[Design and Implementation of Software Platform for AI-ECG Algorithm Research].

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classification algorithm and to solve the problem of subjects data information management. Matlab R2019b and MySQL Sever 8.0 are used to design the software platform. The software platform is divided into three modules including data management module, data receiving module and data processing module. The software platform can be used to query and set the subjects information. It has realized the functions of data receiving, signal processing and the display, analysis and storage of ECG data. The software platform is easy to operate and meets the basic needs of scientific research. It is of great significance to the research of AI-ECG algorithm.

[Assessment of atrial fibrillation inducibility based on epicardial mapping signals].

Atrial fibrillation (AF) is the most common arrhythmia in clinic, which can cause hemodynamic changes, heart failure and stroke, and seriously affect human life and health. As a self-promoting disease, the treatment of AF can become more and more difficult with the deterioration of the disease, and the early prediction and intervention of AF is the key to curbing the deterioration of the disease. Based on this, in this study, by controlling the dose of acetylcholine, we changed the AF vulnerability of five mongrel dogs and tried to assess it by analyzing the electrophysiology of atrial epicardium under different states of sinus rhythm. Here, indices from four aspects were proposed to study the atrial activation rule. They are the variability of atrial activation rhythm, the change of the earliest atrial activation, the change of atrial activation delay and the left-right atrial dyssynchrony. By using binary logistic regression analysis, multiple indices above were transformed into the AF inducibility, which were used to classify the signals during sinus rhythm. The sensitivity, specificity and accuracy of classification reached 85.7%, 95.8% and 91.7%, respectively. As the experimental results show, the proposed method has the ability to assess the AF vulnerability of atrium, which is of great clinical significance for the early prediction and intervention of AF.

[Prediction of recurrence of paroxysmal atrial fibrillation based on RR interval].

Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence.

Ubiquitin C-Terminal Hydrolase L1 regulates autophagy by inhibiting autophagosome formation through its deubiquitinating enzyme activity.

Ubiquitination modification has been shown to play a key role in autophagy. Increasing studies reported the involvement of de-ubiquitinating enzymes (DUBs) in autophagy pathway. To systematically search how DUBs manipulate autophagy, we utilized a double fluorescence tagged LC3 stable HeLa cell line, and did a genome wide screen of 55 human DUBs which is about 60% coverage of the DUB family. We found a bunch of DUBs have impact on autophagy by either changing the LC3 puncta formation or the autophagy flux. One of them, Ubiquitin C-Terminal Hydrolase L1 (UCHL1) correlated to Parkinson's disease, strongly affects autophagy by inhibiting autophagosome formation. We found UCHL1 overexpression inhibits LC3 puncta formation and is dependent on its DUB activity. Knockdown of UCHL1 significantly promotes LC3 puncta formation. Further study revealed that UCHL1 may affect autophagy by interacting with LC3 but not other autophagy related proteins. Interestingly, a Parkinson's disease related mutant UCHL1 I93 M defects its DUB activity and can no longer inhibit autophagosome formation. We further screened 22 commercially available DUB inhibitors and found two potent UCHL1 inhibitors LDN-57444 (LDN) and NSC632839 (NSC), when treating cells, both strongly induce LC3 puncta formation. Taken together, our results indicated a new insight into the manner in which DUB regulates autophagy and provided potential drugs for the Parkinson's disease.

Pericentral hepatocytes produce insulin-like growth factor-2 to promote liver regeneration during selected injuries in mice.

Liver regeneration (LR) happens after various types of injuries. Unlike the well-studied LR caused by partial hepatectomy (PHx), there is accumulating evidence suggesting that LR during other injuries may result from unknown mechanisms. In this study, we found that insulin-like growth factor 2 (IGF-2) was drastically induced following the liver injuries caused by tyrosinemia or long-term treatments of CCl4 . However, this was not observed during the early phase of acute liver injuries after PHx or single treatment of CCl4 . Remarkably, most IGF-2-expressing hepatocytes were located at the histological area around the central vein of the liver lobule after the liver injuries caused either in fumarylacetoacetate hydrolase-deficient mice or in CCl4 chronically treated mice. Hepatocyte proliferation in vivo was significantly promoted by induced IGF-2 overexpression, which could be inhibited by adeno-associated virus-delivered IGF-2 short hairpin RNAs or linsitinib, an inhibitor of IGF-2 signaling. Proliferating hepatocytes in vivo responded to IGF-2 through both insulin receptor and IGF-1 receptor. IGF-2 also significantly promoted DNA synthesis of primary hepatocytes in vitro. More interestingly, the significantly induced IGF-2 was also found to colocalize with glutamine synthetase in the region enriched with proliferating hepatocytes for the liver samples from patients with liver fibrosis. CONCLUSION: IGF-2 is produced by pericentral hepatocytes to promote hepatocyte proliferation and repair tissue damage in the setting of chronic liver injury, which is distinct from the signaling that occurs post-PHx. (Hepatology 2017;66:2002-2015).

