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Abstract Stress granules (SGs) are dynamic, membrane-less organelles. With their formation and disassembly processes characterized, it remains elusive how compositional transitions are coordinated during prolonged stress to meet changing functional needs. Here, using time-resolved proteomic profiling of the acute to prolonged heat-shock SG life cycle, we identify dynamic SG proteins, further segregated into early and late proteins. Comparison of different groups of SG proteins suggests that their biochemical properties help coordinate SG compositional and functional transitions. In particular, early proteins, with high phase-separation-propensity, drive the rapid formation of the initial SG platform, while late proteins are subsequently recruited as discrete modules to further functionalize SGs. This model, supported by immunoblotting and immunofluorescence imaging, provides a conceptual framework for the compositional transitions throughout the acute to prolonged SG life cycle. Additionally, an early SG constituent, non-muscle myosin II, is shown to promote SG formation by increasing SG fusion, underscoring the strength of this dataset in revealing the complexity of SG regulation.

Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to reduce unintended harm from sudden seizures. The purpose of this study is to investigate the applicability of transfer learning (TL) technique and model inputs for different deep learning (DL) model structures, which may provide a reference for researchers to design algorithms. Moreover, we also attempt to provide a novel and precise Transformer-based algorithm. Methods: Two classical feature engineering methods and the proposed method which consists of various EEG rhythms are explored, then a hybrid Transformer model is designed to evaluate the advantages over pure convolutional neural networks (CNN)-based models. Finally, the performances of two model structures are analyzed utilizing patient-independent approach and two TL strategies. Results: We tested our method on the CHB-MIT scalp EEG database, the results showed that our feature engineering method gains a significant improvement in model performance and is more suitable for Transformer-based model. In addition, the performance improvement of Transformer-based model utilizing fine-tuning strategies is more robust than that of pure CNN-based model, and our model achieved an optimal sensitivity of 91.7% with false positive rate (FPR) of 0.00/h. Conclusion: Our epilepsy prediction method achieves excellent performance and demonstrates its advantage over pure CNN-based structure in TL. Moreover, we find that the information contained in the gamma ( $\gamma$ ) rhythm is helpful for epilepsy prediction. Significance: We propose a precise hybrid Transformer model for epilepsy prediction. The applicability of TL and model inputs is also explored for customizing personalized models in clinical application scenarios.

Detection of atrial fibrillation (AF) events is significant for early clinical diagnosis and appropriate intervention. However, in existing detection algorithms for paroxysmal AF (AFp), the location of AF starting and ending points in AFp is not concerned. To achieve an accurate identification of AFp events in the long-term dynamic electrocardiograms (ECGs), this paper proposes a two-step method based on machine learning. In the first step, based on features extracted from the calculated R-to-R intervals (RR intervals, the cycle of heart beat), the rhythm type of the ECG signal is first classified into three classes (AFp rhythm, persistent AF (AFf) rhythm, and non-atrial fibrillation (non-AF, N) rhythm) using support vector machine (SVM). In the second step, the starting and ending points for AF episodes of AFp rhythms predicted in the first step are further located based on heartbeat classification. By training a deep convolutional neural network with phased training, the segmented beats of AFp rhythms are divided into AF beats and non-AF beats to determine the beginning and end of any AF episode. The proposed two-step method is trained and tested on the 4th China Physiological Signal Challenge 2021 databases. A final score U of 1.9310 is obtained on the unpublished test set maintained by the challenge organizers, which demonstrates the advantage of the two-step method in AFp event detection. The work is useful for assessing AF burden index for AFp patients.

Abstract Background Paroxysmal extreme pain disorder (PEPD) is a rare autosomal dominant hereditary disease, characterized by paroxysmal burning pain in the rectum, eyes or mandible and autonomic nervous symptoms, including skin redness and bradycardia. PEPD is a sodium channel dysfunctional disorder caused by SCN9A gene variants. It occurs mainly in Caucasians and only one case has been reported in the Chinese population. Here, we report the second PEPD case in a Chinese indivisual. Case presentation A 2 years and 6 months old girl initially presented with non-epileptic tonic seizures at 7 days after birth. Her clinical symptoms in order of presentation were non-epileptic tonic seizures, harlequin color change and pain. Genetic analysis showed the patient carried a heterozygous variant c.4384T>A (p.F1462I) in the SCN9A gene, which was speculated to cause PEPD symptoms. After administrating carbamazepine, the symptoms were relieved and the patient's condition improved. However, the patient’s mother, who carries the same SCN9A variant as her daughter, only showed bradycardia and sinus arrest but no PEPD-related pain. Conclusions This is the second PEPD case reported in the Chinese population. With the discovery of a novel variant in SCN9A, we expanded the genotype spectrum of PEPD. This is the first case suggesting that the clinical presentations of SCN9A-associated PEPD may show inter familial phenotypic diversity. In the future of clinical diagnosis, patients with triggered non-epileptic tonic seizures or pain and harlequin color change should be considered for PEPD and proper and prompt treatment should be given.

