Enhancement of central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting the beta-1 adrenergic receptor (β1-AR) locating in the cardiomyocytes in the DBA/1 mouse SUDEP model

Sudden unexpected death of epilepsy (SUDEP) is the key cause of of death in patients with epilepsy. Due to the complicated pathogenesis of SUDEP, however, the exact mechanism of SUDEP remains elusive. Currently, although it is recognized that the seizure-induced respiratory arrest (S-IRA) may be a main cause for SUDEP, other factors resulting in SUDEP can not be excluded e.g arrhythmias. Our previous findings indicated that the incidence of seizure-induced respiratory arrest S-IRA and SUDEP evoked by acoustic stimulation or pentetrazol (PTZ) injection was significantly reduced by atomoxetine, a norepinephrine reuptake inhibitor (NRI), suggesting that noradrenergic neurotransmission modulates S-IRA and SUDEP. Given that norepinephrine acts on the central and peripheral target to modulate respiratory and circulation function by targeting adrenergic receptor α and beta (a-AR and β-AR) and the arrhythmias can be contributed to SUDEP. Meanwhile, to further test whether cardiac factors are implicated in S-IRA and SUDEP, we choose esmolol hydrochloride, a selective antagonist of beta-1 adrenergic receptor (β1-AR) to test it in our models. Our findings demonstrated that the lower incidence of S-IRA and SUDEP evoked by acoustic stimulation or PTZ in DBA/1 mice by administration with atomoxetine was significantly reversed by intraperitoneal (IP) of esmolol hydrochloride. Importantly, the data of electrocardiogram (ECG) showed that the cardiac arrhythmia evoked by acoustic stimulation including the ventricular tachycardia, ventricular premature beat and atrioventricular block and administration of atomoxetine significantly reduced theses arrhythmias and the incidence of S-IRA and SUDEP in our models. Thus, the dysfunction of respiratory and circulation may be implicated in the pathogenesis of S-IRA and SUDEP hand in hand and enhancing central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting β1-AR locating in the cardiomyocytes. Our findings will show a new light on decoding the pathogenesis of SUDEP. Keywords: sudden unexpected death in epilepsy (SUDEP); seizure-induced respiratory arrest S-IRA); esmolol hydrochloride (Esmolol); Electrocardiogram (ECG); locus coeruleus (LC); cardiac arrhythmia; pentetrazol (PTZ)

Attenuation and Structural Transformation of Crassicauline A During Sand Frying Process and Antiarrhythmic Effects of its Transformed Products

To ensure safety and efficacy, most Aconitum herbs should be processed before clinical application. The processing methods include boiling, steaming, and sand frying. Among these methods, the transformation pathways of diterpenoid alkaloids in the process of sand frying are more complicated. Therefore, crassicauline A, a natural product with two ester bonds, was chosen as the experimental object. Consequently, a known alkaloid, together with three new alkaloids, was derived from crassicauline A. Meanwhile, the cardiotoxicity of converted products was reduced compared with their parent compound. Interestingly, some diterpenoid alkaloids have similar structures but opposite effects, such as arrhythmia and antiarrhythmic. Considering the converted products are structural analogues of crassicauline A, herein, the antiarrhythmic activity of the transformed products was further investigated. In a rat aconitine-induced arrhythmia assay, the three transformed products, which could dose-dependently delay the ventricular premature beat (VPB) incubation period, reduce the incidence of ventricular tachycardia (VT), combined with the increasing arrhythmia inhibition rate, exhibited prominent antiarrhythmic activities. Our experiments speculated that there might be at least two transformation pathways of crassicauline A during sand frying. The structure-activity data established in this paper constructs the critical pharmacophore of diterpenoid alkaloids as antiarrhythmic agents, which could be helpful in searching for the potential drugs that are equal or more active and with lower toxicity, than currently clinical used antiarrhythmic drugs.

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.

