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The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11 × 11 or 3 × 3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

• A novel model called UGDAS is proposed for the biomedical-domain abstractive summarization task. • The model is consisted of a graph-network based denoiser and an auto-regressive generator, and it is more effective to capture the sentence relationship. • The denoiser utilizes domain knowledge to represent the rich information of the sentence in the biomedical domain. • The ranking process is optimized by the integration of sentence position information. • Our model achieves the state-of-the-art result on a recently-introduced CORD-19 dataset and outperforms the related abstractive models on the PubMed dataset. Abstractive summarization models can generate summary auto-regressively, but the quality is often impacted by the noise in the text. Learning cross-sentence relations is a crucial step in this task and the graph-based network is more effective to capture the sentence relationship. Moreover, knowledge is very important to distinguish the noise of the text in special domain. A novel model structure called UGDAS is proposed in this paper, which combines a sentence-level denoiser based on an unsupervised graph-network and an auto-regressive generator. It utilizes domain knowledge and sentence position information to denoise the original text and further improve the quality of generated summaries. We use the recently-introduced dataset CORD-19 (COVID-19 Open Research Dataset) on text summarization task, which contains large-scale data on coronaviruses. The experimental results show that our model achieves the SOTA (state-of-the-art) result on CORD-19 dataset and outperforms the related baseline models on the PubMed Abstract dataset. [ABSTRACT FROM AUTHOR]

• An average case fatality ratio over the first six months after the outbreak of COVID-19 in Poland was 5.43% (95% CI: 5.08% – 5.73%), while among hospitalized cases 16.9% (15.8% – 17.9%). • The case fatality rate had a decreasing trend in time starting from 7.83% (7.08% – 8.61%) during first eight weeks after the outbreak (March – April), to reach 2.52% (2.06% – 3.02%) during the summer season (July – August). • Recent medical care as well as the identification of the source contact, independently of the age, sex and comorbidities, significantly impact an individual mortality risk. After more than one and a half year since the COVID-19 pandemics outbreak the scientific world is constantly trying to understand its dynamics. In this paper of the case fatality rates (CFR) for COVID-19 we study the historic data regarding mortality in Poland during the first six months of pandemic, when no SARS-CoV-2 variants of concern were present among infected. To this end, we apply competing risk models to perform both uni- and multivariate analyses on specific subpopulations selected by different factors including the key indicators: age, sex, hospitalization. The study explores the case fatality rate to find out its decreasing trend in time. Furthermore, we describe the differences in mortality among hospitalized and other cases indicating a sudden increase of mortality among hospitalized cases at the end of the 2020 spring season. Exploratory and multivariate analysis revealed the real impact of each variable and besides the expected factors indicating increased mortality (age, comorbidities) we track more non-obvious indicators. Recent medical care as well as the identification of the source contact, independently of the comorbidities, significantly impact an individual mortality risk. As a result, the study provides a twofold insight into the COVID-19 mortality in Poland. On one hand we explore mortality in different groups with respect to different variables, on the other we indicate novel factors that may be crucial in reducing mortality. The later can be coped, e.g. by more efficient contact tracing and proper organization and management of the health care system to accompany those who need medical care independently of comorbidities or COVID-19 infection. [ABSTRACT FROM AUTHOR]

