018X State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Search for more papers by this author and Yu NieYu Nie https://orcid.org/0000-0002-8744-0046 State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Search for more papers by this author Originally published27 Jun 2022https://doi.org/10.1161/CIRCHEARTFAILURE.122.009531Circulation: Heart Failure. 2022;15Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: June 27, 2022: Ahead of Print To the Editor:We read with great interest the recent article by Prisco et al1 regarding the cardiac-protective role of gp130 blockade in monocrotaline induced pulmonary artery hypertension reflected by the antihypertrophy, antifibrotic effect, and the mitigated right ventricle function after gp130 antagonist SC-144 administration. The study expanded our consideration of gp130’s downstream signal to right heart function and its connecting role of inflammation, cytoskeleton organization and energy metabolism. However, here are some questions we would like to raise regarding the findings in the original article.Regenerative repair after myocardial injury requires gp130-induced STAT3 and Src-YAP pathways.2,3 And it has been reported that the loss of gp130 signaling in heart lead to adaption incapacity under biomechanical stress of pressure overload.4 We would like to respectfully ask what is the authors’ perspective on the opposite biological effect of gp130 concerning cardiac tissue response toward stress between left and right ventricle.Although major, there are different pathways downstream of gp130 other than Jak-STAT3. Did the author also detect other signaling pathways, including Erk-MAPK (extracellular regulated protein kinases-mitogen activated protein kinase pathway), PI3K-Akt (phosphoinositide-3 kinase-serine/threonine kinase Akt [also known as protein kinase B or PKB] pathway), and Src-YAP (pathway of proto-oncogene tyrosine-protein kinase Src and its downstream protein Yes associated protein [YAP]) pathways, in the pulmonary artery hypertension model before and after SC-144 administration? And did the author confirm the similar effect with SC-144 treatment under STAT3 antagonized model? SC-144 was adopted for the construction of gp130 depletion model. But the agent triggers different gp130 downstream signaling alteration including STAT3 inhibition and Erk activation.5The author treated immortalized H9c2 cell line with cytoskeleton stabilizer paclitaxel and detected an impaired mitochondrial metabolic activity. Here, we expected whether cytoskeleton interference would result in the same outcome in mitochondrial metabolism in primary neonatal rat ventricular myocytes (primary cardiomyocytes isolated form neonatal Rattus norvegicus). Paclitaxel has cytotoxicity on proliferative cells by inhibiting spindle formation, and cell mitosis is an energy-consuming process. So, the impair of cell cycle results in metabolic dysfunction. It is confusing whether it was due to the cell cycle toxicity or the cytoskeleton stabilizing that causes the alteration of mitochondrial metabolism.Article InformationSources of FundingThis work was supported by the National Natural Science Foundation of China (grant 81970243) and the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant 2021-I2M-1-008).Disclosures None.FootnotesFor Sources of Funding and Disclosures, see page 823.

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning.

Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model.

Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.

Can you feel the excitement? Physiological correlates of students' self-reported emotions.

This study explored the physiological correlates of students' self-reported emotions in ecologically valid settings by combining biosignal data (on physical and cardiac activity) and experience sampling method (ESM) data. The aim was to examine the concurrent associations between self-reported excitement, calmness, anxiety, and boredom (adopted from the dimensional model of emotions) and students' heart rate (HR) and heart rate variability (HRV) (indicators of physiological arousal and the activation of the autonomous nervous system). Students' physical activity was controlled in the models via the metabolic equivalent of task (MET) values (actigraphy data). A second objective was to explore how to combine the information stored by these three sources of ambulatory assessment. The study comprised 136 high school students with multiple repetitive data points. For three consecutive days, students wore biometric sensors and wristbands collecting their HR, HRV, and MET signals, and answered the ESM questionnaires five times a day on smartphones. When MET values were controlled for, self-reported excitement was related to higher HR as well as lower HRV during a specific moment, indicating stronger sympathetic activity (i.e., physiological arousal/activation). Self-reported boredom was related to lower HR but was unrelated to HRV. Self-reported calmness and anxiety were unrelated to HR and HRV after controlling for MET. A 5-min time window with a Gaussian weighted mean seemed to be an appropriate data processing method for capturing the physiological arousal (or abate) of self-reported excitement and boredom. The findings suggest that the physiological stimulus of elevated HR could be interpreted by students as an adaptive state of excitement. Combining the experience sampling approach and the physiological measures revealed how the mind and body function in interplay and can therefore provide objective evidence of emotional and motivational processes as they unfold in students' daily lives.

Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units

Computerized Cardiotocography (cCTG), which involves the continuous recording of the fetal heart rate (FHR) and uterine contraction (UC) signals, plays a critical role in the evaluation of antepartum fetal well-being. However, traditional cCTG usually performs the processes of CTG feature extraction and classification separately, which has a certain degree of calibration bias and cannot fully use the CTG information. In this paper, we develop a multimodal bidirectional gated recurrent units (MBiGRU) network for end-to-end CTG feature extraction and classification. Specifically, data preprocessing was first conducted on raw CTG data, including missing values and outliers processing, FHR signal normalization, signal segmentation and enhancement. Afterward, the synchronous FHR, UC, and fetal movement (FetMov) signal fragments were converted into the corresponding two-dimensional fragments through the embedding layers, and then the multimodal fusion of signals was performed in the concatenating layers. Furthermore, classification results were obtained by bidirectional gated recurrent unit (BiGRU) with a fully connected layer and sigmoid function. The effectiveness of the proposed MBiGRU model was tested on 16,355 antenatal CTG records collected from collaborating hospitals with consistent case interpretation by three expert obstetricians. The experimental results of ten-fold cross validation showed that the average accuracy, F1-score, and area under the curve (AUC) values of MBiGRU were 86.45%, 86.14%, and 0.9327, respectively. The MBiGRU improved upon the performance of the baseline BiGRU network with no UC or FetMov signal inputs and outperformed other deep learning methods. In conclusion, the proposed MBiGRU network is promising for intelligent antepartum fetal monitoring.

Neurogranin regulates calcium-dependent cardiac hypertrophy

Intracellular Ca2+-calmodulin (CaM) signaling plays an important role in Ca2+-CaM-dependent kinase (CaMKII) and calcineurin (CaN)-mediated cardiac biology. While neurogranin (Ng) is known as a major Ca2+-CaM modulator in the brain, its pathophysiological role in cardiac hypertrophy has never been studied before. In the present study, we report that Ng is expressed in the heart and depletion of Ng dysregulates Ca2+ homeostasis and promotes cardiac failure in mice. 10-month-old Ng null mice demonstrate significantly increased heart-to-body weight ratios compared to wild-type. Using histological approaches, we identified that depletion of Ng increases cardiac hypertrophy, fibrosis, and collagen deposition near perivascular areas in the heart tissue of Ng null mice. Ca2+ spark experiments revealed that cardiac myocytes isolated from Ng null mice have decreased spark frequency and width, while the duration of sparks is significantly increased. We also identified that a lack of Ng increases CaMKIIδ signaling and periostin protein expression in these mouse hearts. Overall, we are the first study to explore how Ng expression in the heart plays an important role in Ca2+ homeostasis in cardiac myocytes as well as the pathophysiology of cardiac hypertrophy and fibrosis.

Fully Automatic Classification of Cardiotocographic Signals with 1D-CNN and Bi-directional GRU.

Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is vital for pregnant women before delivery. During pregnancy, it is essential to classify whether the fetus is abnormal, which helps physicians carry out early intervention to avoid fetal heart hypoxia and even death. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are essential to estimate fetal health status. In this paper, we pre-process the obtained data set and enhance them using Hermite interpolation on the abnormal classification in the samples. We use the 1D-CNN and GRU hybrid models to extract the abstract features of fetal heart rate and uterine contraction signals. Several evaluation metrics are used for evaluation, and the accuracy is 96 %, while the sensitivity is 95 %, and the specificity is 96 %. The experiments show the effectiveness of the proposed method, which can provide physicians and users with more stable, efficient, and convenient diagnosis and decision support.

