Normal Beats Research Articles

Классификация сигналов ЭКГ на основе цифровой обработки сигналов, технического выбора функций и радиочастотного классификатора

Determining the optimal integration between features and classifiers has a significant effect on the performance of automatic heartbeat diagnostic systems. This importance stands out when dealing with critical applications that contain limited resources devices and require accurate and fast heartbeat classifiers to help the doctor make an accurate and quick diagnosis of heart diseases. Aiming at this task, this paper introduces a novel approach for choosing the optimal features of the ECG signal to be used with the Random Forest (RF) classifier following the inter-patient method for ECG signals division and obeying the instructions of the "Association for the Advancement of Medical Instrumentation (AAMI)." The features were chosen based on the concept of "Mutual Information Ranking (MIR)." The presented framework is comprehensive in terms of performing all the necessary processes efficiently, starting from ECG digital signal processing, segmentation, feature extraction, feature selection, and ending with ECG classification. The results of the experiments demonstrate that features corresponding to the normalized QRS width and the normalized RR intervals are the most influential features in the heartbeat classification. All tests were conducted using real ECG signals taken from the "MIT-BIH" Arrhythmia Database (MIT-BIH-ARR-DB). The suggested scheme attained the following F1-scores: 91.02%, 73.17%, and 98.04% in the classification of the Ventricular Ectopic Beats (V or VEB), Supraventricular Ectopic Beats (S or SVEB), and Normal Beats (N or NB), respectively. The overall accuracy was 96.26%. Despite its relative simplicity and reliance on few features, the proposed approach outperforms most of the reported state-of-the-art.

Lentiviral expression of wild-type LAMA3A restores cell adhesion in airway basal cells from children with epidermolysis bullosa

The hallmark of epidermolysis bullosa (EB) is fragile attachment of epithelia due to genetic variants in cell adhesion genes. We describe 16 EB patients treated in the Ear, Nose and Throat department of a tertiary pediatric hospital linked to the United Kingdom’s National EB unit between 1992 and 2023. Patients suffered a high degree of morbidity and mortality from laryngotracheal stenosis. Variants in laminin subunit alpha-3 (LAMA3) were found in 10/15 patients where genotype was available. LAMA3 encodes a subunit of the laminin-332 heterotrimeric extracellular matrix protein complex and is expressed by airway epithelial basal stem cells. We investigated the benefit of restoring wildtype LAMA3 expression in primary EB patient-derived basal cell cultures. EB basal cells demonstrated weak adhesion to cell culture substrates, but could otherwise be expanded similarly to non-EB basal cells. In vitro lentiviral overexpression of LAMA3A in EB basal cells enabled them to differentiate in air-liquid interface cultures, producing cilia with normal ciliary beat frequency. Moreover, transduction restored cell adhesion to levels comparable to a non-EB donor culture. These data provide proof-of-concept for a combined cell and gene therapy approach to treat airway disease in LAMA3-affected EB.

Cardiac Autonomic and Endothelial Function in Acute Lymphoblastic Leukaemia Patients Immediately After Chemotherapy and at the Three-Month Follow-up.