• The M−CDC effectively integrates the advantages of CRISPR/Cas12a, magnetic beads-based separation, and AuNP bioprobes. • M−CDC is an easy-to-operated, sensitive, and specific biosensing platform for nucleic acid assay. • The M−CDC has a great potential to be used for large-scale population SARS-CoV-2 screening in resource-limited areas. The construction of a rapid, simple, and specific nucleic acid detection platform is of great significance to the control of the large-scale spread of infectious diseases. We have recently established a magnetic pull-down-assisted colorimetric method based on the CRISPR/Cas12a system (termed M−CDC), which effectively integrates the advantages of CRISPR/Cas12a, magnetic beads-based separation, and AuNP bioprobe to provide a simple and specific biosensing platform for nucleic acid assay. The M−CDC method is compatible with point-of-care testing and enables the detection of nucleic acid samples in less than an hour without relying on expensive and complex instruments. In this paper, step-by-step instructions for M−CDC assay, including recombinase polymerase amplification (RPA)/reverse transcription-polymerase chain reaction (RT-RPA) of DNA or RNA, Cas12a-mediated target recognition and cleavage, and subsequent magnetic beads-mediated colorimetric readouts are provided. In addition, the protocol for the expression and purification of Lachnospiraceae bacterium -Cas12a (LbCas12a) protein, the design and synthesis of high-efficient crRNA, and the preparation of AuNP bioprobe are also offered. [ABSTRACT FROM AUTHOR]

Rapid detection of nucleic acids is essential for clinical diagnosis of a wide range of infectious and non-infectious diseases. CRISPR-based diagnostic platforms are well-established for rapid and specific detection of nucleic acids but suffer from a low detection sensitivity without a target pre-amplification step. Our recently developed detection system, called CRISPR-ENHANCE, employs engineered crRNAs and optimized conditions to achieve a significantly higher sensitivity and enable femtomolar levels of nucleic acid detection even without target pre-amplification. Using the CRISPR-ENHANCE platform and following the methodology detailed in this paper, nucleic acid detection for low copy numbers can be achieved in less than an hour through either a fluorescence-based detection or a lateral flow assay. The step-by-step instructions provided, in addition to describing how to perform both assays, incorporate details on a LAMP/RT-LAMP-based target amplification step to enable detection of RNA, ssDNA and dsDNA. Furthermore, a protocol for in-house expression and purification of LbCas12a using CL7/lm7-based affinity chromatography, which has been used to achieve a high yield and purity of the enzyme in a single-step, is provided. [ABSTRACT FROM AUTHOR]

• The Spatio-temporal correlations improve the precision of COVID-19 lesion detection and segmentation. • The semantic segmentation in reasonable subareas can effectively mitigate the estimation matters caused by tiny lesions. • Our methods showed excellent robustness, even on samples with severe image augmentations. In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect. Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input. Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples. The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Presented a systematic approach to learn from imbalanced set of bio-medical images. • Developed a practical "survival of the fittest" approach for hyperparameter tuning of models. • Proposed a framework to use leftout imbalanced data for pseudo-testing purpose. • Provided a publicly available chest X-ray dataset on the Kaggle platform. • Outperformed global competitors in terms of F1 and recall scores on the given dataset. The trendy task of digital medical image analysis has been continually evolving. It has been an area of prominent and growing importance from both research and deployment perspectives. Nonetheless, it is necessary to realize that the use of algorithms, methodology, as well as the source of medical image data, must be strictly scrutinized. As the COVID-19 pandemic has been gripping much of the world recently, there has been much efforts gone into developing affordable testing for the masses, and it has been shown that the established and widely available chest X-rays (CXR) images may be used as a screening criteria for assistive diagnosis purpose. Thanks to the dedicated work by various individuals and organizations, publicly available CXR of COVID-19 subjects are available for analytic usage. We have also provided a publicly available CXR dataset on the Kaggle platform. As a case study, this paper presents a systematic approach to learn from a typically imbalanced set of CXR images, which consists of a limited number of publicly available COVID-19 images. Our results show that we are able to outperform the top finishers in a related Kaggle multi-class CXR challenge. The proposed methodology should be able to help guide medical personnel in obtaining a robust diagnosis model to discern COVID-19 from other conditions confidently. [ABSTRACT FROM AUTHOR]

• Network-based approach improve SARS-CoV-2 drug repositioning performance • Aggregating local network information can promote non-linear feature interaction • Graph convolutional network utilize drug-disease-target reduce the data sparsity • Doxycycline, Cefuroxime, and Erythromycin can be used for SARS-CoV-2 treatment The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease. [ABSTRACT FROM AUTHOR]