First detection of multiple cases related to CV-A16 strain of B1c clade in Beijing in 2022.

Coxsackievirus A16 (CV-A16) is a significant etiologic agent of hand, foot, and mouth disease (HFMD) and herpangina (HA), with the capacity to progress to severe complications, including encephalitis, aseptic meningitis, acute flaccid paralysis, myocarditis, and other critical conditions. Beijing's epidemiological surveillance system, established in 2008, encompasses 29 hospitals and 16 district disease control centers. From 2019 to 2021, the circulation of CV-A16 was characterized by the co-circulation of B1a and B1b clades. Multiple cases of HFMD linked to clade B1c has not been reported in Beijing until 2022. This study enrolled 400 HFMD and 493 HA cases. Employing real-time RT-PCR, 368 enterovirus-positive cases were identified, with 180 selected for sequencing. CV-A16 was detected in 18.89% (34/180) of the cases, second only to CV-A6, identified in 63.33% (114/180). Full-length VP1 gene sequences were successfully amplified and sequenced in 22 cases, revealing the presence of clades B1a, B1b, and B1c in 14, 3, and 5 cases, respectively. A cluster of five B1c clade cases occurred between June 29 and July 17, 2022, within a 7-km diameter region in Shunyi District. Phylogenetic analysis of five complete VP1 gene sequences and two full-genome sequences revealed close clustering with the 2018 Indian strain (GenBank accession: MH780757.1) within the B1c India branch, with NCBI BLAST results showing over 98% similarity. Comparative sequence analysis identified three unique amino acid variations (P3S, V25A, and I235V). The 2022 Shunyi District HFMD cases represent the first instances of spatiotemporally correlated CV-A16 B1c clade infections in Beijing, underscoring the necessity for heightened surveillance of B1c clade CV-A16 in HFMD and HA in this region.

Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.

Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi-scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called BrainNetX to better explain the decoding results by automatically annotating task-related brain regions from the brain-network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain-decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI-based brain decoding.

Epidemiological features, genomic characteristics, and origin tracing of the COVID-19 outbreaks in Beijing from January to September 2022.

Different variants of severe acute respiratory syndrome coronavirus 2 have been discovered globally. At present, the Omicron variant has been extensively circulated worldwide. There have been several outbreaks of the Omicron variant in China. Here, we investigated the epidemiologic, genetic characteristics, and origin-tracing data of the outbreaks of COVID-19 in Beijing from January to September 2022. During this time, 19 outbreaks occurred in Beijing, with the infected cases ranging from 2 to 2230. Two concern variants were detected, with eight genotypes. Based on origin tracing analysis, two outbreaks were from the cold-chain transmission and three from items contaminated by humans. Imported cases have caused other outbreaks. Our study provided a detailed analysis of Beijing's outbreaks and valuable information to control the outbreak's spread.

Stability of SARS-CoV-2 and persistence of viral nucleic acids on common foods and widely used packaging material surfaces.

SARS-CoV-2 is still spreading globally. Studies have reported the stability of SARS-CoV-2 in aerosols and on surfaces under different conditions. However, studies on the stability of SARS-CoV-2 and viral nucleic acids on common food and packaging material surfaces are insufficient. The study evaluated the stability of SARS-CoV-2 using TCID50 assays and the persistence of SARS-CoV-2 nucleic acids using droplet digital polymerase chain reaction on various food and packaging material surfaces. Viral nucleic acids were stable on food and material surfaces under different conditions. The viability of SARS-CoV-2 varied among different surfaces. SARS-CoV-2 was inactivated on most food and packaging material surfaces within 1 day at room temperature but was more stable at lower temperatures. Viruses survived for at least 1 week on pork and plastic at 4°C, while no viable viruses were detected on hairtail, orange, or carton after 3 days. There were viable viruses and a slight titer decrease after 8 weeks on pork and plastic, but titers decreased rapidly on hairtail and carton at -20°C. These results highlight the need for targeted preventive and disinfection measures based on different types of foods, packaging materials, and environmental conditions, particularly in the cold-chain food trade, to combat the ongoing pandemic.

Phylogenetics and phylogeographic characteristics of coxsackievirus A16 in hand foot and mouth disease and herpangina cases collected in Beijing, China from 2019 to 2021.