Acute lymphoblastic leukemia (ALL) is a malignant uncontrolled overproduction of immature lymphoid cells in blood and bone marrow. The primary treatment of ALL is chemotherapy. Chemotherapy can have myriad systemic side effects, notably cardiovascular derangement. Autonomic derangement occurrence in cancer patients signifies cardiovascular risk in them and is a determinant of cardiovascular morbidity and mortality. Elevated soluble vascular cell adhesion molecule-1 (sVCAM-1) and soluble intercellular adhesion molecule-1 (sICAM-1) levels implicated in the regulation of inflammation indicate endothelial dysfunction. High levels of high-sensitivity C-reactive protein (hsCRP) can be indicative of low-grade inflammation. Hence, in this study the cardiac autonomic function and endothelial and inflammatory biomarker levels in adult patients with ALLwere assessed immediately and three months after chemotherapy. In this longitudinal study, 30 ALL patients (23 males, seven females) aged between 18 to 50 years, who had completed chemotherapy regimens, and 30 age and gender-matched healthy participants (controls) were recruited. Cardiac autonomic function tests (short-term heart rate variability (HRV), 30:15 ratio, synaptic excitation and inhibition (E/I) ratio, diastolic blood pressure (DBP) response to isometric hand grip), endothelial markers (sVCAM-1 and sICAM-1), and inflammatory marker (hsCRP) were assessed immediately and at three months after chemotherapy. Magnitudes of time domain and frequency domain indices, conventional autonomic function test indices, and biomarkers were deranged in ALL patients immediately after chemotherapy. After three months, cardiac autonomic function parameters were found to improve in the form of increased root mean square of successive differences between normal heartbeats (RMSSD), standard deviation of the interbeat intervals of normal sinus beats (SDNN), total power, high-frequency (HF)nu, and decreased low-frequency(LF)nu & LF-HF ratio. Endothelial (sVCAM-1) and inflammatory markers (hsCRP) were lower in the patient group as compared to the controls immediately after chemotherapy. Three months after chemotherapy, the levels of endothelial and inflammatory markers did not show much change. In this study, we found ALL patients showed higher sympathetic drive, decreased parasympathetic modulation, and sympathovagal imbalance immediately after chemotherapy as compared to the controls, indicating cardiovascular risk. After three months, improvement in cardiovascular autonomic function was observed. ALL itself is a state of inflammation with elevated endothelial and inflammatory markers; thus, the decreased endothelial and inflammatory markers could be attributed to the immediate effect of chemotherapy.

Person identification with arrhythmic ECG signals using deep convolution neural network

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person’s beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.

Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram.

Objective. To develop analytical formulas which can serve as quantitative guidelines for the selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart rate (HR) and heart rate variability (HRV) with a desired level of accuracy.Approach. We developed analytical formulas which relate the ECG sampling rate to conservative bounds on HR and HRV errors: (i) one relating HR and sampling rate to a HR error bound and (ii) the others relating sampling rate to HRV error bounds (in terms of root-mean-square of successive differences (RMSSD) and standard deviation of normal sinus beats (SDNN)). We validated the formulas using experimental data collected from 58 young healthy volunteers which encompass a wide HR and HRV ranges through strenuous exercise.Main results. The results strongly supported the validity of the analytical formulas as well as their tightness. The formulas can be used to (i) predict an upper bound of inaccuracy in HR and HRV for a given sampling rate in conjunction with HR and HRV as well as to (ii) determine a sampling rate to achieve a desired accuracy requirement at a given HR or HRV (or its range).Significance. HR and its variability (HRV) derived from the ECG have been widely utilized in a wide range of research in physiology and psychophysiology. However, there is no established guideline for the selection of the sampling rate for the ECG required to calculate HR and HRV with a desired level of accuracy. Hence, the analytical formulas may guide in selecting sampling rates for the ECG tailored to various applications of HR and HRV.

Low-Intensity Focused Ultrasound to the Human Dorsal Anterior Cingulate Attenuates Acute Pain Perception and Autonomic Responses.

The dorsal anterior cingulate cortex (dACC) is a critical brain area for pain and autonomic processing, making it a promising noninvasive therapeutic target. We leverage the high spatial resolution and deep focal lengths of low-intensity focused ultrasound (LIFU) to noninvasively modulate the dACC for effects on behavioral and cardiac autonomic responses using transient heat pain stimuli. A N = 16 healthy human volunteers (6 M/10 F) received transient contact heat pain during either LIFU to the dACC or Sham stimulation. Continuous electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal response (EDR) were recorded. Outcome measures included pain ratings, heart rate variability, EDR response, blood pressure, and the amplitude of the contact heat-evoked potential (CHEP).LIFU reduced pain ratings by 1.09 ± 0.20 points relative to Sham. LIFU increased heart rate variability indexed by the standard deviation of normal sinus beats (SDNN), low-frequency (LF) power, and the low-frequency/high-frequency (LF/HF) ratio. There were no effects on the blood pressure or EDR. LIFU resulted in a 38.1% reduction in the P2 CHEP amplitude. Results demonstrate LIFU to the dACC reduces pain and alters autonomic responses to acute heat pain stimuli. This has implications for the causal understanding of human pain and autonomic processing in the dACC and potential future therapeutic options for pain relief and modulation of homeostatic signals.

