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Antioxidants play an important role in protecting cardiac arrhythmias. Silymarin, strong antioxidant, is effective in reducing the complications caused by arrhythmias. This study was conducted to determine the effect of silymarin on the prevention and treatment of calcium chloride-induced arrhythmia. In total, 48 male rats were randomly divided into six groups: the first control group for acute administration received intravenous injection of 0.2 mL of dimethylsulfoxide, a cosolvent, immediately after induction of arrhythmia; the second control group for chronic administration, daily gavage of dimethylsulfoxide for 2 weeks before induction of arrhythmia; acute silymarin group, 100 mg/kg intravenous, immediately after the occurrence of arrhythmia; chronic silymarin group, daily gavage of 50 mg/kg for 2 weeks before induction of arrhythmia; amiodarone standard treatment, 5 mg/kg intravenous, immediately after induction of arrhythmia; and quinidine standard treatment, 10 mg/kg intravenous, immediately after induction of arrhythmia. Calcium chloride (140 mg/kg, i.v.) was used to induce arrhythmia. Electrocardiogram was recorded and monitored by PowerLab™ system. The incidence rates of premature ventricular beat (PVB), ventricular tachycardia (VT), and ventricular fibrillation (VF) were calculated. The antiarrhythmic effect of silymarin was observed with a significant decrease in the incidence of premature ventricular beat (22.56 ± 1.04%, P < 0.001), ventricular tachycardia (34.150 ± 1.59%, P < 0.001), and ventricular fibrillation (24.31 ± 1.02%, P < 0.001) compared with the control group (100%). These effects were comparable to antiarrhythmic drugs such as quinidine (29.23% ± 1.24%, 52.23% ± 1.13%, 66.31% ± 1.81%) and amiodarone (22.91% ± .72%, 41.09% ± 1.66%, 61.59% ± 1.11%). Silymarin exerts a potent antioxidant effect, thereby mitigating the risk of VT, VF, and PVC.

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Background: Bleeding is a serious cause of hypotension and tachycardia after childbirth and should always be considered. Case presentation: A healthy woman in her thirties who had previously undergone caesarean section, underwent induction and operative vaginal delivery. Postpartum, she experienced chest pain, hypotension and tachycardia, and had signs of ischaemia on electrocardiogram. A CT scan showed a large intraperitoneal haematoma. The patient underwent immediate laparotomy and received a massive blood transfusion. However, no large haematoma was found. The chest pain was attributed to a myocardial infarction caused by hypovolaemic shock. After discharge, the patient experienced significant vaginal bleeding and was transferred to a different university hospital. A CT scan revealed a large retroperitoneal haematoma. Emergency surgery was performed based on the suspicion of active bleeding, but only an older haematoma was found. Re-evaluation of the initial CT scan revealed that the haematoma was in fact located retroperitoneally and was thereby not found in the first operation. Interpretation: This case highlights the importance of bleeding as an important cause in unstable postpartum patients. Additionally, it is a reminder that retroperitoneal haematomas can occur in obstetric patients and can mask typical symptoms of uterine rupture such as abdominal pain. also hindering perioperative diagnosis.