Evolution of Heart Rate Complexity Indices in the Early Detection of Neonatal Sepsis.

Despite advances in prenatal health care, neonatal sepsis remains a major cause of neonatal mortality. Early diagnosis and adequate treatment are essential to reduce morbidity and mortality related to this disease. In this paper, we propose a new method to detect neonatal sepsis based on heart rate (HR) complexity measures (entropy and compression indices) that takes into consideration neonatal gestational age. First, the percentile curves were computed for all the complexity indices using data from 118 control neonates. Eight indices were computed: the sample entropy (SampEn) and three indices to quantify the multiscale entropy (MSE) curve - the sum, the slope, and the product of the previous two - and the compression ratio (CR), using the bzip2 compressor, as well as the same three indices but related to the multiscale compression (MSC) curve. Then, the corresponding percentile was estimated for 23 sepsis neonates. Results show a significant decrease in the entropy indices SampEn and MSEsum and in the MSCslope a day before the detection of sepsis by the clinicians. The indices CR and MSCsum increased before the antibiotic take. These results imply that sepsis causes a random, uncorrelated pattern on the HR signal. Future studies should include a bigger data set to calculate a compound index comprising information of other physiological signals. Clinical Relevance - Prompt and accurate diagnosis of neona-tal sepsis is essential for the successful clinical management of neonates and significantly reduce morbidity and mortality. Complexity measures applied to the HR time series appear to detect sepsis in neonates starting one day before the clinical detection.

Validation of Dozee, a Ballistocardiography-based Device, for Contactless and Continuous Heart Rate and Respiratory Rate Measurement.

Long-term acquisition of respiratory and heart signals is useful in a variety of applications, including sleep analysis, monitoring of respiratory and heart disorders, and so on. Ballistocardiography (BCG), a non-invasive technique that measures micro-body vibrations caused by cardiac contractions as well as motion caused by breathing, snoring, and body movements, would be ideal for long-term vital parameter acquisition. Turtle Shell Technologies Pvt. Ltd.'s Dozee device, which is based on BCG, is a contactless continuous vital parameters monitoring system. It is designed to measure Heart Rate (HR) and Respiratory Rate (RR) continuously and without contact in a hospital setting or at home. A validation study for HR and RR was conducted using Dozee by comparing it to the vitals obtained from the FDA-approved Patient Monitor. This was done in a sleep laboratory setting over 110 nights in 51 subjects to evaluate HR and over 20 nights in 17 subjects to evaluate RR at the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India. Approximately 789 hours data for HR and approximately 112 hours data for RR was collected. Dozee was able to achieve a mean absolute error of 1.72 bpm for HR compared to the gold standard ECG. A mean absolute error of ∼1.24 breaths/min was obtained in determining RR compared to currently used methods. Dozee is ideal for long-term contactless monitoring of vital parameters due to its low mean absolute errors in measuring both HR and RR. Clinical Relevance- Continuous and long-term vitals monitoring is known to enable early screening of clinical deterioration, improve patient outcomes and reduce mortality. Current methods of continuous monitoring are overly complex, costly, and rely heavily on patient compliance. The proposed remote vitals monitoring solution based on BCG was found to be at par with gold standard methods of recording HR and RR. As a result, clinicians can use it to effectively monitor patients in both the hospital and at home.

Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation.

Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.