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively.

Clinical Characteristics and Features of Idiopathic Premature Ventricular Contractions with an Enlarged Left Atrium in Patients Without Structural Heart Diseases

Background and aims: Idiopathic premature ventricular contractions (PVCs) may cause subtle changes in left atrium (LA) structure and function. Here, we investigated whether serum sodium, body mass index (BMI), N-terminal pro-B-type natriuretic peptide (NT-proBNP) and other characteristics might be associated with LA in these patients. Methods: A total of 268 consecutive patients diagnosed with idiopathic PVCs were retrospectively analyzed. We assessed associations of enlarged LA and with the clinical features obtained from 24-hour Holter monitoring, electrocardiography and serum data in patients with PVCs. Results: Patients with an enlarged LA (n = 101), compared with a normal LA (n = 167), had significantly lower serum sodium (140.9 ± 3.0 mmol/L vs 141.7 ± 2.8 mmol/L; P = 0.022), higher BMI (24.5 ± 2.7 kg/m2 vs 21.7 ± 2.5 kg/m2; P < 0.001), higher NT-proBNP [99.3 (193.6) pg/mL vs 77.8 (68.8) pg/mL; P < 0.001] and lower average heart rates (73.0 ± 8.0 bpm vs 75.3 ± 7.6 bpm; P = 0.019). No significant differences were observed in P-wave dispersion, QRS duration, PVC coupling interval, pleomorphism, circadian rhythm, non-sustained ventricular tachycardia, serum potassium, serum magnesium, hypersensitive C-reactive protein, low-density lipoprotein cholesterol, symptoms and PVC duration. Conclusions: Beyond the burden of PVCs, attributes such as serum sodium, BMI, NT-proBNP and average heart beats may potentially correlate with LA enlargement in individuals with idiopathic PVCs.

Pilot Randomized Controlled Trial of Multi-Disciplinary Community-Based Biofeedback Vs Health Promotion Programs on Mental Health And Drug Use Among People Experiencing Homelessness

Background: PEH are most vulnerable to COVID-19 due to high levels of chronic disease and social isolation. However, in Skid Row Los Angeles, there are limited resources for mental health and drug treatment services. Biofeedback (BF) has been found to decrease depression and substance use among drug users; however, no studies have assessed the impact of BF on PEH during the COVID-19 pandemic.
 Aim: Our multi-disciplinary team designed and pilot tested an 8-week Heart Rate Variability-Biofeedback (HRV-BF) intervention, utilizing a nurse-led, Community Health Worker model of care with 50 people experiencing homelessness (PEH) as compared with a Health Promotion (HP) program. Our goal was to establish feasibility of the program, and to assess effects HRV-BF on measures of HRV, mental health and drug use as compared to HP.
 Methods: Utilizing Community Based Participatory Research (CBPR) principles, a community advisory board was established to refine the HRV-BF intervention and assess acceptability and feasibility. We enrolled 40 PEH and randomized into HRV-BF vs HP program and assessed a) impact on anxiety and depression; b) HRV and cortisol indices; and c) drug and alcohol use among all participants.
 Results: In total, 40 PEH were enrolled with 36 completing the intervention; 17 HRV-BF and 19 HP. Majority were male (80%) and were equally Black (37.5%) or Hispanic (35%). The mean age of PEH was 49 (SD 16.7), median age 53. The HRV measure RMSSD (root mean square of successive differences between normal heart beats, a time-based variable reflecting vagally mediated HRV), assessed during the resting 5-minute period, showed significant increases in 71% of participants (75% among HRV-BF and 68% among HP), using paired t-tests with natural log transformed variables, p=0.004, and by non-parametric tests, p= 0.006 (p values one-sided). Across the sample, PHQ-9 (depressive symptoms) showed marginally significant improvement (p= 0.09). Anxiety and Mental Health Inventory scores showed improvement, though not significant. Between-group analysis showed the HRV-BF group had better GAD-7 and MHI scores at 8 weeks (post-intervention), though not statistically significant. Chronic disease was found to be high in both groups (67% among HRV-BF and 82% among HP). No significant differences were found in terms of substance use.
 Conclusions: As the first known study to date examining the role of a biofeedback self-regulation training on biomarkers of stress/mental health measures among PEH, our study shows feasibility and acceptability among the study population and positive initial results for both arms of the study. We plan to extend the follow-up period beyond the intervention period of 8 weeks to establish the sustainability of the intervention.
 Funding: Susan Samueli Integrative Health Institute Pilot Studies Award