Acquired long QT syndrome, manifesting in the first complex after a ventricular extrasystole

A case report of unusual QT interval prolongation after ventricular premature beat is presented.

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study

Background With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. Objective The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. Methods Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. Results The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. Conclusions This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research.

Significant abnormal glycemic variability increased the risk for arrhythmias in elderly type 2 diabetic patients

Background Little is known about whether the influence of glycemic variability on arrhythmia is related to age in type 2 diabetes mellitus (T2DM). Therefore, we aimed to compare the association between glycemic variability and arrhythmia in middle-aged and elderly T2DM patients. Methods A total of 107 patients were divided into two groups: elderly diabetes mellitus group (EDM, n = 73) and middle-aged diabetes mellitus group (MDM, n = 34). The main clinical data, continuous glucose monitoring (CGM) and dynamic ECG reports were collected. The parameters including standard deviation of blood glucose (SDBG), largest amplitude of glycemic excursions (LAGE), mean amplitude of glycemic excursions (MAGE), absolute means of daily differences (MODD), time in range (TIR), time below range (TBR), time above range (TAR), coefficient of variation (CV) were tested for glycemic variability evaluation. Results In terms of blood glucose fluctuations, MAGE (5.77 ± 2.16 mmol/L vs 4.63 ± 1.89 mmol/L, P = 0.026), SDBG (2.39 ± 1.00 mmol/L vs 2.00 ± 0.82 mmol/L, P = 0.048), LAGE (9.53 ± 3.37 mmol/L vs 7.84 ± 2.64 mmol/L, P = 0.011) was significantly higher in EDM group than those of MDM group. The incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat were significantly higher in EDM group compared with the MDM group (all P < 0.05). Among patients with hypoglycemia events, the incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat (all P < 0.05) were significantly higher in the EDM group than those in the MDM group. In EDM group, TIR was negatively correlated with atrial tachycardia in the MAGE1 layer and with atrial tachycardia and ventricular premature beat in the MAGE2 layer, TBR was significantly positively correlated with atrial tachycardia in the MAGE2 layer (all P < 0.05). In MDM group, TAR was positively correlated with ventricular premature beat and atrial tachycardia in the MAGE2 layer (all P < 0.05). Conclusions The study demonstrated the elderly patients had greater glycemic variability and were more prone to arrhythmias. Therefore, active control of blood glucose fluctuation in elderly patients will help to reduce the risk of severe arrhythmia.

Treatment of refractory ventricular premature beat from the left ventricle summit using intramyocardial retrograde transvenous ethanol administration

A clinical observation of intramyocardial transvenous ethanol administration for the treatment of refractory ventricular extrasystole is presented. The procedure was carried out as part of a prospective international multicenter study “Intramural venous ethanol infusion for refractory ventricular arrhythmias”.

Significant Abnormal Glycemic Variability Increased the Risk for Arrhythmias in Middle-Aged and Elderly Type 2 Diabetic Patients

Background: Little is known about whether the influence of glycemic variability on arrhythmia is related to age in type 2 diabetes mellitus (T2DM). Therefore, we aimed to compare the association between glycemic variability and arrhythmia in middle-aged and elderly T2DM patients. Methods: A total of 107 patients were divided into two groups: elderly diabetes mellitus group (EDM, n=73) and middle-aged diabetes mellitus group (MDM, n=34). The main clinical data, continuous glucose monitoring (CGM) and dynamic ECG reports were collected. The parameters including standard deviation (SDBG), largest amplitude of glycemic excursions (LAGE), mean amplitude of glycemic excursions (MAGE), absolute means of daily differences (MODD) were tested for glycemic variability evaluation. Results: In terms of blood glucose fluctuations, MAGE (5.77±2.16mmol/L vs 4.63 ±1.89mmol/L, P=0.026), SDBG (2.39±1.00mmol/L vs 2.00±0.82mmol/L, P=0.048), LAGE (9.53±3.37mmol/L vs 7.84±2.64 mmol/L, P=0.011) was significantly higher in EDM group than those of MDM group. The incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat were significantly higher in EDM group compared with the MDM group (all P<0.05). Among patients with hypoglycemia events, the incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat (all P<0.05) were significantly higher in the EDM group than those in the MDM group. Conclusions: The study demonstrated the elderly patients had greater glycemic variability and were more prone to arrhythmias. Therefore, active control of blood glucose fluctuation in elderly patients will help to reduce the risk of severe arrhythmia.

