DeepMiceTL: a deep transfer learning based prediction of mice cardiac conduction diseases using early electrocardiograms.

Cardiac conduction disease is a major cause of morbidity and mortality worldwide. There is considerable clinical significance and an emerging need of early detection of these diseases for preventive treatment success before more severe arrhythmias occur. However, developing such early screening tools is challenging due to the lack of early electrocardiograms (ECGs) before symptoms occur in patients. Mouse models are widely used in cardiac arrhythmia research. The goal of this paper is to develop deep learning models to predict cardiac conduction diseases in mice using their early ECGs. We hypothesize that mutant mice present subtle abnormalities in their early ECGs before severe arrhythmias present. These subtle patterns can be detected by deep learning though they are hard to be identified by human eyes. We propose a deep transfer learning model, DeepMiceTL, which leverages knowledge from human ECGs to learn mouse ECG patterns. We further apply the Bayesian optimization and $k$-fold cross validation methods to tune the hyperparameters of the DeepMiceTL. Our results show that DeepMiceTL achieves a promising performance (F1-score: 83.8%, accuracy: 84.8%) in predicting the occurrence of cardiac conduction diseases using early mouse ECGs. This study is among the first efforts that use state-of-the-art deep transfer learning to identify ECG patterns during the early course of cardiac conduction disease in mice. Our approach not only could help in cardiac conduction disease research in mice, but also suggest a feasibility for early clinical diagnosis of human cardiac conduction diseases and other types of cardiac arrythmias using deep transfer learning in the future.

Prediction of incident atrial fibrillation using deep learning, clinical models and polygenic scores.

BACKGROUND AND AIMS: Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical and AF polygenic scores (PGS). METHODS: ECG in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with preexisting AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping datasets: 70% for training, 10% for validation, and 20% for testing. Performance of ECG-AI, clinical models and PGS was assessed in the test dataset. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital dataset. RESULTS: A total of 669,782 ECGs from 145,323 patients were included. Mean age was 61±15 years, and 58% were male. The primary outcome was observed in 15% of patients and the ECG-AI model showed an area under the receiver operating characteristic curve (AUC) of 0.78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval 4.02-4.57). In a subgroup analysis of 2,301 patients, ECG-AI outperformed CHARGE-AF (AUC=0.62) and PGS (AUC=0.59). Adding PGS and CHARGE-AF to ECG-AI improved goodness-of-fit (likelihood ratio test p<0.001), with minimal changes to the AUC (0.76-0.77). In the external validation cohort (mean age 59±18 years, 47% male, median follow-up 1.1 year) ECG-AI model performance= remained consistent (AUC=0.77). CONCLUSIONS: ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and polygenic scores.

QRS-T angle: is it a specific parameter associated with sudden cardiac death in type 2 diabetes? Results from the SURDIAGENE and the Mini-Finland prospective cohorts.

AIMS/HYPOTHESIS: Type 2 diabetes is associated with a high risk of sudden cardiac death (SCD), but the risk of dying from another cause (non-SCD) is proportionally even higher. The aim of the study was to identify easily available ECG-derived features associated with SCD, while considering the competing risk of dying from non-SCD causes. METHODS: In the SURDIAGENE (Survie, Diabete de type 2 et Genetique) French prospective cohort of individuals with type 2 diabetes, 15 baseline ECG parameters were interpreted among 1362 participants (mean age 65 years; HbA1c 62±17 mmol/mol [7.8±1.5%]; 58% male). Competing risk models assessed the prognostic value of clinical and ECG parameters for SCD after adjusting for age, sex, history of myocardial infarction, N-terminal pro b-type natriuretic peptide (NT-proBNP), HbA1c and eGFR. The prospective Mini-Finland cohort study was used to externally validate our findings. RESULTS: During median follow-up of 7.4 years, 494 deaths occurred including 94 SCDs. After adjustment, frontal QRS-T angle ≥90° (sub-distribution HR [sHR] 1.68 [95% CI 1.04, 2.69], p=0.032) and NT-proBNP level (sHR 1.26 [95% CI 1.06, 1.50] per 1 log, p=0.009) were significantly associated with a higher risk of SCD. Nevertheless, frontal QRS-T angle was the only marker not to be associated with causes of death other than SCD (sHR 1.08 [95% CI 0.84, 1.39], p=0.553 ). These findings were replicated in the Mini-Finland study subset of participants with diabetes (sHR 2.22 [95% CI 1.05, 4.71], p=0.04 for SCD and no association for other causes of death). CONCLUSIONS/INTERPRETATION: QRS-T angle was specifically associated with SCD risk and not with other causes of death, opening an avenue for refining SCD risk stratification in individuals with type 2 diabetes.

Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest.

BACKGROUND: Out-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation (VF) is associated with relatively poor survival. The ability to predict refractory VF (requiring ≥3 shocks) in advance of repeated shock failure could enable preemptive targeted interventions aimed at improving outcome, such as earlier administration of antiarrhythmics, reconsideration of epinephrine use or dosage, changes in shock delivery strategy, or expedited invasive treatments. METHODS: We conducted a cohort study of VF out-of-hospital cardiac arrest to develop an ECG-based algorithm to predict patients with refractory VF. Patients with available defibrillator recordings were randomized 80%/20% into training/test groups. A random forest classifier applied to 3-s ECG segments immediately before and 1 minute after the initial shock during cardiopulmonary resuscitation was used to predict the need for ≥3 shocks based on singular value decompositions of ECG wavelet transforms. Performance was quantified by area under the receiver operating characteristic curve. RESULTS: Of 1376 patients with VF out-of-hospital cardiac arrest, 311 (23%) were female, 864 (63%) experienced refractory VF, and 591 (43%) achieved functional neurological survival. Total shock count was associated with decreasing likelihood of functional neurological survival, with a relative risk of 0.95 (95% CI, 0.93-0.97) for each successive shock (P<0.001). In the 275 test patients, the area under the receiver operating characteristic curve for predicting refractory VF was 0.85 (95% CI, 0.79-0.89), with specificity of 91%, sensitivity of 63%, and a positive likelihood ratio of 6.7. CONCLUSIONS: A machine learning algorithm using ECGs surrounding the initial shock predicts patients likely to experience refractory VF, and could enable rescuers to preemptively target interventions to potentially improve resuscitation outcome.

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

Intracoronary ECG ST-Segment Shift Remission Time During Reactive Myocardial Hyperemia (Τ-icECG): A New Method to Assess Hemodynamic Coronary Stenosis Severity.

Background-Fractional flow reserve (FFR) measurements are recommended for assessing hemodynamic coronary stenosis severity. Intracoronary ECG (icECG) is easily obtainable and highly sensitive in detecting myocardial ischemia due to its close vicinity to the myocardium. We hypothesized that the remission time of myocardial ischemia on icECG after a controlled coronary occlusion accurately detects hemodynamically relevant coronary stenosis. Methods-This retrospective, observational study included patients with chronic coronary syndrome undergoing hemodynamic coronary stenosis assessment immediately following a strictly 1-minute proximal coronary artery balloon occlusion with simultaneous icECG recording. IcECG was used for a beat-to-beat analysis of the ST-segment shift during reactive hyperemia immediately following balloon deflation. The time from coronary balloon deflation until the ST-segment shift reached 37% of its maximum level, i.e., icECG ST-segment shift remission time(τ-icECG in seconds,s) was obtained by an automatic algorithm. τ-icECG was tested against the simultaneously obtained reactive hyperemia FFR at a threshold of 0.80 as reference parameter. Results-One hundred and thirty-nine icECGs from 120 patients (age 68±10 years) were analysed. Receiver operating characteristic (ROC) analysis of τ-icECG for the detection of hemodynamically relevant coronary stenosis at an FFR of ≤0.80 was performed. The area under the ROC curve was equal to 0.621(p=0.0363) at an optimal τ-icECG threshold of 8s(sensitivity 61%, specificity 67%). τ-icECG correlated inversely and linearly with FFR(p=0.0327). Conclusion-This first proof-of-concept study demonstrates that τ-icECG, a measure of icECG ST segment-shift remission after a 1-minute coronary artery balloon occlusion accurately detects hemodynamically relevant coronary artery stenosis according to FFR at a threshold of ≥8seconds.