Correlation between in vitro sperm kinetic, oxidative stress assessments and field fertility of cryopreserved bull semen

This study assessed kinetic parameters and oxidative stress in bull sperm after post-thaw (PT) or after sperm selection by Percoll™ gradient, and thermo resistance test (SS + TRT) to identify useful indicators of field fertility. For the experiment, commercial doses of frozen semen were obtained from six Aberdeen Angus bulls. Three of the bulls were classified as high fertility and three as low fertility according to the IFert™ index provided by the international breeding company CRV Lagoa. Pooled semen samples were distributed between two treatment groups for analysis: post-thaw (PT) or sperm selection (SS) (Percoll™) and thermal resistance test (SS + TRT). The samples were evaluated using sperm kinetics (CASA) (motility %, progressive motility %, VCL µm/s, VSL µm/s, VAP µm/s, LIN %, STR % and WOB%), production of reactive oxygen species (ROS), lipid peroxidation, superoxide dismutase (SOD) enzyme activity and total antioxidant capacity. Data were analyzed using Two-Way ANOVA, considering the fertility index, the treatment used in the samples as effects, and the interaction between these factors. When a significant effect was observed, the values were compared using the Bonferroni test. A Pearson Correlation analysis was performed between the fertility indices and the sperm parameters analyzed in vitro, to evaluate the relationship between sperm quality and the fecundity rate obtained by the bulls. Sperm kinetic parameters, including total motility, progressive motile, and beat cross-frequency, were higher in low fertility compared to high fertility bulls (P < 0.05). However, curvilinear velocity was greater in high fertility bulls followed by SS + TRT. Straight-line velocity, average path velocity, linearity, and beat cross-frequency beat were higher in high fertility bulls after SS + TRT. Reactive oxygen species was correlated with fertility after SS. In addition, there was a decrease in lipid peroxidation was observed only in high fertility bulls. However, lipid peroxidation and high fertility were correlated after PT and SS + TRT. The combination of in vitro sperm kinetic parameters predicted in vivo fertility more accurately than individual kinetic parameters. The lipid peroxidation of sperm is an important indicator of fertility in bulls. High fertility bulls appeared to be more susceptible to lipid peroxidation, which was only reduced in high fertility bulls, suggesting that their sperm can repair the damage induced by oxidative stress.

Inter-patient ECG arrhythmia heartbeat classification network based on multiscale convolution and FCBA