Efficacy and Safety of Pemetrexed Combined with Lenalidomide in the Treatment of Relapsed/Refractory Primary Central Nervous System Lymphoma

ABSTRACT BACKGROUND/PURPOSE: There is currently no standard effective treatment for patients with relapsed/refractory primary central nervous system lymphoma (PCNSL). The prognosis is poor, so more studies are needed. Pemetrexed is similar to methotrexate in structure and can cross the blood-brain barrier. Compared with methotrexate, it has more targets and fewer side effects. Lenalidomide is a second generation immunomodulator with multiple functions, such as regulating immunity, anti-tumor and tumor microenvironment. A prospective observational study was conducted to explore the efficacy and safety of pemetrexed combined with lenalidomide in the treatment of relapsed/refractory primary central nervous system lymphoma. METHODS: This is a prospective observational study, we collected patients who had undergone whole brain radiotherapy and two or more chemotherapy regimens between January 2018 and December 2019 but still experienced disease progression or recurrence. each patient was treated with pemetrexed at a dose of 900mg/m2 and lenalidomide 25mg over 3 weeks as salvage chemotherapy, and one cycle consists of 4 weeks. Folic acid, vitamin B12 and dexamethasone were used to induce toxicities to pemetrexed given before chemotherapy. Oral aspirin was used to prevent thrombosis caused by lenalidomide. Adverse events were recorded in each patient during inpatients, outpatient or telephone follow-up. RESULTS: A total of 38 patients with recurrent/refractory PCNSL were enrolled in our study, including 26 males and 12 females with a median age of 57 (range from 33 to 73) years old. After treatment, the overall response rate was 68.4%(Table 1). The median progression free survival (PFS) time was 6 months and median overall survival (OS) time was 14 months (Figure1, Figure2). The adverse events mainly included fatigue, gastrointestinal reaction, myelosuppression, thrombocytopenia, fever and infection. After long-term or short-term chemotherapy, the patients had different degrees of myelosuppression symptoms presented as leukopenia (six cases in grade 1 and one case in grade 3), neutropenia (six cases in grade 1 and two cases in grade 3), anemia (6 cases in grade 1), thrombocytopenia (1 case in grade 2 and 3 cases in grade 3). Nausea and vomiting, as the common gastrointestinal reaction, appeared in two (grade 1) and one (grade 2) cases, respectively. One patient died of severe pneumonia infection. Three cases developed grade 1 cardiac disorder, including asymptomatic sinus bradycardia and asymptomatic ventricular premature beat, respectively. In addition, other reactions including fatigue (four cases in grade 1 and one case in grade 2), fever (four cases). All patients had no abnormal liver function, kidney function, constipation and leukoencephalopathy (Table 2). CONCLUSION: This study has been the first prospective clinical study of pemetrexed combined with lenalidomide in the treatment of patients with relapsed/refractory PCNSL in international. The results indicate that chemotherapy with pemetrexed combined with lenalidomide may be an effective therapy for the treatment of relapsed/refractory PCNSL with modest toxicity. KEYWORDS: Primary central nervous system lymphoma; Relapse/refractory; Pemetrexed combined with lenalidomide; Efficacy; Safety Disclosures No relevant conflicts of interest to declare.

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study (Preprint)

BACKGROUND With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. OBJECTIVE The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. METHODS Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. RESULTS The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. CONCLUSIONS This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research.