Variable epilepsy phenotypes associated with heterozygous mutation in the SCN9A gene: report of two cases.

Up to now, SCN9A mutations encoding Nav1.7 have been limited to inherited pain syndromes. A few of pathogenic SCN9A mutations with or without SCN1A mutations have been identified in epileptic patients. Here, we report two heterozygous SCN9A mutations with no SCN1A mutations, which are associated with variable epilepsy phenotypes and explored the possibility of SCN9A contributing to a multifactorial etiology for epilepsy. Our findings suggest that the two SCN9A mutations (c.980G>A chr2:167149868 p.G327E; c.5702_5706del chr2:167055410 p.I1901fs) should be regarded as pathogenic mutations. Two heterozygous mutations of SCN9A are associated with a wide clinical spectrum of seizure phenotypes including simple febrile seizures, afebrile seizures, generalized tonic-clonic seizure, myoclonic or tonic seizures, and focal clonic seizures. Patients with deletion mutations tend to be associated with more severe seizure type than missense mutations.

[Rhythm analysis of body surface potential mapping recordings from atrial fibrillation patients based on autocorrelation function].

The study of atrial fibrillation (AF) has been known as a hot topic of clinical concern. Body surface potential mapping (BSPM), a noninvasive electrical mapping technology, has been widely used in the study of AF. This study adopted 10 AF patients' preoperative and postoperative BSPM data (each patient's data contained 128 channels), and applied the autocorrelation function method to obtain the activation interval of the BSPM signals. The activation interval results were compared with that of manual counting method and the applicability of the autocorrelation function method was verified. Furthermore, we compared the autocorrelation function method with the commonly used fast Fourier transform (FFT) method. It was found that the autocorrelation function method was more accurate. Finally, to find a simple rule to predict the recurrence of atrial fibrillation, the autocorrelation function method was used to analyze the preoperative BSPM signals of 10 patients with persistent AF. Consequently, we found that if the patient's proportion of channels with dominant frequency larger than 2.5 Hz in the anterior left region is greater than the other three regions (the anterior right region, the posterior left region, and the posterior right region), he or she might have a higher possibility of AF recurrence. This study verified the rationality of the autocorrelation function method for rhythm analysis and concluded a simple rule of AF recurrence prediction based on this method.

Several insights into the preprocessing of electrograms in atrial fibrillation for dominant frequency analysis.

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.

[Analysis of the Rhythm of Atrial Epicardial Mapping Data Based on Dominant Frequency].

If heart function is normal, the atrial cells are excited in a stable rhythm. But this would change during atrial fibrillation. In this paper, after comparing with the method of characteristic point, we use the dominant frequency method to analyze the activation pattern under sinus and atrial fibrillation rhythm in different parts of atria based on epicardial mapping system. It is found that the activation rhythm changes a lot in different parts of atria, and the automaticity of atrial cells change obviously in somewhere. The result shows that dominant frequency method is very suitable for the analysis of atrial fibrillation signal. What's more, we also roughly discuss the role of this method in exploring the driving sources during atrial fibrillation.

[The correlation analysis of the epicardial signals by Shannon entropy].

This paper applied the Shannon Entropy based on the cross correlation to analyze the epicardial signals from anterior wall of the canine atria. The result demonstrated that during sinus rhythm, the stability level of the correlation among signals from anterior right atria is much higher than the signals from anterior left atria. All the signals from the anterior wall descended when the rhythm changed from sinus rhythm to atrial fibrillation(AF). However, there were some regions still having a stable correlation during AF. The results will be helpful to enhance understanding of the correlation characteristic of AF.

[Implementation of 3D heart modeling based on an improved region rowing method].