Cardiac arrhythmias that can lead to sudden cardiac death are common. Electrocardiograms (ECGs) offer valuable information about cardiac status and play a crucial role in evaluating patients with arrhythmia in clinical diagnosis. The development of machine learning technologies has made the computer-aided diagnosis of cardiac arrhythmias possible, which can improve the efficiency and quality of medical treatment. In this study, a new method for the automatic classification of heartbeats is developed. This paper presents a novel approach to detect arrhythmias in the inter-patient paradigm using a convolutional neural network (CNN) that integrates multiscale convolutional blocks, frequency convolutional block attention (FCBA) modules, and RR interval features. The proposed method also includes oversampling for the heartbeats of the minority class and the addition of random noise to increase training samples and alleviate the problem of data imbalance. We used the MIT-BIH-AR arrhythmia database , which is a globally recognized ECG database, to evaluate the classification performance of the proposed model in this paper. Based on the experiments, the sensitivities of the normal beats (N), supraventricular ectopic beats (SVEB), and ventricular ectopic beats (VEB) were 96.9%, 89.3%, and 93.3%, respectively. In the inter-patient heartbeat classification paradigm, the overall accuracy of the proposed method is 95.60% in classifying heartbeats. The study shows that the proposed scheme outperforms other published schemes in terms of classification results.

Predicting Post-Traumatic Stress Disorder Treatment Response Using Heart Rate Variability to Virtual Reality Environment and Modified Stroop Task: An Exploratory Study.

Predicting treatment response can inform treatment decisions, expectations, and optimize use of mental health treatment resources. This study examined heart rate (HR), heart rate variability (HRV), and a modified Stroop task (mStroop) to predict post-traumatic stress disorder (PTSD) treatment response. We report on an observational, longitudinal study with 45 U.S. veterans in outpatient PTSD care, who had deployed to Iraq or Afghanistan. HR and HRV were collected before, during, and after virtual reality (VR) combat and civilian scenes. HRV recovery was defined as HRV after a 3-minute VR simulation minus HRV during a VR scene. mStroop threat variables included index scores for combat and general threat. Self-report data were collected at baseline and 6 months later. The outcome variable was the 17-item Clinician Administered PTSD Scale (CAPS). Controlling for baseline CAPS and number of combat experiences, the following baseline HRV recovery variables were significant predictors of 6-month CAPS: standard deviation of normal beat to beat interval (SDNN) after combat scene minus SDNN during combat scene and low-frequency (LF HRV) after civilian scene minus LF during civilian scene. HRV at rest, HR reactivity, HR recovery, and mStroop scores did not predict treatment response. In conclusion, HRV recovery variables in the context of a standardized VR stressor were significant predictors of PTSD treatment response after controlling for baseline CAPS and number of combat experiences. The direction of this relationship indicates that greater baseline HRV recovery predicts lower 6-month PTSD symptom severity. This was an exploratory study in need of replication.

Autonomic Responses During Acute Anterior Versus Inferior Myocardial Infarction: A Systematic Review and Meta-Analysis.

Autonomic responses elicited by myocardial infarction varydepending on the site of injury, but accurate assessment using heart rate variability during the acute phase is limited. We systematically searched PubMed without language restrictions throughout July 2023. We reviewed studies reporting autonomic indices separately for anterior and inferior infarcts, followed by a meta-analysis of those reporting the standard deviation of the inter-beat interval between normal sinus beats during the initial 24 hours after the onset of symptoms. Six studies were included, comprising 341 patients (165 anterior, 176 inferior infarcts), all with satisfactory scores on the Newcastle-Ottawa quality scale. The estimated average of the standardized mean difference (based on the random-effects model) was -0.722 (95% confidence intervals: -0.943 to -0.501), which differed from zero (z=-6.416, p<0.0001). This finding indicates sympathetic and vagal dominance during acute anterior and inferior infarcts, respectively, with excessive responses likely contributing to early arrhythmogenesis. Despite the amelioration of autonomic dysfunction by revascularization, infarct location should be considered when commencing β-adrenergic receptor blockade, especially after delayed procedures.