Beating the heart failure odds: long-term survival after myocardial ischemia in juvenile rainbow trout.

Salmonid fish include some of the most valued cultured fish species worldwide. Unlike most other fish, the hearts of salmonids, including Atlantic salmon and rainbow trout, have a well-developed coronary circulation. Consequently, their hearts' reliance on oxygenation through coronary arteries leaves them prone to coronary lesions, believed to precipitate myocardial ischemia. Here, we mimicked such coronary lesions by subjecting groups of juvenile rainbow trout to coronary ligation, assessing histomorphological myocardial changes associated with ischemia and scarring in the context of cardiac arrhythmias using electrocardiography (ECG). Notable ECG changes resembling myocardial ischemia-like ECG in humans, such as atrioventricular blocks and abnormal ventricular depolarization (prolonged and fragmented QRS complex), as well as repolarization (long QT interval) patterns, were observed during the acute phase of myocardial ischemia. A remarkable 100% survival rate was observed among juvenile trout subjected to coronary ligation after 24 wk. Recovery from coronary ligation occurred through adaptive ventricular remodeling, coupled with a fast cardiac revascularization response. These findings carry significant implications for understanding the mechanisms governing cardiac health in salmonid fish, a family particularly susceptible to cardiac diseases. Furthermore, our results provide valuable insights into comparative studies on the evolution, pathophysiology, and ontogeny of vertebrate cardiac repair and restoration.NEW & NOTEWORTHY Juvenile rainbow trout exhibit a remarkable capacity to recover from cardiac injury caused by myocardial ischemia. Recovery from cardiac damage occurs through adaptive ventricular remodeling, coupled with a rapid cardiac revascularization response. These findings carry significant implications for understanding the mechanisms governing cardiac health within salmonid fishes, which are particularly susceptible to cardiac diseases.

Prevalence and prognostic relevance of electrocardiographic abnormalities among patients with ANCA-associated vasculitis.

OBJECTIVES: Current guidelines provide limited evidence for cardiovascular screening in ANCA-associated vasculitis (AAV). This study aimed to investigate the prevalence of electrocardiogram (ECG) abnormalities and associations between no, minor or major ECG abnormalities with cardiovascular mortality in AAV patients compared with matched controls. METHOD: Using a risk-set matched cohort design, patients diagnosed with granulomatosis with polyangiitis or microscopic polyangiitis with digital ECGs were identified from Danish registers from 2000-2021. Patients were matched 1:3 to controls without AAV on age, sex, and year of ECG measurement. Associated hazards of cardiovascular mortality according to ECG abnormalities were assessed in Cox regression models adjusted for age, sex, and comorbidities, with subsequent computation of 5-year risk of cardiovascular mortality standardized to the age- and sex-distribution of the sample. RESULTS: A total of 1431 AAV patients were included (median age: 69 years, 52.3% male). Median follow-up was 4.8 years. AAV was associated with higher prevalence of left ventricular hypertrophy (17.5% vs 12.5%), ST-T deviations (10.1% vs 7.1%), atrial fibrillation (9.6% vs 7.5%), and QTc prolongation (5.9% vs 3.6%). Only AAV patients with major ECG abnormalities demonstrated significantly elevated risk of cardiovascular mortality [HR 1.99 (1.49-2.65)] compared with controls. This corresponded to a 5-year risk of cardiovascular mortality of 19.14% (16-22%) vs 9.41% (8-11%). CONCLUSION: Patients with AAV demonstrated a higher prevalence of major ECG abnormalities than controls. Notably, major ECG abnormalities were associated with a significantly increased risk of cardiovascular mortality. These results advocate for the inclusion of ECG assessment into routine clinical care for AAV patients.

Accessory pathway localization with probabilistic density maps generated by a mobile application: Assessment of a full pre-excitation net-vector method.

