SARS-CoV-2 virus classification based on stacked sparse autoencoder.

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural network based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.

Inflammatory and Hemostatic Markers in COVID-19 Patients with Arterial Thrombosis Are Significantly Lower at Hospital Admission than in COVID-19 Patients without Thrombosis.

Patients with Coronavirus disease 2019 (COVID-19) are at increased risk of venous thromboembolism (VTE); however, data on arterial thromboembolism (ATE) is still limited. We report a case series of thromboembolic events (TE) in 290 COVID-19 patients admitted between October and December 2020 to a Portuguese hospital. Admission levels of various laboratory parameters were evaluated and compared between COVID-19 patients with (TE) and without thrombotic events (non-TE). The overall incidence of isolated ATE was 5.52%, isolated VTE was 2.41% and multiple mixed events was 0.7%. A total of 68% events were detected upon admission to the hospital with 76% corresponding to ATE. Admissions to the Intensive Care Unit were higher in patients with TE, when comparing with the non-TE group (44% vs. 27.2%; p = 0.003). Patients with ATE presented significantly lower levels of CRP (p = 0.007), ferritin (p = 0.045), LDH (p = 0.037), fibrinogen (p = 0.010) and higher monocyte counts (p = 0.033) comparatively to the non-TE patients. These results point to an early occurrence of TE and an increased incidence of ATE over VTE. The less prominent inflammation markers in patients with TE and the early presence of TE in patients with otherwise no reason for hospitalization, may suggest a direct role of SARS-CoV-2 in the thrombotic process.

Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification.

COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organization. On 17 January 2022, there were more than 329 million cases, with more than 5.5 million deaths. Although COVID-19 has a low mortality rate, its high capacities for contamination, spread, and mutation worry the authorities, especially after the emergence of the Omicron variant, which has a high transmission capacity and can more easily contaminate even vaccinated people. Such outbreaks require elucidation of the taxonomic classification and origin of the virus (SARS-CoV-2) from the genomic sequence for strategic planning, containment, and treatment of the disease. Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network (CNN). Unlike the other literature, the proposed approach does not limit the length of the genome sequence. The results show that the novel proposal accurately distinguishes SARS-CoV-2 from the sequences of other viruses. The results were obtained from 1557 instances of SARS-CoV-2 from the National Center for Biotechnology Information (NCBI) and 14,684 different viruses from the Virus-Host DB. As a CNN has several changeable parameters, the tests were performed with forty-eight different architectures; the best of these had an accuracy of 91.94 ± 2.62% in classifying viruses into their realms correctly, in addition to 100% accuracy in classifying SARS-CoV-2 into its respective realm, Riboviria. For the subsequent classifications (family, genera, and subgenus), this accuracy increased, which shows that the proposed architecture may be viable in the classification of the virus that causes COVID-19.

Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil.

OBJECTIVES: To analyse the spatial distribution of rates of COVID-19 cases and its association with socio-economic conditions in the state of Pernambuco, Brazil. METHODS: Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio-economic factors on rates. RESULTS: Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID-19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID-19. CONCLUSIONS: Our results provide important information on socio-economic factors related to the spread of COVID-19 and can serve as a basis for decision-making in similar circumstances.

Representations and social anchorages of the new coronavirus and the COVID-19 treatment by Brazilians

Objetivou-se neste estudo apreender a gênese das representações sociais do novo coronavírus, bem como do tratamento da COVID-19, considerando-se diferentes ancoragens sociais de brasileiros. Contou-se com 595 participantes, predominantemente do sexo feminino (69,9%) e da região Nordeste do Brasil (64,9%). Os dados, coletados através de um questionário online, permitiram análises de Classificações Hierárquicas Descendentes, indicando que a gênese das representações sociais do novo coronavírus é marcada por preocupações relativas à sua disseminação e implicações psicossociais e afetivas. Já o campo representacional do tratamento enfatiza a remissão ou a amenização dos sintomas causados pela COVID-19. As variações nas representações sociais identificadas nesta pesquisa, em função dos diferentes grupos sociais, indicam que futuras intervenções devem considerar as especificidades de cada um deles na disseminação de representações e práticas sociais direcionadas para conter o estado pandêmico. This study aimed to apprehend the genesis of the Social Representations of the new coronavirus, as well as of the treatment of the COVID-19, considering Brazilian people's different social anchorages. For that purpose, an online questionnaire was answered by 595 participants, predominantly female (69.9%) and from the Northeastern region of Brazil (64.9%). The data collected allowed analyzes of Descending Hierarchical Classifications, indicating that the new coronavirus Social Representations genesis is marked by concerns regarding its dissemination and its psychosocial and affective implications. On the other hand, the representational field of the treatment emphasizes the remission or alleviation of symptoms caused by COVID-19. Given the differences between social groups, the Social Representations variations identified in this research indicate that future interventions should consider each group's specificities in the dissemination of representations and social practices aiming at containing the pandemic state.