IntroductionAtrial fibrillation (AF) is prone to heart failure and stroke. Early management can effectively reduce the stroke rate and mortality. Current clinical guidelines screen high-risk individuals based solely on age, while this study aims to explore the possibility of other AF risk predictors.MethodsA total of 18,738 elderly people (aged over 60 years old) in Chinese communities were enrolled in this study. The baseline characteristics were mainly based on the diagnosis results of electrocardiogram (ECG) machine during follow up, accompanied by some auxiliary physical examination basic data. After the analysis of both independent and combined baseline characteristics, AF risk predictors were obtained and prioritized according to the results. Independent characteristics were studied from three aspects: Chi-square test, Mann–Whitney U test and Cox univariate regression analysis. Combined characteristics were studied from two aspects: machine learning models and Cox multivariate regression analysis, and the former was combined with recursive feature elimination method and voting decision.ResultsThe resulted optimal combination of risk predictors included age, atrial premature beats, atrial flutter, left ventricular hypertrophy, hypertension and heart disease.ConclusionPatients diagnosed by short-time ECG machines with the occurrence of the above events had a higher probability of AF episodes, who are suggested to be included in the focus of long-term ECG monitoring or increased screening density. The incidence of risk predictors in different age ranges of AF patients suggests differences in age-specific patient management. This can help improve the detection rate of AF, standardize the management of patients, and slow down the progression of AF.

IntroductionAutism 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.MethodsWe 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.ResultsWe 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.ConclusionsOverall, 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.

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.

BackgroundPostoperative atrial fibrillation (POAF) is often associated with serious complications. In this study, we collected long-term single-lead electrocardiograms (ECGs) of patients with preoperative sinus rhythm to build statistical models and machine learning models to predict POAF.MethodsAll patients with preoperative sinus rhythm who underwent cardiac surgery were enrolled and we collected long-term ECG data 24 h before surgery and 7 days after surgery by single-lead ECG. The patients were divided into a POAF group a no-POAF group. A clinical model and a clinical + ECG model were constructed. The ECG parameters were designed and support vector machine (SVM) was selected to build a machine learning model and evaluate its prediction efficiency.ResultsA total of 100 patients were included. The detection rate of POAF in long-term ECG monitoring was 31% and that in conventional monitoring was 19%. We calculated 7 P-wave parameters, Pmax (167 ± 31 ms vs. 184 ± 37 ms, P = 0.018), Pstd (15 ± 7 vs. 19 ± 11, P = 0.031), and PWd (62 ± 28 ms vs. 80 ± 35 ms, P = 0.008) were significantly different. The AUC of the clinical model (sex, age, LA diameter, GFR, mechanical ventilation time) was 0.86. Clinical + ECG model (sex, age, LA diameter, GFR, mechanical ventilation time, Pmax, Pstd, PWd), AUC was 0.89. In the machine learning model, the accuracy (Ac) of the train set and test set was above 80 and 60%, respectively.ConclusionLong-term ECG monitoring could significantly improve the detection rate of POAF. The clinical + ECG model and the machine learning model based on P-wave parameters can predict POAF.

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.

Although radio frequency ablation is the most effective treatment for atrial fibrillation (AF), there is still a high recurrence rate. The purpose of this paper was to initially assess the probability of the recurrence of AF based on the preoperative body surface potential mapping (BSPM) signals, in other words, to predict the efficiency of ablation and assist physicians in developing more effective treatment options. At present, deep learning methods based on convolutional neural networks (CNNs) do not require complex mathematical abstractions or manual interventions; thus, higher computation efficiency can be obtained in such research. However, the use of the fully connected multi-layer perceptron (MLP) algorithms has shown low classification performance. This paper proposes an improved CNN algorithm (CNN-SVM method) for the recurrence classification in AF patients by combining with the support vector machine (SVM) architecture. The algorithm is validated on the preoperative AF signals of 14 patients for classification. All postoperative patients are followed up for one year; ten of them remain in sinus rhythm, whereas the other four turn back to AF. The ECG data for these patients are obtained through the 128-Lead BSPM system. The results show that the proposed CNN-SVM method can automatically extract the characteristic information through the CNN network. The constructed model ultimately achieved an accuracy of 96%, a sensitivity of 88%, and a specificity of 96%. It is concluded that the CNN-SVM method solves the drawbacks of MLP only for separating linear data. It improves the overall performance of AF recurrence classification, thereby providing a valuable reference for doctors to develop personalized treatment plans.

Atrial fibrillation (AF) is a common clinical arrhythmia with a lifetime risk and has become an attractive topic of clinical research and health management. Although the 12-lead classical ECG configuration is adequate for the detection of AF, its configuration is suboptimal for constructing surface potential mapping or extracting other diagnostic features during AF. Body surface potential mapping (BSPM) has a broad application prospect in AF study, but the electrode wiring is complicated and the hardware cost is high. This paper proposes a method to improve the performance of leads selection algorithm from a 128-lead BSPM system with equidistantly spaced electrodes. 128-lead BSPM data obtained from 8 AF patients were used in the study. Leads selection was performed by a sequential selection based on an adapted Botteron's approach and multiple linear regression. The result showed that the proposed method has significant improvement on the performance of the accurate reconstruction of dominant frequency on the patient-specific dataset. The proposed method may not only improve the performance on the spectrum characteristic but also effectively improve the performance of time domain criteria when the number of selected leads is above 12.