INTRODUCTION: Precise electrocardiographic localization of accessory pathways (AP) can be challenging. Seminal AP localization studies were limited by complexity of algorithms and sample size. We aimed to create a nonalgorithmic method for AP localization based on color-coded maps of AP distribution generated by a web-based application. METHODS: APs were categorized into 19 regions/types based on invasive electrophysiologic mapping. Preexcited QRS complexes were categorized into 6 types based on polarity and notch/slur. For each QRS type in each lead the distribution of APs was visualized on a gradient map. The principle of common set was used to combine the single lead maps to create the distribution map for AP with any combination of QRS types in several leads. For the validation phase, a separate cohort of APs was obtained. RESULTS: A total of 800 patients with overt APs were studied. The application used the exploratory data set of 553 consecutive APs and the corresponding QRS complexes to generate AP localization maps for any possible combination of QRS types in 12 leads. Optimized approach (on average 3 steps) for evaluation of preexcited electrcardiogram was developed. The area of maximum probability of AP localization was pinpointed by providing the QRS type for the subsequent leads. The exploratory data set was validated with the separate cohort of APs (n = 256); p = .23 for difference in AP distribution. CONCLUSIONS: In the largest data set of APs to-date, a novel probabilistic and semi-automatic approach to electrocardiographic localization of APs was highly predictive for anatomic localization.

The positive F wave in lead V1 of typical atrial flutter is caused by activation of the right atrial appendage: Insight from mapping during entrainment from the right atrial appendage.

INTRODUCTION: Typical atrial flutter (AFL) is a macroreentrant tachycardia in which intracardiac conduction rotates counterclockwise around the tricuspid annulus. Typical AFL has specific electrocardiographic characteristics, including a negative sawtooth-like wave in the inferior lead and a positive F wave in lead V1. This study aimed to analyze the origin of the positive F wave in lead V1, which has not been completely understood. METHODS: This study enrolled 10 patients who underwent radiofrequency catheter ablation for a typical AFL. Electroanatomical mapping was performed both during typical AFL and entrainment from the right atrial appendage (RAA). The 12-lead electrocardiogram (ECG) and three-dimensional (3D) electroanatomical maps were analyzed. RESULTS: The positive F wave in lead V1 changed during entrainment from the RAA in all the cases. The 3D map during entrainment from the RAA revealed an area of antidromic capture around the RAA, which collided with the orthodromic wave in the anterior right atrium. This area of antidromic capture around the RAA was the only difference from the 3D electroanatomical map of AFL and is considered the cause of the change in the F wave in lead V1 during entrainment. CONCLUSION: The analysis of the differences in the 12-lead ECG and 3D maps between tachycardia and entrainment from the RAA clearly demonstrated that activation around the RAA is responsible for the generation of the positive F wave in lead V1 of typical AFL.

An artificial intelligence-enabled electrocardiogram algorithm for the prediction of left atrial low-voltage areas in persistent atrial fibrillation.

OBJECTIVES: We aimed to construct an artificial intelligence-enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low-voltage areas (LVAs) in patients with persistent atrial fibrillation. METHODS: The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12-lead ECGs obtained before the ablation procedures were performed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs. The DR-FLASH and APPLE clinical scores for LVA prediction were calculated. We used a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis to evaluate model performance. RESULTS: The data obtained from the participants were split into training (n = 469), validation (n = 58), and test sets (n = 60). LVAs were detected in 53.7% of all participants. Using ECG alone, the deep learning algorithm achieved an area under the ROC curve (AUROC) of 0.752, outperforming both the DR-FLASH score (AUROC = 0.610) and the APPLE score (AUROC = 0.510). The random forest classification model, which integrated a probabilistic deep learning model and clinical features, showed a maximum AUROC of 0.759. Moreover, the ECG-based deep learning algorithm for predicting extensive LVAs achieved an AUROC of 0.775, with a sensitivity of 0.816 and a specificity of 0.896. The random forest classification model for predicting extensive LVAs achieved an AUROC of 0.897, with a sensitivity of 0.862, and a specificity of 0.935. CONCLUSION: The deep learning model based exclusively on ECG data and the machine learning model that combined a probabilistic deep learning model and clinical features both predicted the presence of LVAs with a higher degree of accuracy than the DR-FLASH and the APPLE risk scores.

Electrocardiographic abnormalities in patients with cardiomyopathies.