Coxsackievirus A16 (CV-A16) is a significant pathogen responsible for causing hand foot and mouth disease (HFMD) and herpangina (HA). This study aimed to investigate the recent evolution and spread of CV-A16 by monitoring HFMD and HA cases in 29 hospitals across 16 districts in Beijing from 2019 to 2021. The first five cases of HFMD and the first five cases of HA each month in each hospital were included in the study. Real-time reverse transcription polymerase chain reaction was used to identify CV-A16, CV-A6, and EV-A71. From each district, two to four CV-A16 positive samples with a relatively long sampling time interval every month were selected for sequencing. A total of 3344 HFMD cases and 2704 HA cases were enrolled in this study, with 76.0% (2541/3344) of HFMD and 45.4% (1227/2704) of HA cases confirmed to be infected by enterovirus. Among the EV-positive samples, CV-A16 virus was detected in 33.61% (854/2541) of HFMD cases and 13.4% (165/1227) of HA cases, with the predominant cluster being B1a. Both B1a and B1b had a co-circulation of local and imported strains, with different origin time (1993 vs. 1995), different global distribution (14 countries vs. 10 countries), and different transmission centers but mainly distributed in the southern and eastern regions of Beijing. Strengthening surveillance of HFMD in southern and eastern regions will improve the prevention and control efficiency of enterovirus infections.

Stability of mpox virus on different commonly contacted surfaces.

Mpox is still spreading globally and is mostly reported to be transmitted by skin and mucosal contact. However, transmission through contact with fomites, contaminated objects, or surfaces has been reported in general population. Evaluation of the stability of mpox virus (MPXV) on different surfaces is important to minimize mpox transmission. In the study, the stability of MPXV on different kinds of commonly contacted surfaces was determined. MPXV was observed to have a surface-dependent stability pattern. Viable virus was detected on both glass and stainless steel for up to 5 days, and on plastic surfaces for up to 3 days. In contrast, no viable MPXV was detected on wooden board and cardboard, which are porous and water-absorbent surfaces, after 1 and 2 days of incubation, respectively. In addition, MPXV nucleic acids were more stable and showed better correlation with viral titers on stainless steel, plastic, and glass. The results indicate that fomite transmission of MPXV is plausible. Moreover, the stability of MPXV was highly surface-dependent and more stable on smooth surfaces, which could provide more information for minimizing the transmission of mpox and emphasize the significance of environmental disinfection in mpox prevention and control.

Molecular epidemiology and phylodynamic analysis of enterovirus 71 in Beijing, China, 2009-2019.

BACKGROUND: Enterovirus 71(EV71)-associated hand, foot and mouth disease (HFMD) decreased dramatically in Beijing from 2009 to 2019. This study was to investigate the epidemiological characteristics, evolutionary dynamics, geographic diffusion pathway, and other features of EV71 in Beijing, China. METHODS: We conducted a retrospective study of EV71-associated HFMD and its causative agent in Beijing, China, from 2009 to 2019. Phylogenetic and phylogeographic methods based on the EV71 genome were used to determine the evolution features, origin, and spatiotemporal dynamics. Positive selection sites in the VP1 gene were identified and exhibited in the tertiary structure. Bayesian birth-death skyline model was used to estimate the effective reproductive number (Re). RESULTS: EV71-associated HFMD decreased greatly in Beijing. From 2009 to 2019, EV71 strains prevalent in Beijing shared high homology in each gene segment and evolved with a rate of 4.99*10- 3 substitutions per site per year. The genetic diversity of EV71 first increased and peaked in 2012 and then decreased with fluctuations. The time to the most recent common ancestor (TMRCA) of EV71 in Beijing was estimated around 2003 when the EV71 strains were transmitted to Beijing from east China. Beijing played a crucial role in seeding EV71 to central China as well. Two residues (E145Q/G, A293S) under positive selection were detected from both the VP1 dataset and the P1 dataset. They were embedded within the loop of the VP1 capsid and were exposed externally. Mean Re estimate of EV71 in Beijing was about 1.007. CONCLUSION: In recent years, EV71 was not the primary causative agent of HFMD in Beijing. The low Re estimate of EV71 in Beijing implied that strategies for preventing and controlling HFMD were performed effectively. Beijing and east China played a crucial role in disseminating EV71 to other regions in China.

An outbreak of Coxsackievirus A6-associated hand, foot, and mouth disease in a kindergarten in Beijing in 2015.