On Fourier phases and their relevance for nonlinear time series analysis

Many processes in nature are governed by nonlinear mechanisms that are frequently superposed by pronounced linear components. To characterize such complex dynamics it is desirable to disentangle linear and nonlinear features in empirical data. Quantitative nonlinear measures are also influenced by linear properties of the signals and, for the univariate case, Fourier transform surrogates do not represent properly the null hypothesis of zero nonlinear features. Here we elaborate on the application of a recently published method in Fourier space that does not suffer from comparison with inappropriate surrogate data and that reveals with high sensitivity correlations between Fourier phases and amplitudes. In addition, we propose a simple pre-processing procedure that avoids the mixing of linear and nonlinear features when applying conventional nonlinear measures, which can drastically increase the sensitivity of the statistical evaluation and avoids numerical problems associated with surrogate data. We test our proposal on data derived from numerical models and we analyze electroencephalographic recordings from epilepsy patients as well as heart rate signals from healthy subjects before and during meditation.

Effects of exercise anticipation on cardiorespiratory coherence.

In this study, we explored the role of feedforward mechanisms in triggering cardiorespiratory adjustments before the onset of exercise. To isolate the feedforward aspects, we examined the effect of exercise anticipation on cardiorespiratory coherence. Twenty‐nine healthy males (age = 18.8 [0.96] years) were subjected to bicycle (BE) and handgrip exercise (H) at two different intensities, viz., low and high. Bicycle exercise was performed in a unilateral (left‐ and right‐sided) or bilateral mode, whereas handgrip was performed only in a unilateral mode. Single‐lead ECG and respiratory rhythm, measured in the 5 min of anticipation phase before the onset of exercise, were used for analysis. Coherence was computed between ECG‐derived instantaneous heart rate and respiratory signal. Average coherence in the high‐frequency band (0.15–0.4 Hz) was used to estimate respiratory sinus arrhythmia (RSA). We found that coherence decreased with the anticipation of exercise relative to baseline (baseline = 0.54 [0.16], BE = 0.41 [0.12], H = 0.39 [0.12], p < 0.001). The decrease was greater for high intensity exercise (low = 0.42 [0.11], high = 0.37 [0.1], p < 0.001). The fall of coherence with intensity was stronger for bicycle exercise (BE: low = 0.44 [0.12], high = 0.37 [0.12], H: low = 0.4 [0.12], high = 0.37 [0.12], p = 0.00433). The expectation of bilateral exercise resulted in lower coherence compared to unilateral exercise (right‐sided = 0.45 [0.16], left‐sided = 0.4 [0.16], bilateral = 0.36 [0.15], unilateral vs. bilateral: p < 0.001), and the left‐sided exercise had lower coherence compared to that of the right (left‐sided vs. right‐sided: p = 0.00925). Handgrip exercise showed similar trend (right‐sided = 0.4 [0.15], left‐sided = 0.37 [0.14], p = 0.0056). In conclusion, feedforward RSA adjustments in anticipation of exercise covaried with subsequent exercise‐related features like intensity, muscle mass (unilateral vs. bilateral), and the exercise side (left vs. right). The left versus the right difference in coherence indicates autonomic asymmetry. Feedforward changes in RSA are like those seen during actual exercise and might facilitate the rapid phase transition between rest and exercise.

Low-Cost Portable ECG Monitoring Device for Inaccessible Areas in Pakistan

The need for a constant health monitoring system is essential especially for cardiovascular disease (CVD) that may cause sudden stroke or even death. In this paper, we present the design and development of a low-cost, efficient, and user-friendly portable Electrocardiography (ECG) monitoring device. Most of the existing heart rate measurement devices and tools available in underdeveloped countries like Pakistan are quite expensive, far to reach and inaccessible in most of the rural areas especially for the female population to reach the healthcare centers. Our proposed device monitors the patient’s body information using three lead-silver chloride ECG disposable electrodes and detects the pulse signal. The detected heart pulse signal is first filtered and then amplified. The device can display the heart signal on an external PC/laptop Graphical User Interface (GUI) and the digitized pulse rate on a Liquid Crystal Display or (LCD) to detect any signs of abnormality. We have evaluated the device on several trial subjects. Finally, the results were compared with the standard ECG signal. Our device measures the pulse signal with good accuracy like commercial devices with higher costs.

