CytoSorb in patients with coronavirus disease 2019: A rapid evidence review and meta-analysis.

Background: After its approval by the European Union in 2011, CytoSorb therapy has been applied to control cytokine storm and lower the increased levels of cytokines and other inflammatory mediators in blood. However, the efficiency of this CytoSorb treatment in patients with coronavirus disease (COVID-19) still remains unclear. To elucidate the Cytosorb efficiency, we conducted a systematic review and single-arm proportion meta-analysis to combine all evidence available in the published literature to date, so that this comprehensive knowledge can guide clinical decision-making and future research. Methods: The literature published within the period 1 December 2019 to 31 December 2021 and stored in the Cochrane Library, Embase, PubMed, and International Clinical Trials Registry Platform (ICTRP) was searched for all relevant studies including the cases where COVID-19 patients were treated with CytoSorb. We performed random-effects meta-analyses by R software (3.6.1) and used the Joanna Briggs Institute checklist to assess the risk of bias. Both categorical and continuous variables were presented with 95% confidence intervals (CIs) as pooled proportions for categorical variables and pooled means for continuous outcomes. Results: We included 14 studies with 241 COVID-19 patients treated with CytoSorb hemadsorption. Our findings reveal that for COVID-19 patients receiving CytoSorb treatment, the combined in-hospital mortality was 42.1% (95% CI 29.5-54.6%, I2 = 74%). The pooled incidence of adjunctive extracorporeal membrane oxygenation (ECMO) support was 73.2%. Both the C-reactive protein (CRP) and interleukin-6 (IL-6) levels decreased after CytoSorb treatment. The pooled mean of the CRP level decreased from 147.55 (95% CI 91.14-203.96) to 92.36 mg/L (95% CI 46.74-137.98), while that of IL-6 decreased from 339.49 (95% CI 164.35-514.63) to 168.83 pg/mL (95% CI 82.22-255.45). Conclusions: The majority of the COVID-19 patients treated with CytoSorb received ECMO support. In-hospital mortality was 42.1% for the COVID-19 patients who had CytoSorb treatment. Both CRP and IL-6 levels decreased after Cytosorb treatment.

Factoring and correlation in sleep, fatigue and mental workload of clinical first-line nurses in the post-pandemic era of COVID-19: A multi-center cross-sectional study.

Background: A better understanding of the factors and their correlation with clinical first-line nurses' sleep, fatigue and mental workload is of great significance to personnel scheduling strategies and rapid responses to anti-pandemic tasks in the post-COVID-19 pandemic era. Objective: This multicenter and cross-sectional study aimed to investigate the nurses' sleep, fatigue and mental workload and contributing factors to each, and to determine the correlation among them. Methods: A total of 1,004 eligible nurses (46 males, 958 females) from three tertiary hospitals participated in this cluster sampling survey. The Questionnaire Star online tool was used to collect the sociodemographic and study target data: Sleep quality, fatigue, and mental workload. Multi-statistical methods were used for data analysis using SPSS 25.0 and Amos 21.0. Results: The average sleep quality score was 10.545 ± 3.399 (insomnia prevalence: 80.2%); the average fatigue score was 55.81 ± 10.405 (fatigue prevalence: 100%); and the weighted mental workload score was 56.772 ± 17.26. Poor sleep was associated with mental workload (r = 0.303, P < 0.05) and fatigue (r = 0.727, P < 0.01). Fatigue was associated with mental workload (r = 0.321, P < 0.05). COVID-19 has caused both fatigue and mental workload. As 49% of nurses claimed their mental workload has been severely affected by COVID-19, while it has done slight harm to 68.9% of nurses' sleep quality. Conclusion: In the post-COVID-19 pandemic era, the high prevalence of sleep disorders and fatigue emphasizes the importance of paying enough attention to the mental health of nurses in first-class tertiary hospitals. Efficient nursing strategies should focus on the interaction of sleep, fatigue and mental workload in clinical nurses. In that case, further research on solutions to the phenomenon stated above proves to be of great significance and necessity. Clinical trial registration: [https://clinicaltrials.gov/], identifier [ChiCTR2100053133].

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

Time Series Based Data Explorer and Stream Analysis for Anomaly Prediction

All over the world, time series-based anomaly prediction plays a vital role in all walks of life such as medical monitoring in hospitals and climate and environment risks. In the present study, a survey on the methods and techniques for time series data mining and proposes is carried, in order to solve a brand-new problem, time series progressive anomaly prediction. In terms of contents, the first part sketches out the methods that have captured most of the interest of researchers, which include an overview of abnormal prediction problems, a summary of main characteristics of anomaly prediction, and an introduction of anomaly prediction methodology in literature. The second part focuses on the future research trends on the phase/staged abnormal prediction of time series, where a novel time series compression method and a corresponding similarity measure will be designed, which can be explored subsequently. Finally, the related challenges to take this trend are mentioned. It is hoped that this paper can provide a profound understanding of anomaly prediction for the time series-based data mining research field.

Automated detection of COVID-19 through convolutional neural network using chest x-ray images.

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

Machine Learning Applications in Drug Repurposing.

The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.

Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network.

The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19's artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.