Research on Arrhythmia Classification and Risk Degree Prediction based on Deep Neural Network and Convolutional Neural Network

In this study, a method of arrhythmia classification and risk prediction based on deep neural network and convolutional neural network (CNN) is proposed for ECG data. Electrocardiogram data record the electrophysiological activity of the heart, including normal heart beats and various arrhythmias. In order to monitor and identify arrhythmia in real time and accurately, this study used CNN model for data analysis. The characteristics of CNN, such as local perception, parameter sharing and multi-level feature extraction, make it perform well in ECG data analysis. The data comes from the ' Certification Cup ' Mathematics China Mathematical Modeling Network Challenge in 2023 and is preprocessed to meet the needs of the model. In the process of establishing and solving the model, the cross-entropy loss function is used to optimize, and the effectiveness and robustness of the model are verified by various evaluation methods. The results show that the model can accurately classify and predict the risk of arrhythmia, providing a powerful diagnostic tool for doctors and a valuable reference for future arrhythmia research.

Novel PVC detection algorithm in an implantable cardiac monitor for longitudinal risk monitoring

Abstract Background Guideline therapy is recommended in patients with frequent premature ventricular contractions (PVCs). Longitudinal monitoring of the PVC burden can effectively guide treatment options, stratify patient risk, and manage HF progression. Implantable cardiac monitors (ICMs) may provide a suitable long-term diagnostic tool to monitor PVC burden and guide clinical therapy. Objective Quantify the performance of a novel QRS morphology and timing-based PVC detection algorithm as implemented for an ICM. Methods The algorithm utilizes self-adapting timing-based ectopy detection and dynamic measures of QRS morphology to identify unifocal and multifocal PVCs. The training dataset consisted of 200 sECG episodes recorded in implanted BIOMONITOR (BM) devices, collected from 48 patients. The algorithm was then evaluated on 227 sixty-second duration sECG episodes from 79 BM patients in the CERTITUDE registry. Each beat was adjudicated as normal, PVC, or PAC by at least two clinical experts. In a total of 22,433 beats, 756 true PVCs were assessed (3.4% PVC prevalence). Overall, patient averaged, and generalized estimating equation (GEE) estimates for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated, as well as patient-specific PVC burden (PVCs/Total Beats). Results Patient-averaged PVC detection sensitivity was 73.8%, with a PPV of 89.2%, specificity of 99.6%, and NPV of 99.1%. Using a GEE model to adjust for multiple episodes per patient, the algorithm achieved detection sensitivity of 66.6±9.8% (95% CI) and PPV of 86.6±6.5%, with specificity of 99.5±0.4% and NPV of 99.0±0.5%. The main reasons for missed PVC detections were morphological similarities to normal beats, insufficient prematurity, or lack of a compensatory pause. Additionally, the algorithm faithfully represented the PVC burden, with a median difference of -0.24% between the PVC burden calculated by the detection algorithm and the adjudicated burden. Algorithm burden was highly correlated with the true PVC burden for each patient, with a Pearson correlation coefficient of 0.80 (p &amp;lt; 0.0001). Conclusions The PVC detection algorithm was highly specific, achieving a specificity of 99.6% and a sensitivity of 73.8%, and accurately representing the overall PVC burden for each patient. This PVC detection algorithm would provide a valuable clinical diagnostic marker to monitor PVC burden trends to guide therapy as well as assess the longitudinal risk of HF. Measurement of PVC burden combined with the 5.5-year longevity of the BIOMONITOR ICM could provide an innovative approach to optimize clinical management of patients.Example PVCs detected by the algorithm

Effect of crocin supplementation in the extender on the quality of chilled canine semen

The aim of the study was to evaluate the effects of crocin on canine sperm quality parameters during prolonged storage at 4 °C. Ejaculates from 10 dogs were diluted in a TRIS- egg yolk extender supplemented with 0 (control group), 0.5, 1, and 2 mM crocin and stored at 4 °C. Sperm membrane functional integrity, motility, and kinetics were assessed after 3 h, 24 h, 4 days and 7 days of storage. Based on the results, the more efficient concentration of crocin (0.5 mM) was chosen to evaluate sperm intracellular ROS levels, lipid peroxidation, and DNA fragmentation vs. the control. Semen with the addition of 0.5 mM crocin with respect to the control exhibited: i) increased (P < 0.05) sperm membrane functionality at 4 and 7 days of storage; ii) higher (P < 0.05) average path (VAP), straight-line velocities (VSL), and beat cross frequency (BCF) at 4 d of storage at 4 °C; iii) decreased (P < 0.05) intracellular ROS levels after 3 and 24 h storage. No differences in lipid peroxidation and DNA fragmentation were recorded between the control and C0.5 groups at any time point. Lipid peroxidation did not increase over time, while DNA fragmentation increased (P < 0.05) in both groups after 4 days of storage. The results demonstrated that the enrichment of extender with crocin improves to a certain extent canine semen quality, particularly after 4 days of storage at 4 °C.