BACKGROUND: Coxsackievirus A6 (CVA6) is one of the major agents to cause hand, foot and mouth disease (HFMD) outbreaks globally. The objective of this study is to investigate the epidemiologic and clinical manifestations of CVA6 outbreak, and thus guide the diagnosis and treatment of the disease, as well as disease prevention. METHODS: An HFMD outbreak in a kindergarten was reported to Shijingshan District Center for Disease Control and Prevention (SCDC) on November 2, 2015 in Beijing, China. Epidemiological investigation was conducted. We performed a nine-week follow-up study to collect and analyze the clinical manifestations of HFMD cases. RESULTS: The outbreak yield 56 (15.7%) clinical diagnosed HFMD cases out of 357 registered children in the kindergarten with the mean age of 3.5 years old. This outbreak lasted for three days and ceased after initiating infectious disease controlling procedures, including periodical suspension of the kindergarten activities, environmental disinfection, and family health education. Fifty-one cases were followed for nine weeks. The positive rate of clinical manifestations of rash, fever, desquamation, pigmentation and onychomadesis were 100.0%, 84.3%, 68.6%, 17.6% and 43.1%, respectively. Children developed desquamation within the first 4 weeks after disease onset and developed onychomadesis between the 3th and 8th week after disease onset. Children with desquamation had 9.3 (95%CI: 1.836-47.437) times higher odds of developing onychomadesis compared to those without this manifestation. Ten out of 14 collected samples were CVA6 positive, and five positive samples shared a high degree of similarity in the VP1 nucleotide and amino acid sequences (99.9-100.0% and 100%). CONCLUSION: This HFMD outbreak was caused by CVA6, featured with delayed symptoms. Emerging CVA6-associated HFMD and its delayed symptoms should be paid more attention to reduce outbreaks and provide more information to doctors and parents.

Evaluations of modes of pooling specimens for COVID-19 screened by quantitative PCR and droplet digital PCR.

Though pooling samples for SARS-CoV-2 detection has effectively met the need for rapid diagnostic and screening tests, many factors can influence the sensitivity of a pooled test. In this study, we conducted a simulation experiment to evaluate modes of pooling specimens and aimed at formulating an optimal pooling strategy. We focussed on the type of swab, their solvent adsorption ability, pool size, pooling volume, and different factors affecting the quality of preserving RNA by different virus solutions. Both quantitative PCR and digital PCR were used to evaluate the sampling performance. In addition, we determined the detection limit by sampling which is simulated from the virus of different titers and evaluated the effect of sample-storage conditions by determining the viral load after storage. We found that flocked swabs were better than fibre swabs. The RNA-preserving ability of the non-inactivating virus solution was slightly better than that of the inactivating virus solution. The optimal pooling strategy was a pool size of 10 samples in a total volume of 9 mL. Storing the collected samples at 4 °C or 25 °C for up to 48 h had little effect on the detection sensitivity. Further, we observed that our optimal pooling strategy performed equally well as the single-tube test did. In clinical applications, we recommend adopting this pooling strategy for low-risk populations to improve screening efficiency and shape future strategies for detecting and managing other respiratory pathogens, thus contributing to preparedness for future public health challenges.

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network.

Cylindrical organs, e.g., blood vessels, airways, and intestines, are ubiquitous structures in biomedical optical imaging analysis. Image segmentation of these structures serves as a vital step in tissue physiology analysis. Traditional model-driven segmentation methods seek to fit the structure by constructing a corresponding topological geometry based on domain knowledge. Classification-based deep learning methods neglect the geometric features of the cylindrical structure and therefore cannot ensure the continuity of the segmentation surface. In this paper, by treating the cylindrical structures as a 3D graph, we introduce a novel contour-based graph neural network for 3D cylindrical structure segmentation in biomedical optical imaging. Our proposed method, which we named CylinGCN, adopts a novel learnable framework that extracts semantic features and complex topological relationships in the 3D volumetric data to achieve continuous and effective 3D segmentation. Our CylinGCN consists of a multiscale 3D semantic feature extractor for extracting inter-frame multiscale semantic features, and a residual graph convolutional network (GCN) contour generator that combines the semantic features and cylindrical topological priors to generate segmentation contours. We tested the CylinGCN framework on two types of optical tomographic imaging data, small animal whole body photoacoustic tomography (PAT) and endoscopic airway optical coherence tomography (OCT), and the results show that CylinGCN achieves state-of-the-art performance. Code will be released at https://github.com/lzc-smu/CylinGCN.git.

Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography.

Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require extensive computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for estimating partial differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is considerably accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.

Application of 3D bioprinting technology apply to assessing Dangguiniantongtang (DGNT) decoctions in arthritis.

