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Introduction: Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders mainly representing impaired social communication. The etiology of ASD includes genetic and environmental risk factors. Rodent models containing ASD risk gene mutations or environmental risk factors, such as exposure to maternal inflammation, show abnormal behavior. Although zebrafish conserves many important brain structures of humans and has sophisticated and fine behaviors in social interaction, it is unknown whether the social behaviors of their offspring would be impaired due to exposure to maternal inflammation. Methods: We exposed zebrafish to maternal immune activation (MIA) by injection with polyinosinic:polycytidylic acid [poly(I:C)], and screened their behaviors through social behavioral tests such as social preference and shoaling behavior tests. We compared phenotypes resulted from different ways of poly(I:C) exposure. RNA sequencing was performed to explore the differential expression genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis was performed with the detected DEGs to find the concentrated pathways. Finally, we knocked out the fatty acid-binding protein 2 (fabp2), a key node of the concentrated PPI network, to find its rescues on the altered social behavior. Results: We reported here that MIA offspring born to mothers injected with poly(I:C) exhibited impaired social approach and social cohesion that mimicked human ASD phenotypes. Both maternal exposure and direct embryo exposure to poly(I:C) resulted in activations of the innate immune system through toll-like receptors 3 and 4. RNA-sequencing results from MIA brain tissues illustrated that the numbers of overexpressed genes were significantly more than that of underexpressed genes. GO and KEGG analyses found that MIA-induced DEGs were mainly concentrated in complement and coagulation cascade pathways. PPI network analyses suggested that villin-1 (vil1) pathway might play a key role in MIA-induced ASD. Knockout of fabp2 in F0 zebrafish rescued the social behavior deficits in MIA offspring. Conclusions: Overall, our work established an ASD model with assessable behavior phenotype in zebrafish and provided key insights into environmental risk factor in ASD etiology and the influence of fabp2 gene on ASD-like behavior. [ABSTRACT FROM AUTHOR]

Catheter ablation has become an important treatment for atrial fibrillation (AF), but its recurrence rate is still high. The aim of this study was to predict AF recurrence using a three-dimensional (3D) network model based on body-surface potential mapping signals (BSPMs). BSPMs were recorded with a 128-lead vest in 14 persistent AF patients before undergoing catheter ablation (Maze-IV). The torso geometry was acquired and meshed by point cloud technology, and the BSPM was interpolated into the torso geometry by the inverse distance weighted (IDW) method to generate the isopotential map. Experiments show that the isopotential map of BSPMs can reflect the propagation of the electrical wavefronts. The 3D isopotential sequence map was established by combining the spatial--temporal information of the isopotential map; a 3D convolutional neural network (3D-CNN) model with temporal attention was established to predict AF recurrence. Our study proposes a novel attention block that focuses the characteristics of atrial activations to improve sampling accuracy. In our experiment, accuracy (ACC) in the intra-patient evaluation for predicting the recurrence of AF was 99.38%. In the inter-patient evaluation, ACC of 3D-CNN was 81.48%, and the area under the curve (AUC) was 0.88. It can be concluded that the dynamic rendering of multiple isopotential maps can not only comprehensively display the conduction of cardiac electrical activity on the body surface but also successfully predict the recurrence of AF after CA by using 3D isopotential sequence maps. [ABSTRACT FROM AUTHOR]

Background: Electrocardiogram (ECG) and carotid ultrasound (CUS) are important tools for the diagnosis and prediction of cardiovascular disease (CVD). This study aimed to investigate the associations between ECG and CUS parameters and explore the feasibility of assessing carotid health with ECG. Methods: This cross-sectional cohort study enrolled 319 healthy Chinese subjects. Standard 12-lead ECG parameters (including the ST-segment amplitude [STA]), CUS parameters (intima-media thickness [IMT] and blood flow resistance index [RI]), and CVD risk factors (including sex, age, and systolic blood pressure [SBP]) were collected for analysis. Participants were divided into the high-level RI group (average RI ≥ 0.76, n = 171) and the normal RI group (average RI < 0.76, n = 148). Linear and stepwise multivariable regression models were performed to explore the associations between ECG and CUS parameters. Results: Statistically significant differences in sex, age, SBP, STA and other ECG parameters were observed in the normal and the high-level RI group. The STA in lead V3 yielded stronger significant correlations (r = 0.27-0.42, p < 0.001) with RI than STA in other leads, while ECG parameters yielded weak correlations with IMT (|r| ≤ 0.20, p < 0.05). STA in lead V2 or V3, sex, age, and SBP had independent contributions (p < 0.01) to predicting RI in the stepwise multivariable models, although the models for IMT had only CVD risk factors (age, body mass index, and triglyceride) as independent variables. The prediction model for RI in the left proximal common carotid artery (CCA) had higher adjusted R² (adjusted R² = 0.31) than the model for RI in the left middle CCA (adjusted R² = 0.29) and the model for RI in the right proximal CCA (adjusted R² = 0.20). Conclusion: In a cohort of healthy Chinese individuals, the STA was associated with the RI of CCA, which indicated that ECG could be utilized to assess carotid health. The utilization of ECG might contribute to a rapid screening of carotid health with convenient operations. [ABSTRACT FROM AUTHOR]

Background: Dominant frequency (DF) analysis of atrial electrograms has become an important method in characterizing atrial fibrillation (AF). As a classic method, Botteron's approach is widely used in the preprocessing of frequency analysis during AF. It includes three steps: (1) band-pass filtering at 40-250 Hz, (2) absolute value, and (3) low-pass filtering at 20 Hz. This paper aims to expound the necessity and adjustability of each step.Methods and Results: Unipolar epicardial mapping signals were recorded during AF from eight mongrel dogs with cholinergic AF model. Episodes of these data were randomly selected to evaluate the impact of different pass bands and the necessity of low-pass filtering with 20 Hz cutoff frequency. Each episode of AF signal is 5 s long with a sampling rate of 2 kHz. Simulated electrograms were adopted to discuss the role of taking absolute value. Furthermore, direct spectral analysis method (FFT et al.) is compared with Botteron's preprocessing approach. According to our statistical analysis, the pass band of 40-250 Hz was not the best, while 20-100 Hz presented the high accuracy rate of DF. From the comparing result of direct FFT without Botteron's approach we deduced that the rectification of absolute value was meaningful for the fundamental atrial frequency. The final step, 20 Hz low-pass filter can completely be omitted in DF analysis. In consideration of the demand for real-time distribution of DF in clinical or experimental situations, down-sampling method and the impact of ventricular artifacts on DF was also discussed.Conclusion: In the actual application of the three preprocessing steps, the pass band selection of band-pass filter can be adjusted and the rectification of taking absolute value is important. Nevertheless, the final step of 20 Hz low-pass filter is totally unnecessary. In real-time signal processing situations, taking down-sampling method and ignoring the ventricular artifacts can also have high performance in DF analysis of atrial electrograms. [ABSTRACT FROM AUTHOR]