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.

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.

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. 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 infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k = 6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

The Effect of Fish Oil, Vitamin D and Protein on URTI Incidence in Young Active People.

Upper respiratory tract infections (URTI) are a frequent illness among athletes. We investigated the effect of a multi-nutrient supplement (vitamin D, fish oil and protein) on the occurrence of URTI in young active people. 42 young recreational athletes were randomly assigned to receive either supplementation (550 mg DHA, 550 mg EPA, 10 µg vitamin D3 and 8 g whey protein) or placebo for 16 weeks. Unstimulated saliva samples were collected by passive drool. Samples were analysed for IgA (sIgA) concentration and the secretion rate extrapolated by multiplying concentration by saliva flow rate. Physical activity levels and URTI incidence were monitored by questionnaire. Training status was not different between the 2 groups. There were no differences in the incidence, severity and duration of URTI. However the number of symptom days was lower in the supplemented compared to the control group (1.72±1.67 vs. 2.79±1.76; P<0.05). sIgA concentration and secretion rate did not differ between groups. This study demonstrates that 16 weeks of supplementation with fish oil, vitamin D and protein did not modify the incidence, severity and duration of URTI, although the total number of symptom days was reduced, in a healthy active population.

A broad spectrum of clinical presentations in congenital disorders of glycosylation I: a series of 26 cases.

INTRODUCTION: Congenital disorders of glycosylation (CDG), or carbohydrate deficient glycoprotein syndromes, form a new group of multisystem disorders characterised by defective glycoprotein biosynthesis, ascribed to various biochemical mechanisms. METHODS: We report the clinical, biological, and molecular analysis of 26 CDG I patients, including 20 CDG Ia, two CDG Ib, one CDG Ic, and three CDG Ix, detected by western blotting and isoelectric focusing of serum transferrin. RESULTS: Based on the clinical features, CDG Ia could be split into two subtypes: a neurological form with psychomotor retardation, strabismus, cerebellar hypoplasia, and retinitis pigmentosa (n=11), and a multivisceral form with neurological and extraneurological manifestations including liver, cardiac, renal, or gastrointestinal involvement (n=9). Interestingly, dysmorphic features, inverted nipples, cerebellar hypoplasia, and abnormal subcutaneous fat distribution were not consistently observed in CDG Ia. By contrast, the two CDG Ib patients had severe liver disease, enteropathy, and hyperinsulinaemic hypoglycaemia but no neurological involvement. Finally, the CDG Ic patient and one of the CDG Ix patients had psychomotor retardation and seizures. The other CDG Ix patients had severe proximal tubulopathy, bilateral cataract, and white matter abnormalities (one patient), or multiorgan failure and multiple birth defects (one patient). CONCLUSIONS: Owing to the remarkable clinical variability of CDG, this novel disease probably remains largely underdiagnosed. The successful treatment of CDG Ib patients with oral mannose emphasises the paramount importance of early diagnosis of PMI deficiency.