Image segmentation is a key step for image processing. This study developed an improved region growing algorithm to extract the outline of the heart for 3D-modeling which based on the acquisition of canine cardiac CT images from animal experiment. In this paper the method was also compared with the classic algorithm of threshold segmentation. The result showed that the method can be used for the 3D display technology of cardiac electrical activity in clinical electrophysiology mapping.

A "two-step classification" machine learning method for non-invasive localization of premature ventricular contraction origins based on 12-lead ECG.

BACKGROUND: Premature ventricular contraction (PVC) is a type of cardiac arrhythmia that originates from ectopic pacemaker in the ventricles. The localization of the origin of PVC is essential for successful catheter ablation. However, most studies on non-invasive PVC localization focus on elaborate localization in specific regions of the ventricle. This study aims to propose a machine learning algorithm based on 12-lead electrocardiogram (ECG) data that can improve the accuracy of PVC localization in the whole ventricle. METHODS: We collected 12-lead ECG data from 249 patients with spontaneous or pacing-induced PVCs. The ventricle was divided into 11 segments. In this paper, we propose a machine learning method consisting of two consecutive classification steps. In the first classification step, each PVC beat was labeled to one of the 11 ventricular segments using six features, including a newly proposed morphological feature called "Peak_index." Four machine learning methods were tested for comparative multi-classification performance and the best classifier result was kept to the next step. In the second classification step, a binary classifier was trained using a smaller combination of features to further differentiate segments that are easily confused. RESULTS: The Peak_index as a proposed new classification feature combined with other features is suitable for whole ventricle classification by machine learning methods. The test accuracy of the first classification reached 75.87%. It is shown that a second classification for confusable categories can improve the classification results. After the second classification, the test accuracy reached 76.84%, and when a sample classified into adjacent segments was considered correct, the test "rank accuracy" was improved to 93.49%. The binary classification corrected 10% of the confused samples. CONCLUSION: This paper proposes a "two-step classification" method to localize the origin of PVC beats into the 11 regions of the ventricle using non-invasive 12-lead ECG. It is expected to be a promising technique to be used in clinical settings to help guide ablation procedures.

A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.

The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm overlook the probability distribution differences between the source domain (SD) and target domain (TD) databases. The motivation of this paper is to address the challenge of labeled data scarcity at the model level while exploring an effective method to eliminate domain discrepancy between SD and TD databases, especially when SD and TD are derived from inconsistent tasks. This study proposes a multi-module heartbeat classification algorithm. Initially, unsupervised feature extractors are designed to extract rich features from unlabeled SD and TD data. Subsequently, a novel adaptive transfer method is proposed to effectively eliminate domain discrepancy between features of SD for pre-training (PTF-SD) and features of TD for fine-tuning (FTF-TD). Finally, the adapted PTF-SD is employed to pre-train a designed classifier, and FTF-TD is used for classifier fine-tuning, with the objective of evaluating the algorithm's performance on the TD task. In our experiments, MNIST-DB serves as the SD database for handwritten digit image classification task, MIT-DB as the TD database for heartbeat classification task. The overall accuracy of classifying heartbeats into normal heartbeats, supraventricular ectopic beats (SVEBs), and ventricular ectopic beats (VEBs) reaches 96.7 %. Specifically, the sensitivity (Sen), positive predictive value (PPV), and F1 score for SVEBs are 0.802, 0.701, and 0.748, respectively. For VEBs, Sen, PPV, and F1 score are 0.976, 0.840, and 0.903, respectively. The results indicate that the proposed multi-module algorithm effectively addresses the challenge labeled data scarcity in heartbeat classification through unsupervised learning and adaptive feature transfer methods.

A Lightweight Hybrid Model Using Multiscale Markov Transition Field for Real-Time Quality Assessment of Photoplethysmography Signals.