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BACKGROUND: Heart rate variability (HRV) has been reported to be associated with cardiovascular diseases (CVD), while few studies focused on the instantaneous heart rate (IHR). This study aimed to establish models to predict the occurrence of cardiovascular events based on the IHR sequence. METHODS: A total of 2977 participants with useful electrocardiogram (ECG) data and free of CVD events at baseline from the Sleep Heart Health Study (SHHS) database were included in this retrospective cohort study. All IHR indicators were measured during the awake period before sleep. The logistic regression, random forest, and XGBoost methods were used to develop the predictive models. The model performance was quantified by calculating the area under the curve (AUC). RESULTS: Of theses 2977 participants, 1460 (49.04%) participants had CVD events during the 15-year follow-up. Higher standard deviation of IHR (SDHR) (OR=0.906; 95% CI, 0.832-0.986), coefficient of variation of IHR (CVHR) (OR=0.910; 95% CI, 0.835-0.990), power in low frequency (LF) (OR=0.896; 95% CI, 0.822-0.975), power in high frequency (HF) (OR=0.872; 95% CI, 0.796-0.955), and total power (TP) (OR=0.887; 95% CI, 0.813-0.967) were associated with the lower risk of CVD events, while ratio of semi-minor axis and semi-major axis in Poincare plot (SDratio) (OR=1.105; 95% CI, 1.012-1.206) was related to the higher risk of CVD events. The AUCs of the logistic regression, random forest, and the XGBoost models were 0.734 (95% CI, 0.701-0.767), 0.794 (95% CI, 0.764-0.823) and 0.828 (95% CI, 0.801-0.855) in the testing set, respectively. CONCLUSIONS: IHR sequences were important predictors of cardiovascular events. The IHR indicators should be paid more attention to in future clinical researches on CVD.

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BACKGROUND: Degenerative severe aortic valve stenosis (AS) is increasingly prevalent in the aging population, leading to the adoption of transcatheter aortic valve replacement (TAVR) as a less invasive alternative. While TAVR indications have expanded, the procedure is associated with a substantial incidence of major adverse cardiac events (MACE). The study aims to establish a preoperative risk-stratification system for TAVR candidates based on Sokolow-Lyon voltage (SLV) and other relevant factors. METHODS: A total of 181 consecutive patients who underwent TAVR were retrospectively reviewed. Baseline characteristics, preoperative electrocardiogram (ECG) and echocardiography findings, and TAVR procedures were assessed. Low SLV (<3.5 mV) was defined based on ECG measurements. RESULTS: Baseline characteristics revealed a mean age of 84 years, with 71.8% females. The two-year incidence of MACE defined as a composite of cardiac death and hospitalization due to heart failure, was 11.6%, significantly higher in the low SLV group. Low SLV emerged as an independent prognostic factor. The Tokyo Bay Risk (TBR) Score, including low SLV, Body Mass Index <18.5 kg/m2, and previous coronary artery disease, effectively stratified MACE risk. Higher TBR scores (2 or 3) correlated with increased MACE risk. CONCLUSIONS: Patients with low SLV in pre-procedural ECG demonstrated a heightened risk of two-year MACE. The TBR score, incorporating low SLV, proved valuable for preoperative risk assessment. Careful consideration of TAVR indications, along with TBR score integration, is crucial for optimizing outcomes.

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Vasospastic angina (VSA) was first described in 1959 by Myron Prinzmetal as "the variant form of angina pectoris" on the sole basis of medical history and ECG. This condition is currently categorized as an endotype of myocardial infarction without coronary obstruction (Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA)). Diagnostic criteria have been suggested by expert consensus. Provocative testing during coronary angiography is the gold standard test but is rarely used. The clinical presentation is often neglected, and the diagnosis is missed. However, VSA may lead to life-threatening arrhythmias. There are simple and effective therapies that are markedly different from those for the atherosclerotic coronary artery disease.

Le vasospasme coronarien (VC) a été décrit pour la première fois en 1959 par Myron Prinzmetal comme « la forme variante de l'angine de poitrine ¼ sur la seule base de l'anamnèse et de l'ECG. Le VC est actuellement classé comme un endotype de l'infarctus du myocarde sans obstruction coronaire (Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA)). Des critères diagnostiques ont été proposés par des consensus d'experts. Le test de provocation lors de la coronarographie est l'examen de choix mais est rarement employé. La symptomatologie est souvent méconnue et le diagnostic n'est pas suffisamment évoqué. Pourtant, le VC peut conduire à des arythmies potentiellement fatales. Nous disposons de moyens thérapeutiques simples et efficaces, qui diffèrent sensiblement de ceux de la maladie coronarienne athérosclérotique.