Abnormalities in impulse generation and transmission are among the first signs of cardiac remodeling in cardiomyopathies. Accordingly, 12-lead electrocardiogram (ECG) of patients with cardiomyopathies may show multiple abnormalities. Some findings are suggestive of specific disorders, such as the discrepancy between QRS voltages and left ventricular (LV) mass for cardiac amyloidosis or the inverted T waves in the right precordial leads for arrhythmogenic cardiomyopathy. Other findings are less sensitive and/or specific, but may orient toward a specific diagnosis in a patient with a specific phenotype, such as an increased LV wall thickness or a dilated LV. A "cardiomyopathy-oriented" mindset to ECG reading is important to detect the possible signs of an underlying cardiomyopathy and to interpret correctly the meaning of these alterations, which differs in patients with cardiomyopathies or other conditions.

Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus.

BACKGROUND: Recent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM). Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs to improve the identification of SBHF. Accordingly, we sought whether machine learning model based on ewECG features was superior to NT-proBNP, as well as a conventional screening tool-the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM. METHODS: Participants in two clinical trials of SBHF (defined as diastolic dysfunction [DD], reduced global longitudinal strain [GLS ≤ 18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography. Supervised machine learning was adopted to identify the optimal combination of ewECG extracted features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF. RESULTS: SBHF was identified in 128 (72%) participants in the training dataset (median 72 years, 41% female) and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area under the curve (AUC) of 0.81 (95% CI 0.787-0.794) in identifying SBHF, significantly better than both NT-proBNP (AUC 0.56, 95% CI 0.44-0.68, p < 0.001) and ARIC HF (AUC 0.67, 95%CI 0.56-0.79, p = 0.002). ewECG features were also led to robust models screening for DD (AUC 0.74, 95% CI 0.73-0.74), reduced GLS (AUC 0.76, 95% CI 0.73-0.74) and LVH (AUC 0.90, 95% CI 0.88-0.89). CONCLUSIONS: Machine learning based modelling using additional ewECG extracted features are superior to NT-proBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confirm diagnosis. Trial registration LEAVE-DM, ACTRN 12619001393145 and Vic-ELF, ACTRN 12617000116325.

Hyperventilation testing in the diagnosis of vasospastic angina: A clinical review and meta-analysis.

BACKGROUND: Given the limited access to invasive vasospastic reactivity testing in Western Countries, there is a need to further develop alternative non-invasive diagnostic methods for vasospastic angina (VSA). Hyperventilation testing (HVT) is defined as a class IIa recommendation to diagnose VSA by the Japanese Society of Cardiology. METHODS: In this systematic review and meta-analysis reported according to the PRISMA statement, we review the mechanisms, methods, modalities and diagnostic accuracy of non-invasive HVT for the diagnostic of VSA. RESULTS: A total of 106 articles published between 1980 and 2022 about VSA and HVT were included in the systematic review, among which 16 were included in the meta-analysis for diagnostic accuracy. Twelve electrocardiogram-HVT studies including 804 patients showed a pooled sensitivity of 54% (95% confidence intervals [CI]; 30%-76%) and a pooled specificity of 99% (95% CI; 88%-100%). Four transthoracic echocardiography-HVT studies including 197 patients revealed a pooled sensitivity of 90% (95% CI; 82%-94%) and a pooled specificity of 98% (95% CI; 86%-100%). Six myocardial perfusion imaging-HVT studies including 112 patients yielded a pooled sensitivity of 95% (95% CI; 63%-100%) and a pooled specificity of 78% (95% CI; 19%-98%). Non-invasive HVT resulted in a low rate of adverse events, ventricular arrhythmias being the most frequently reported, and were resolved with the administration of nitroglycerin. CONCLUSIONS: Non-invasive HVT offers a safe alternative with high diagnostic accuracy to diagnose VSA in patients with otherwise undiagnosed causes of chest pain.

Detection of focal to bilateral tonic-clonic seizures using a connected shirt.

OBJECTIVE: This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. METHODS: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). RESULTS: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. SIGNIFICANCE: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.

Cardioprotective role of a magnolol and honokiol complex in the prevention of doxorubicin-mediated cardiotoxicity in adult rats.