OBJECTIVE: The proliferation of wearable devices has escalated the standards for photoplethysmography (PPG) signal quality. This study introduces a lightweight model to address the imperative need for precise, real-time evaluation of PPG signal quality, followed by its deployment and validation utilizing our integrated upper computer and hardware system. METHODS: Multiscale Markov Transition Fields (MMTF) are employed to enrich the morphological information of the signals, serving as the input for our proposed hybrid model (HM). HM undergoes initial pre-training utilizing the MIMIC-III and UCI databases, followed by fine-tuning the Queensland dataset. Knowledge distillation (KD) then transfers the large-parameter model's knowledge to the lightweight hybrid model (LHM). LHM is subsequently deployed on the upper computer for real-time signal quality assessment. RESULTS: HM achieves impressive accuracies of 99.1% and 96.0% for binary and ternary classification, surpassing current state-of-the-art methods. LHM, with only 0.2 M parameters (0.44% of HM), maintains high accuracy despite a 2.6% drop. It achieves an inference speed of 0.023 s per image, meeting real-time display requirements. Furthermore, LHM attains a 97.7% accuracy on a self-created database. HM outperforms current methods in PPG signal quality accuracy, demonstrating the effectiveness of our approach. Additionally, LHM substantially reduces parameter count while maintaining high accuracy, enhancing efficiency and practicality for real-time applications. CONCLUSION: The proposed methodology demonstrates the capability to achieve high-precision and real-time assessment of PPG signal quality, and its practical validation has been successfully conducted during deployment. SIGNIFICANCE: This study contributes a convenient and accurate solution for the real-time evaluation of PPG signals, offering extensive application potential.

USP20 deubiquitinates and stabilizes the reticulophagy receptor RETREG1/FAM134B to drive reticulophagy.

The endoplasmic reticulum (ER) serves as a hub for various cellular processes, and maintaining ER homeostasis is essential for cell function. Reticulophagy is a selective process that removes impaired ER subdomains through autophagy-mediatedlysosomal degradation. While the involvement of ubiquitination in autophagy regulation is well-established, its role in reticulophagy remains unclear. In this study, we screened deubiquitinating enzymes (DUBs) involved in reticulophagy and identified USP20 (ubiquitin specific peptidase 20) as a key regulator of reticulophagy under starvation conditions. USP20 specifically cleaves K48- and K63-linked ubiquitin chains on the reticulophagy receptor RETREG1/FAM134B (reticulophagy regulator 1), thereby stabilizing the substrate and promoting reticulophagy. Remarkably, despite lacking a transmembrane domain, USP20 is recruited to the ER through its interaction with VAPs (VAMP associated proteins). VAPs facilitate the recruitment of early autophagy proteins, including WIPI2 (WD repeat domain, phosphoinositide interacting 2), to specific ER subdomains, where USP20 and RETREG1 are enriched. The recruitment of WIPI2 and other proteins in this process plays a crucial role in facilitating RETREG1-mediated reticulophagy in response to nutrient deprivation. These findings highlight the critical role of USP20 in maintaining ER homeostasis by deubiquitinating and stabilizing RETREG1 at distinct ER subdomains, where USP20 further recruits VAPs and promotes efficient reticulophagy.Abbreviations: ACTB actin beta; ADRB2 adrenoceptor beta 2; AMFR/gp78 autocrine motility factor receptor; ATG autophagy related; ATL3 atlastin GTPase 3; BafA1 bafilomycin A1; BECN1 beclin 1; CALCOCO1 calcium binding and coiled-coil domain 1; CCPG1 cell cycle progression 1; DAPI 4',6-diamidino-2-phenylindole; DTT dithiothreitol; DUB deubiquitinating enzyme; EBSS Earle's Balanced Salt Solution; FFAT two phenylalanines (FF) in an acidic tract; GABARAP GABA type A receptor-associated protein; GFP green fluorescent protein; HMGCR 3-hydroxy-3-methylglutaryl-CoA reductase; IL1B interleukin 1 beta; LIR LC3-interacting region; MAP1LC3/LC3 microtubule associated protein 1 light chain 3; PIK3C3/Vps34 phosphatidylinositol 3-kinase catalytic subunit type 3; RB1CC1/FIP200 RB1 inducible coiled-coil 1; RETREG1/FAM134B reticulophagy regulator 1; RFP red fluorescent protein; RHD reticulon homology domain; RIPK1 receptor interacting serine/threonine kinase 1; RTN3L reticulon 3 long isoform; SEC61B SEC61 translocon subunit beta; SEC62 SEC62 homolog, preprotein translocation factor; SIM super-resolution structured illumination microscopy; SNAI2 snail family transcriptional repressor 2; SQSTM1/p62 sequestosome 1; STING1/MITA stimulator of interferon response cGAMP interactor 1; STX17 syntaxin 17; TEX264 testis expressed 264, ER-phagy receptor; TNF tumor necrosis factor; UB ubiquitin; ULK1 unc-51 like autophagy activating kinase 1; USP20 ubiquitin specific peptidase 20; USP33 ubiquitin specific peptidase 33; VAMP8 vesicle associated membrane protein 8; VAPs VAMP associated proteins; VMP1 vacuole membrane protein 1; WIPI2 WD repeat domain, phosphoinositide interacting 2; ZFYVE1/DFCP1 zinc finger FYVE-type containing 1.

Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information.

BACKGROUND: Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation. METHODS: In total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques. RESULTS: The proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm. CONCLUSION: Our results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.

[Design of portable 12-lead digital ECG with low power consumption].

The design of portable and low power consumption 12-lead ECG is based on the digital signal processor TMS320C5515 and the analog front end ADS1298. The ADS1298 collects the ECG signals and deliver them to TMS320C5515. The preprocessed ECG signals are displayed real-time on a LCD and can be stored without compression for a long time. The ECG signals can also be sent to an up computer by a USB connector so that ECG data can be analyzed offline. The system has small volume, high precision and low power consumption.

Value of baseline characteristics in the risk prediction of atrial fibrillation.

Introduction: Atrial 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. Methods: A 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. Results: The resulted optimal combination of risk predictors included age, atrial premature beats, atrial flutter, left ventricular hypertrophy, hypertension and heart disease. Conclusion: Patients 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.

Time-resolved proteomic profiling reveals compositional and functional transitions across the stress granule life cycle.

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.

Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules.

Eukaryotic stress granules (SGs) are highly dynamic assemblies of untranslated mRNAs and proteins that form through liquid-liquid phase separation (LLPS) under cellular stress. SG formation and elimination process is a conserved cellular strategy to promote cell survival, although the precise regulation of this process is poorly understood. Here, we screened six E3 ubiquitin ligases present in SGs and identified TRIM21 (tripartite motif containing 21) as a central regulator of SG homeostasis that is highly enriched in SGs of cells under arsenite-induced oxidative stress. Knockdown of TRIM21 promotes SG formation whereas overexpression of TRIM21 inhibits the formation of physiological and pathological SGs associated with neurodegenerative diseases. TRIM21 catalyzes K63-linked ubiquitination of the SG core protein, G3BP1 (G3BP stress granule assembly factor 1), and G3BP1 ubiquitination can effectively inhibit LLPS, in vitro. Recent reports suggested the involvement of macroautophagy/autophagy, as a stress response pathway, in the regulation of SG homeostasis. We systematically investigated well-defined autophagy receptors and identified SQSTM1/p62 (sequestosome 1) and CALCOCO2/NDP52 (calcium binding and coiled-coil domain 2) as the primary receptors that directly interact with G3BP1 during arsenite-induced stress. Endogenous SQSTM1 and CALCOCO2 localize to the periphery of SGs under oxidative stress and mediate SG elimination, as single knockout of each receptor causes accumulation of physiological and pathological SGs. Collectively, our study broadens the understanding in the regulation of SG homeostasis by showing that TRIM21 and autophagy receptors modulate SG formation and elimination respectively, suggesting the possibility of clinical targeting of these molecules in therapeutic strategies for neurodegenerative diseases.Abbreviations: ACTB: actin beta; ALS: amyotrophic lateral sclerosis; BafA1: bafilomycin A1; BECN1: beclin 1; C9orf72: C9orf72-SMCR8 complex subunit; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; Co-IP: co-immunoprecipitation; DAPI: 4',6-diamidino-2-phenylindole; FTD: frontotemporal dementia; FUS: FUS RNA binding protein; G3BP1: G3BP stress granule assembly factor 1; GFP: green fluorescent protein; LLPS: liquid-liquid phase separation; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NBR1: NBR1 autophagy cargo receptor; NES: nuclear export signal; OPTN: optineurin; RFP: red fluorescent protein; SQSTM1/p62: sequestosome 1; SG: stress granule; TAX1BP1: Tax1 binding protein 1; TOLLIP: toll interacting protein; TRIM21: tripartite motif containing 21; TRIM56: tripartite motif containing 56; UB: ubiquitin; ULK1: unc-51 like autophagy activating kinase 1; WT: wild-type.