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Sudden cardiac arrest/death in pediatric patients is a rare but potentially preventable event. Cardiomyopathies and channelopathies are the most common causes which are detectable with ECG and transthoracic echocardiography in asymptomatic subjects. Coronary artery anomalies are a rare cause of sudden cardiac arrest/death, but these events suggest that ECG and echocardiography, focused on the site of origin of the coronary arteries, should be both part of the screening tool of young athletes. Finally, the rare cardiac arrest events in young patients with ventricular preexcitation without prior symptoms or markers of high risk suggest that transcatheter ablation should be considered in all pediatric patients with ventricular preexcitation because it can eliminate the small long-term risk of sudden cardiac arrest/death, but a careful consideration of the most appropriate timing is mandatory.

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BACKGROUND: Timely diagnosis and treatment for ST-elevation myocardial infarction (STEMI) requires a coordinated response from multiple providers. Rapid intervention is key to reducing mortality and morbidity. Activation of the cardiac catheterization laboratory may occur through verbal communication and may also involve the secure sharing of electrocardiographic images between frontline health care providers and interventional cardiologists. To improve this response, we developed a quick, easy-to-use, privacy-compliant smartphone app, that is SMART AMI-ACS (Strategic Management of Acute Reperfusion and Therapies in Acute Myocardial Infarction Acute Coronary Syndromes), for real-time verbal communication and sharing of electrocardiographic images among health care providers in Ontario, Canada. The app further provides information about diagnosis, management, and risk calculators for patients presenting with acute coronary syndrome. OBJECTIVE: This study aims to integrate the app into workflow processes to improve communication for STEMI activation, resulting in decreased treatment times, improved patient outcomes, and reduced unnecessary catheterization laboratory activation and transfer. METHODS: Implementation of the app will be guided by the Reach, Effectiveness, Acceptability, Implementation, and Maintenance (RE-AIM) framework to measure impact. The study will use quantitative registry data already being collected through the SMART AMI project (STEMI registry), the use of the SMART AMI app, and quantitative and qualitative survey data from physicians. Survey questions will be based on the Consolidated Framework for Implementation Research. Descriptive quantitative analysis and thematic qualitative analysis of survey results will be conducted. Continuous variables will be described using either mean and SD or median and IQR values at pre- and postintervention periods by the study sites. Categorical variables, such as false activation, will be described as frequencies (percentages). For each outcome, an interrupted time series regression model will be fitted to evaluate the impact of the app. RESULTS: The primary outcomes of this study include the usability, acceptability, and functionality of the app for physicians. This will be measured using electronic surveys to identify barriers and facilitators to app use. Other key outcomes will measure the implementation of the app by reviewing the timing-of-care intervals, false "avoidable" catheterization laboratory activation rates, and uptake and use of the app by physicians. Prospective evaluation will be conducted between April 1, 2022, and March 31, 2023. However, for the timing- and accuracy-of-care outcomes, registry data will be compared from January 1, 2019, to March 31, 2023. Data analysis is expected to be completed in Fall 2024, with the completion of a paper for publication anticipated by the end of 2024. CONCLUSIONS: Smartphone technology is well integrated into clinical practice and widely accessible. The proposed solution being tested is secure and leverages the accessibility of smartphones. Emergency medicine physicians can use this app to quickly, securely, and accurately transmit information ensuring faster and more appropriate decision-making for STEMI activation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05290389; https://clinicaltrials.gov/study/NCT05290389. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55506.

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The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial-temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization.