Coupled oscillations of the Wilberforce pendulum unveiled by smartphones

The Wilberforce pendulum illustrates important properties of coupled oscillators including normal modes and beat phenomena. When helical spring is attached to a mass to create the Wilberforce pendulum, the longitudinal and torsional oscillations are coupled. A Wilberforce can be constructed simply from a standard laboratory spring, and a smartphone's accelerometer and gyroscope can be used to monitor the oscillations. We show that the resulting time-series data match theoretical predictions, and we share the procedures for observing both normal modes and beats.

Age-dependent contribution of intrinsic mechanisms to sinoatrial node function in humans

Average beat interval (BI) and beat interval variability (BIV) are primarily determined by mutual entrainment between the autonomic-nervous system (ANS) and intrinsic mechanisms that govern sinoatrial node (SAN) cell function. While basal heart rate is not affected by age in humans, age-dependent reductions in intrinsic heart rate have been documented even in so-called healthy individuals. The relative contributions of the ANS and intrinsic mechanisms to age-dependent deterioration of SAN function in humans are not clear. We recorded ECG on patients (n = 16 < 21 years and n = 23 41–78 years) in the basal state and after ANS blockade (propranolol and atropine) in the presence of propofol and dexmedetomidine anesthesia. Average BI and BIV were analyzed. A set of BIV features were tested to designated the “signatures” of the ANS and intrinsic mechanisms and also the anesthesia “signature”. In young patients, the intrinsic mechanisms and ANS mainly contributed to long- and short-term BIV, respectively. In adults, both ANS and intrinsic mechanisms contributed to short-term BIV, while the latter also contributed to long-term BIV. Furthermore, anesthesia affected ANS function in young patients and both mechanisms in adult. The work also showed that intrinsic mechanism features can be calculated from BIs, without intervention.

Mathematical birth of Early Afterdepolarizations in a cardiomyocyte model

Early Afterdepolarizations (EADs) are abnormal behaviors that can lead to cardiac failure and even cardiac death. In this paper we investigate the occurrence and development of these phenomena in a reduced Luo–Rudy cardiac model. Through a comprehensive dynamical analysis, we map out the distinct patterns observed in the parametric plane, differentiating between normal beats without EADs and pathological beats with EADs. By examining the bifurcation structure of the model, we elucidate the dynamical elements associated with these patterns and their transitions. Using a fast–slow analysis, we explore the emergence and evolution of EADs in the model. Notably, our approach combines the two commonly used fast–slow approaches (1-slow–2-fast and 2-slow–1-fast), and we show how both approaches together provide a more complete understanding of this phenomenon.

Soliton polarization dynamics in a fiber laser of zero dispersion.

Polarization properties of a soliton generated in a fiber laser of zero dispersion are investigated. Similar to the solitons generated in a fiber laser of all anomalous dispersion, the polarization ellipse of the soliton rotated during pulse evolution inside the cavity. The number of rotations relies on the cavity averaged birefringence with nonlinear bias. The larger the cavity averaged birefringence is, the bigger the bias is. When the period multiplying of solitons appears, the number of rotations depends on both multiplying periods and the cavity averaged birefringence. Multiple polarization states can be observed at a fixed position in the cavity depending on the multiplying period. When the cavity length is equal to n times of the averaged beat length, the polarization ellipse of the soliton rotates n∗m times at a fixed position, where m is equal to the multiplying period.