The aim of this study was to develop a three-dimensional (3D) cell model in order to evaluate the effectiveness of a traditional Chinese medicine decoction in the treatment of arthritis. Chondrocytes (ATDC5) and osteoblasts (MC3T3-E1) were 3D printed separately using methacryloyl gelatin (GelMA) hydrogel bioinks to mimic the natural 3D cell environment. Both cell types showed good biocompatibility in GelMA. Lipopolysaccharide (LPS) was added to the cell models to create inflammation models, which resulted in increased expression of inflammatory factors IL-1ß, TNF-α, iNOS, and IL-6, and decreased expression of cell functional genes such as Collagen II (COLII), transcription factor SOX-9 (Sox9), Aggrecan, alkaline phosphatase (ALP), RUNX family transcription factor 2 (Runx2), Collagen I (COLI), Osteopontin (OPN), and bone morphogenetic protein-2 (BMP-2). The created inflammation model was then used to evaluate the effectiveness of Dangguiniantongtang (DGNT) decoctions. The results showed that DGNT reduced the expression of inflammatory factors and increased the expression of functional genes in the cell model. In summary, this study established a 3D cell model to assess the effectiveness of traditional Chinese medicine (TCM) decoctions, characterized the gene expression profile of the inflammatory state model, and provided a practical reference for future research on TCM efficacy evaluation for arthritis treatment.

Effectiveness and Determinants of Implementing the "Xinjiang Model" for Tuberculosis Prevention and Control: A Quantitative Study.

Objective: To analyze the effectiveness of the "Xinjiang Model" for tuberculosis prevention and control in Kashgar Prefecture, Xinjiang, and to explore the determinants of the policy implementation effect. Methods: The registration data of pulmonary tuberculosis (PTB) patients in Kashgar Prefecture from 2012 to 2021 were collected to describe the temporal trend of registered incidence. A questionnaire survey was conducted among PTB patients registered and treated in the tuberculosis management information system in Zepu and Shache Counties from January 2022 to July 2023 to collect and analyze "Xinjiang model" determinants of effectiveness. Results: The PTB registered incidence in Kashgar Prefecture showed a significant increasing trend from 2012 to 2018 (APC=18.7%) and a significant decreasing trend from 2018-2021 (APC=-28.8%). Among the Kashgar Prefecture, compared with average registered incidence in 2012-2017, registered incidence in 2021 in Shufu, Maigaiti, and Zepu Counties had a greater decline rate of 58.68%, 57.16%, and 54.02%, respectively, while the registered incidence in 2021 in Shache County increased by 6.32%. According to the comprehensive analysis of the factors affecting the effect of policy implementation, the proportion of PTB patients in Zepu County whose health status has now significantly improved compared with that before treatment was significantly greater than that in Shache County (P<0.05); patients in Shache County were significantly less aware than those in Zepu County of how to take tuberculosis drugs, precautions, adverse reactions, and regular reviews during treatment; the factors that accounted for the greater proportion of heavy treatment burden in both Shache and Zepu Counties were discomfort caused by taking or injecting drugs, accounting for 12.8% and 8.7%, respectively. Conclusion: The "Xinjiang model" can effectively control the epidemic situation of tuberculosis in Kashgar, and the knowledge of tuberculosis treatment, adverse reactions to tuberculosis drugs, and treatment costs were the determinants of the effectiveness of policy implementation.

Online Learning Koopman Operator for Closed-Loop Electrical Neurostimulation in Epilepsy.

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic electroencephalogram (EEG) with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.

Dynamic three-sided matching model for personnel-robot-position matching problem in intelligent environments.

In recent years, intelligent robots have facilitated intelligent production, and a new type of problem (personnel-robot-position matching (PRPM)) has been encountered in personnel-position matching (PPM). In this study, a dynamic three-sided matching model is proposed to solve the PRPM problem in an intelligent production line based on man-machine collaboration. The first issue considered is setting the dynamic reference point, which is addressed in the information evaluation phase by proposing a method for setting the dynamic reference point based on the prospect theory. Another important issue involves multistage preference information integration, wherein a probability density function and a value function are introduced. Considering the attenuation of preference information in a time series, the attenuation index model is introduced to calculate the satisfaction matrix. Furthermore, a dynamic three-sided matching model is established. Additionally, a multi-objective decision-making model is established to optimize the matching of multiple sides (personnel, intelligent robots, and positions). Subsequently, the model is transformed into a single objective model using the triangular balance principle, which is introduced to obtain the final optimisation results in this modelling process. A case study is presented to illustrate the practicality of the dynamic three-sided matching model in intelligent environments. The results indicate that this model can solve the PRPM problem in an intelligent production line.

Automatic 3D reconstruction of an anatomically correct upper airway from endoscopic long range OCT images.