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BACKGROUND: There are few studies on the safety of sub-maximal exercise testing of aerobic exercise in apparently healthy Chinese populations. The purpose of this study was to explore the frequency of exercise electrocardiography (ECG) abnormalities and the corresponding exercise intensities, as well as the associated influencing factors, during a symptom-limited stepwise incremental cardiopulmonary exercise test (CPET) in an apparently healthy Chinese population. METHODS: A cross-sectional study was done in four communities, one urban and one rural in the North (Beijing) and in the South (Hezhou, Guangxi) of China from 1 January 2017 to 31 December 2018, respectively. Total of 1642 participants was recruited, 918 were eligible and completed demographic indicators, routine blood indicators, physical activity status, symptom-limited CPET and exercise ECG were included in the analysis. RESULTS: Of the exercise ECG outcomes, 10 (1.1%) were positive and occurred at exercise intensities ≥ 62.50% heart rate reserve (HRR); 44 (4.8%) were equivocal and 864 (94.1%) were normal. Individuals with Cardiovascular Disease Risk Factor (CVDRF) = 3-4 were 2.6 times more likely to have a equivocal and abnormal exercise ECG than those with CVDRF = 0-2. Exercise ECGs of individuals with CVDRF = 5-7 were 5.4 times more likely to be positive and abnormal than exercise ECGs of individuals with CVDRF = 0-2. CONCLUSIONS: The exercise intensity of 62.5% HRR can be used as a safe upper limit for safe participation in exercise in apparently healthy Chinese population; the greater the number of CVDRFs, the greater the likelihood of cardiovascular risk during exercise.

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The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

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Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

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Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

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BACKGROUND: Kounis syndrome is defined as the concurrence of acute coronary syndromes in the setting of allergic or anaphylactic reactions. It primarily affects men aged 40-70 years and is often associated with chest pain. This syndrome is often unrecognized and undiagnosed in clinical practice due to a low level of awareness. Herein, we present a case of type I Kounis syndrome in a young woman without chest pain. CASE PRESENTATION: A 28-year-old Japanese woman with a history of atopic dermatitis received a glycyrrhizin, glutathione, and neurotropin preparation (a preparation of inflamed skin extract from rabbits inoculated with vaccinia virus) at a dermatology clinic to treat pruritus caused by atopic dermatitis. Immediately after the administration, the patient developed abdominal pain and generalized body wheals. The patient was diagnosed with anaphylaxis and was transported to our hospital. She had no chest pain on arrival at our hospital; however, a 12-lead electrocardiogram showed ST elevation in leads I, aVL, V2, and V3, and an echocardiogram showed decreased wall motion in the anterior and lateral walls of the left ventricle. Sublingual nitroglycerin administration improved ST-segment elevation and left ventricular wall motion abnormalities. The patient underwent emergency coronary angiography, which revealed no significant stenosis, and was diagnosed with type I Kounis syndrome. CONCLUSION: Kounis syndrome without chest pain is rare in young women. Since it can be fatal in cases with severe allergic symptoms such as anaphylaxis, the possibility of concurrent acute coronary syndrome should be considered when treating systemic allergic reactions, regardless of age, sex, or the presence or absence of chest symptoms.

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Variations in interoceptive signals from the baroreceptors (BRs) across the cardiac and respiratory cycle can modulate cortical excitability and so affect awareness. It remains debated at what stages of processing they affect awareness-related event-related potentials (ERPs) in different sensory modalities. We investigated the influence of the cardiac (systole/diastole) and the respiratory (inhalation/exhalation) phase on awareness-related ERPs. Subjects discriminated visual threshold stimuli while their electroencephalogram, electrocardiogram, and respiration were simultaneously recorded. We compared ERPs and their intracranial generators for stimuli classified correctly with and without awareness as a function of the cardiac and respiratory phase. Cyclic variations of interoceptive signals from the BRs modulated both the earliest electrophysiological markers and the trajectory of brain activity when subjects became aware of the stimuli: an early sensory component (P1) was the earliest marker of awareness for low (diastole/inhalation) and a perceptual component (visual awareness negativity) for high (systole/exhalation) BR activity, indicating that BR signals interfere with the sensory processing of the visual input. Likewise, activity spread from the primary visceral cortex (posterior insula) to posterior parietal cortices during high and from associative interoceptive centers (anterior insula) to the prefrontal cortex during low BR activity. Consciousness is thereby resolved in cognitive/associative regions when BR is low and in perceptual centers when it is high. Our results suggest that cyclic fluctuations of BR signaling affect both the earliest markers of awareness and the brain processes underlying conscious awareness.