Wearable Psychological Stress Monitoring Equipment and Data Analysis Based on a Wireless Sensor

With the rapid development of social economy, human psychological pressure is an important factor affecting human health. Excessive psychological pressure will lead to serious psychological diseases such as depression and anxiety. The traditional psychological stress monitoring method is mainly limited to the psychological scale. This method has a certain subjectivity, so its corresponding test data is not representative. At the hardware design level, this paper will select miniaturized, low-power, and low-cost microphysiological sensors to monitor the psychological pressure level discrimination indicators such as heart rate, body temperature, and heart rate waveform and fully optimize the layout of wireless sensors at the hardware layout level to achieve the high efficiency of the whole system. The detection of human pulse signal and heart rate signal is mainly carried out through microsensors, and the temperature signal is filtered and amplified, and analog-to-digital conversion is carried out to realize the accurate measurement of key signal waveform. At the level of hardware system and software algorithm, this paper creatively proposes a psychological stress recognition algorithm based on evidence theory. By extracting the collected key signal features, we can identify the primary stage of psychological stress and finally realize the evaluation and analysis of individual psychological stress through evidence theory. The experimental results show that the trust degree of an individual psychological stress test is improved by 0.187 compared with the traditional algorithm, and the corresponding psychological stress trust degree is up to 0.988, which has obvious advantages.

Cardioprotective Effects of 1-(3,6-Dibromo-carbazol-9-yl)-3-Phenylamino-Propan-2-Ol in Diabetic Hearts via Nicotinamide Phosphoribosyltransferase Activation.

Diabetes is associated with increased cardiac injury and sudden death. Nicotinamide phosphoribosyltransferase (Nampt) is an essential enzyme for the NAD+ salvage pathway and is dysregulated in diabetes. Nampt activation results in rescued NADH/NAD+ ratios and provides pharmacological changes necessary for diabetic cardioprotection. Computer docking shows that 1-(3,6-Dibromo-carbazol-9-yl)-3-phenylamino-propan-2-ol (P7C3) allows for enhanced Nampt dimerization and association. To test the pharmacological application, we used male leptin receptor-deficient (db/db) mice and treated them with Nampt activator P7C3. The effects of 4-week P7C3 treatment on cardiac function were evaluated along with molecular signaling changes for phosphorylated protein kinase B (p-AKT), phosphorylated endothelial nitric oxide synthase (p-eNOS), and sirtuin 1 (SIRT1). The cardiac function evaluated by ECG and echocardiography were significantly improved after 4 weeks of P7C3 treatment. Biochemically, higher NADH/NAD+ ratios in diabetic hearts were rescued by P7C3 treatment. Moreover, activities of Nampt and SIRT1 were significantly increased in P7C3-treated diabetic hearts. P7C3 treatment significantly decreased the blood glucose in diabetic mice with 4-week treatment as noted by glucose tolerance test and fasting blood glucose measurements compared with vehicle-treated mice. P7C3 activated Nampt enzymatic activity both in vitro and in the 4-week diabetic mouse hearts, demonstrating the specificity of the small molecule. P7C3 treatment significantly enhanced the expression of cardioprotective signaling of p-AKT, p-eNOS, and Beclin 1 in diabetic hearts. Nampt activator P7C3 allows for decreased infarct size with decreased Troponin I and lactose dehydrogenase (LDH) release, which is beneficial to the heart. Overall, the present study shows that P7C3 activates Nampt and SIRT1 activity and decreases NADH/NAD+ ratio, resulting in improved biochemical signaling providing cardioprotection. SIGNIFICANCE STATEMENT: This study shows that 1-(3,6-Dibromo-carbazol-9-yl)-3-phenylamino-propan-2-ol (P7C3) is effective in treating diabetes and cardiovascular diseases. The novel small molecule is antiarrhythmic and improves the ejection fraction in diabetic hearts. The study successfully demonstrated that P7C3 decreases the infarct size in hearts during myocardial infarction and ischemia-reperfusion injury. Biochemical and cellular signaling show increased NAD+ levels, along with Nampt activity involved in upregulating protective signaling in the diabetic heart. P7C3 has high therapeutic potential for rescuing heart disease.