Endoscopic airway optical coherence tomography (OCT) is a non-invasive and high resolution imaging modality for the diagnosis and analysis of airway-related diseases. During OCT imaging of the upper airway, in order to reliably characterize its 3D structure, there is a need to automatically detect the airway lumen contour, correct rotational distortion and perform 3D airway reconstruction. Based on a long-range endoscopic OCT imaging system equipped with a magnetic tracker, we present a fully automatic framework to reconstruct the 3D upper airway model with correct bending anatomy. Our method includes an automatic segmentation method for the upper airway based on dynamic programming algorithm, an automatic initial rotation angle error correction method for the detected 2D airway lumen contour, and an anatomic bending method combined with the centerline detected from the magnetically tracked imaging probe. The proposed automatic reconstruction framework is validated on experimental datasets acquired from two healthy adults. The result shows that the proposed framework allows the full automation of 3D airway reconstruction from OCT images and thus reveals its potential to improve analysis efficiency of endoscopic OCT images.

In vivo co-registered hybrid-contrast imaging by successive photoacoustic tomography and magnetic resonance imaging.

Magnetic resonance imaging (MRI) and photoacoustic tomography (PAT) offer two distinct image contrasts. To integrate these two modalities, we present a comprehensive hardware-software solution for the successive acquisition and co-registration of PAT and MRI images in in vivo animal studies. Based on commercial PAT and MRI scanners, our solution includes a 3D-printed dual-modality imaging bed, a 3-D spatial image co-registration algorithm with dual-modality markers, and a robust modality switching protocol for in vivo imaging studies. Using the proposed solution, we successfully demonstrated co-registered hybrid-contrast PAT-MRI imaging that simultaneously displays multi-scale anatomical, functional and molecular characteristics on healthy and cancerous living mice. Week-long longitudinal dual-modality imaging of tumor development reveals information on size, border, vascular pattern, blood oxygenation, and molecular probe metabolism of the tumor micro-environment at the same time. The proposed methodology holds promise for a wide range of pre-clinical research applications that benefit from the PAT-MRI dual-modality image contrast.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging. Recently, deep-learning-based image deconvolution approaches have shown promise for image recovery. However, PAT imaging presents unique challenges, including spatially varying resolution and the absence of ground truth data. Consequently, there is a pressing need for a novel learning strategy specifically tailored for PAT imaging. Herein, we propose a configurable network model named Deep hybrid Image-PSF Prior (DIPP) that builds upon the physical image degradation model of PAT. DIPP is an unsupervised and deeply learned network model that aims to extract the ideal PAT image from complex system degradation. Our DIPP framework captures the degraded information solely from the acquired PAT image, without relying on ground truth or labeled data for network training. Additionally, we can incorporate the experimentally measured Point Spread Functions (PSFs) of the specific PAT system as a reference to further enhance performance. To evaluate the algorithm's effectiveness in addressing multiple degradations in PAT, we conduct extensive experiments using simulation images, publicly available datasets, phantom images, and in vivo small animal imaging data. Comparative analyses with classical analytical methods and state-of-the-art deep learning models demonstrate that our DIPP approach achieves significantly improved restoration results in terms of image details and contrast.

Does the weekend effect exist for acute type A aortic dissection?-a retrospective case-control study.

Background: The weekend effect refers to the mortality difference for patients admitted/operated on weekends compared to those on weekdays. The study aimed to provide new evidence on the impact of the weekend effect on acute type A aortic dissection (ATAAD). Methods: Primary endpoints were operative mortality, stroke, paraplegia, and continuous renal replacement therapy (CRRT). A meta-analysis of current evidence on the weekend effect was first conducted. Analyses based on single-center data (retrospective, case-control study) were further performed. Results: A total of 18,462 individuals were included in the meta-analysis. The pooled results showed that mortality was not significantly higher for ATAAD on weekends compared to that on weekdays [odds ratio (OR): 1.16, 95% CI: 0.94-1.43]. The single-center cohort included 479 patients, which also showed no significant differences in primary and secondary outcomes between the two groups. The unadjusted OR for weekend group over weekday group was 0.90 (95% CI: 0.40-1.86, P=0.777). The adjusted OR for weekend group was 0.94 (95% CI: 0.41-2.02, P=0.880) controlling for significant preoperative factors, and 0.75 (95% CI: 0.30-1.74, P=0.24) controlling for significant preoperative and operative factors altogether. In PSM matched cohort, the operative mortality was still comparable between the weekend group [10 (7.2%)] and weekday group [9 (6.5%)] (P=1.000). No significant survival difference was observed between the two groups (P=0.970). Conclusions: The weekend effect was not found to be applicable to ATAAD. However, clinicians should be cautious of the weekend effect as it is disease-specific and may vary across healthcare systems.