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Zebrafish and their mutant lines have been extensively used in biomedical investigations, cardiovascular studies, and drug screening. In the current study, the commercial version of the novel system, Zebra II, is presented. The protocol demonstrates electrocardiogram (ECG) acquisition and analysis from multiple zebrafish within controllable working environments. The device is composed of an external and independent perfusion system, a 4-point electrode, temperature sensors, and an embedded electronic system. In previous studies, the device prototype underwent validation against the established iWORX system through several tests, demonstrating similar data quality and ECG response to drug interventions. Following this, the study delved into examining the impact of anesthetic drugs and temperature fluctuations on zebrafish ECG, necessitating instant data evaluation. Thanks to the apparatus's capacity for consistent delivery of anesthetics and drugs, it was possible to extend ECG data collection up to 1 h, markedly longer than the 5 min duration supported by current systems. This paper introduces a pioneering, cloud-based, automated analysis utilizing data from four zebrafish, offering an efficient method for conducting combination experiments and significantly reducing time and effort. The system proved effective in capturing and analyzing ECG, especially in detecting drug-induced arrhythmias in wild-type zebrafish. Additionally, the capability to gather data across multiple channels facilitated the execution of randomized controlled trials with zebrafish models. The developed ECG system overcomes existing limitations, showing the potential to greatly expedite drug discovery and cardiovascular research involving zebrafish.

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The dorsal root ganglia (DRG), housing primary sensory neurons, transmit somatosensory and visceral afferent inputs to the dorsal horn of the spinal cord. They play a pivotal role in both physiological and pathological states, including neuropathic and visceral pain. In vivo calcium imaging of DRG enables real-time observation of calcium transients in single units or neuron ensembles. Accumulating evidence indicates that DRG neuronal activities induced by somatic stimulation significantly affect autonomic and visceral functions. While lumbar DRG calcium imaging has been extensively studied, thoracic segment DRG calcium imaging has been less explored due to surgical exposure and stereotaxic fixation challenges. Here, we utilized in vivo calcium imaging at the thoracic1 dorsal root ganglion (T1-DRG) to investigate changes in neuronal activity resulting from somatic stimulations of the forelimb. This approach is crucial for understanding the somato-cardiac reflex triggered by peripheral nerve stimulations (PENS), such as acupuncture. Notably, synchronization of cardiac function was observed and measured by electrocardiogram (ECG), with T-DRG neuronal activities, potentially establishing a novel paradigm for somato-visceral reflex in the thoracic segments.

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Takotsubo cardiomyopathy (TCM) is characterized by transient left ventricular dysfunction, diagnosed via echocardiography or left ventriculography. In most cases, TCM involves an emotional, physical, or combined trigger. Acute coronary syndrome is one of the most frequent misdiagnoses in TCM patients due to electrocardiogram (ECG) abnormalities and elevated cardiac biomarkers. Typically, coronary angiography reveals no stenosis or occlusion of the coronary arteries. Hypertrophic cardiomyopathy (HCM) is a distinct pathology characterized by a hypertrophied left ventricle with various phenotypes. However, some reports have described TCM cases mimicking obstructive-type HCM in some patients. We present a case of a female patient diagnosed with TCM based on clinical, laboratory, and imaging tests. Differentiating TCM from HCM was challenging due to ventriculography and echocardiography findings, as hyperdynamic contraction of the basal segments of the left ventricle caused an increased left ventricular outflow tract (LVOT) gradient and severe mitral valve regurgitation. Detailed evaluation and close echocardiographic follow-up are essential in such rare cases.

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