The focal adhesion protein β-parvin controls cardiomyocyte shape and sarcomere assembly in response to mechanical load

Physiological and pathological cardiac stress induced by exercise and hypertension, respectively, increase the hemodynamic load for the heart and trigger specific hypertrophic signals in cardiomyocytes leading to adaptive or maladaptive cardiac hypertrophy responses involving a mechanosensitive remodeling of the contractile cytoskeleton. Integrins sense load and have been implicated in cardiac hypertrophy, but how they discriminate between the two types of cardiac stress and translate mechanical loads into specific cytoskeletal signaling pathways is not clear. Here, we report that the focal adhesion protein β-parvin is highly expressed in cardiomyocytes and facilitates the formation of cell protrusions, the serial assembly of newly synthesized sarcomeres, and the hypertrophic growth of neonatal rat ventricular cardiomyocytes (NRVCs) invitro. In addition, physiological mechanical loading of NRVCs by either the application of cyclic, uni-axial stretch, or culture on physiologically stiff substrates promotes NRVC elongation in a β-parvin-dependent manner, which is achieved by binding of β-parvin to α/β-PIX, which in turn activates Rac1. Importantly, loss-of-function studies in mice also revealed that β-parvin is essential for the exercise-induced cardiac hypertrophy response invivo. Our results identify β-parvin as a novel mechano-responsive signaling hub in hypertrophic cardiomyocytes that drives cell elongation in response to physiological mechanical loads.

Generative adversarial networks for unbalanced fetal heart rate signal classification

Deep Learning Classification is often used to analyze biomedical data. One of them is to analyze the Fetal Heart Rate (FHR) signal data used to check and monitor maternal and fetal health and prevent mobility and mortality in fetuses at risk of developing hypoxia. The problem that often occurs in the data is data unbalance. Time Series Generative Adversarial Networks (TSGAN) solves data imbalance in the FHR signal and generate more data and better classification performance. Augmentation using the GAN model in this study obtained an increase in the Quality Index of 3%–44% from other models.

Perceived stress and physiological consistency during mental stress exercises and controlled breathing

IntroductionThe measurement of the physiological coherence, the order and the quality of the connection of complex systems such as the cardiac and the respiratory system, varies in situations of stress and relaxation.ObjectivesWe aim to assess changes in physiological coherence and perception of stress during mental stress and directed breathing exercises.MethodsRepeated-measures study in healthy adults without prior training in breathing techniques, aged between 18 and 65 years of both sexes who were evaluated in three situations: baseline, mental stress (Stroop test and successive subtractions), and directed breathing, during which were captured heart rate and respiratory signals to estimate physiological coherence and the participants rated the perceived stress at each moment.Results34 participants were analyzed, 59% women, with a median age of 36 years (Rq = 13). During mental stress tasks, the median for physiological coherence was similar to baseline coherence but increased significantly with five minutes of directed breathing exercises (38% vs. 63% p &lt;0.0001). The highest perception of stress was during successive subtractions (Me 7, Rq = 4) and the lowest during directed breathing exercises (Me 2 Rq = 3.0). The correlation was sought between physiological coherence and perception of stress during each of the four moments of the study. Basal (Rho Spearman -0.05, p 0.54); Stroop (Rho -0.17, p 0.03); successive subtractions (Rho 0.50, p 0.77); and directed breathing (Rho -0.28, p 0.09).ConclusionsA correlation was found between physiological coherence and perception of stress during the Stroop test; however, no association was found.DisclosureNo significant relationships.