Electrocardiogram (ECG) is an efficient and commonly used tool for detecting arrhythmias. With the development of dynamic ECG monitoring, an effective and simple algorithm is needed to deal with large quantities of ECG data. In this study, we proposed a method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks. For a short-time (10 s) single-lead ECG signal, the time-frequency distribution matrix of the signal was first obtained using a time-frequency transform method, and then a convolutional neural network was used to discriminate the rhythm of the signal. ECG data in multiple databases were used and were divided into 12 classes. Finally, the performance of three kinds of time-frequency transform methods are evaluated, including short-time Fourier transform (STFT), continuous wavelet transform (CWT), and pseudo Wigner-Ville distribution (PWVD). The best result was obtained by STFT, with an accuracy of 96.65%, an average sensitivity of 96.47%, an average specificity of 99.68%, and an average F1 score of 96.27%, respectively. Especially, the area under curve (AUC) value is 0.9987. The proposed method in this work may be efficient and valuable to detect multiple arrhythmias for dynamic ECG monitoring.

Pace mapping is commonly used to locate the origin of ventricular arrhythmias, especially premature ventricular contraction (PVC). However, this technique relies on clinicians’ ability to rapidly interpret ECG data. To avoid time-consuming interpretation of ECG morphology, some automated algorithms or computational models have been explored to guide the ablation. Inspired by these studies, we propose a novel model based on spatial and morphological domains. The purpose of this study is to assess this model and compare it with three existing models. The data are available from the Experimental Data and Geometric Analysis Repository database in which three in vivo PVC patients are included. To measure the hit rate (A hit occurs when the predicted site is within 15 mm of the target) of different algorithms, 47 target sites are tested. Moreover, to evaluate the efficiency of different models in narrowing down the target range, 54 targets are verified. As a result, the proposed algorithm achieves the most hits (37/47) and fewest misses (9/47), and it narrows down the target range most, from 27.62 ± 3.47 mm to 10.72 ± 9.58 mm among 54 target sites. It is expected to be applied in the real-time prediction of the origin of ventricular activation to guide the clinician toward the target site.

Summary: AMFR/gp78 and USP13 are a pair of ubiquitin ligase and deubiquitinase that ensure the accuracy of endoplasmic reticulum-associated degradation (ERAD). Depletion of USP13 leads to caspase activation and cleavage of the ERAD chaperone BAG6, which is reversed by knockdown of AMFR. However, the mechanism and physiological relevance of this regulation are still unclear. Here, by using the NEDDylator system, we screened out TXN as a substrate of AMFR and USP13 and showed its involvement in regulating CASP3 activation and BAG6 cleavage. Furthermore, we showed that the cleaved N-terminal BAG6 is located in the cytosol and interacts with both LC3B-I and unprocessed form of LC3B (Pro-LC3B) through the LIR1 motif to suppress autophagy. An NMR approach verified the direct interaction between BAG6 LIR1 and LC3B-I or Pro-LC3B. Collectively, our findings uncover a mechanism that converts BAG6 from an ERAD regulator to an autophagy tuner and apoptosis inducer during ER stress.

Background: Tic disorders (TD) are common neuropsychiatric disorders among children and adolescents. It is controversial that trace elements may participate in the pathogenesis of TD. Our study aimed to investigate the trace elements status of zinc (Zn), copper (Cu), iron (Fe), and magnesium (Mg) in children with TD, in comparison to healthy controls.Methods: The medical records of eligible TD children and normal healthy children from January 1 to December 31, 2018 in the outpatient clinic were retrospectively reviewed. The clinical information of all subjects were collected including age, gender, diagnosis, previous health records, and serum trace elements level (Cu, Zn, Fe, Mg) at the time of diagnosis before initiating treatment.Results: In total, 1204 TD children (7.63 ± 2.45 years) and 1,220 healthy children (7.27 ± 3.15 years) who were divided into two gender and three age groups (2–4years, 5–9years, ≥10 years) were reviewed in our study. Our study showed that TD children generally had lower whole blood levels of Zn, Cu, Fe than the normal controls (P < 0.01). No significant difference was observed in whole blood levels of Mg. After adjusting for gender, the trends still remained. Further analysis was performed according to age, the trends still remained in Zn and Fe in all age groups (P < 0.05). However, we observed an almost significantly (P = 0.055) lower level of Cu in TD of 2–4 years group while significant differences in other two groups (P < 0.01). Further multiple linear regression and point biserial correlation showed that the lower blood levels of Zn, Cu, and Fe were correlated with the incidence of TD.Conclusion: The present results indicated that lower blood levels of zinc, iron, copper were associated with TD. Trace elements may be used as an auxiliary treatment for TD and need to be further explored.

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]

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]