Doxorubicin (DOXO) induces marked cardiotoxicity, though increased oxidative stress while there are some documents related with cardioprotective effects of some antioxidants against organ-toxicity during cancer treatment. Although magnolia bark has some antioxidant-like effects, its action in DOXO-induced heart dysfunction has not be shown clearly. Therefore, here, we aimed to investigate the cardioprotective action of a magnolia bark extract with active component magnolol and honokiol complex (MAHOC; 100 mg/kg) in DOXO-treated rat hearts. One group of adult male Wistar rats was injected with DOXO (DOXO-group; a cumulative dose of 15 mg/kg in 2-week) or saline (CON-group). One group of DOXO-treated rats was administered with MAHOC before DOXO (Pre-MAHOC group; 2-week) while another group was administered with MAHOC following the 2-week DOXO (Post-MAHOC group). MAHOC administration, before or after DOXO, provided full survival of animals during 12-14 weeks, and significant recoveries in the systemic parameters of animals such as plasma levels of manganese and zinc, total oxidant and antioxidant statuses, and also systolic and diastolic blood pressures. This treatment also significantly improved heart function including recoveries in end-diastolic volume, left ventricular end-systolic volume, heart rate, cardiac output, and prolonged P-wave duration. Furthermore, the MAHOC administrations improved the structure of left ventricles such as recoveries in loss of myofibrils, degenerative nuclear changes, fragmentation of cardiomyocytes, and interstitial edema. Biochemical analysis in the heart tissues provided the important cardioprotective effect of MAHOC on the redox regulation of the heart, such as improvements in activities of glutathione peroxidase and glutathione reductase, and oxygen radical-absorbing capacity of the heart together with recoveries in other systemic parameters of animals, while all of these benefits were observed in the Pre-MAHOC treatment group, more prominently. Overall, one can point out the beneficial antioxidant effects of MAHOC in chronic heart diseases as a supporting and complementing agent to the conventional therapies.

Heart rate reduction during voluntary deep diving in free-ranging loggerhead sea turtles.

Air-breathing vertebrates exhibit cardiovascular responses to diving including heart rate reduction (diving bradycardia). Field studies on aquatic mammals and birds have shown that the intensity of bradycardia can vary depending on diving behaviour, such as the depth of dives and dive duration. However, in aquatic reptiles, the variation in heart rate during deep dives under natural conditions has not been fully investigated. In this study, we released five loggerhead sea turtles (Caretta caretta) outfitted with recorders into the sea and recorded their electrocardiogram, depth, water temperature and longitudinal acceleration. After 3 days, the recorders automatically detached from the turtles. The heart rate signals were detected from the electrodes placed on the surface of the plastron. The mean (±s.d.) heart rate of 12.8±4.1 beats min-1 during dives was significantly lower than that of 20.9±4.1 beats min-1 during surface periods. Heart rate during dives varied with dive depth, although it remained lower than that at the surface. When the turtle dived deeper than 140 m, despite the relatively high flipper stroke rate (approximately 19 strokes min-1), the heart rate dropped rapidly to approximately 2 beats min-1 temporarily. The minimum instantaneous heart rate during dives was lower at deeper dive depths. Our results indicate that loggerhead sea turtles show variations in the intensity of diving bradycardia depending on their diving behaviour, similar to that shown by marine mammals and birds.

Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

Brugada syndrome precipitated by uncomplicated malaria treated with dihydroartemisinin piperaquine: a case report.

BACKGROUND: Cardiovascular events following anti-malarial treatment are reported infrequently; only a few studies have reported adverse outcomes. This case presentation emphasizes cardiological assessment of Brugada syndrome, presenting as life-threatening arrhythmia during anti-malarial treatment. Without screening and untreated, this disease may lead to sudden cardiac death. CASE PRESENTATION: This is a case of 23-year-old male who initially presented with palpitations followed by syncope and shortness of breath with a history of malaria. He had switched treatment from quinine to dihydroartemisinin-piperaquine (DHP). Further investigations revealed the ST elevation electrocardiogram pattern typical of Brugada syndrome, confirmed with flecainide challenge test. Subsequently, anti-malarial treatment was stopped and an Implantable Cardioverter Defibrillator (ICD) was inserted. CONCLUSIONS: Another possible cause of arrhythmic events happened following anti-malarial consumption. This case highlights the possibility of proarrhytmogenic mechanism of malaria infection and anti-malarial drug resulting in typical manifestations of Brugada syndrome.

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS: We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). RESULTS: Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). CONCLUSION: Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.