Scientific reports
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Malignant hyperthermia (MH) is a pharmacogenetic disorder of skeletal muscle metabolism characterized by generalized muscle rigidity, increased body temperature, rhabdomyolysis, hyperkalemia and severe metabolic acidosis. The underlying mechanism of MH involves excessive Ca2+ release from myotubes via the ryanodine receptor type 1 (RYR1) and the voltage-dependent L-type calcium channel (CACNA1S). As more than 300 variants of unknown significance have been detected to date, we examined whether freely available pathogenicity prediction tools are able to detect relevant MH causing variants. ⋯ All pathogenic variants and variants of unknown significance were scored as probably damaging in individuals, demonstrating a high sensitivity. Specificity was very low in one of the three tested programs. However, due to potential genotype-phenotype discordance, bioinformatic prediction tools are currently of limited value in diagnosing pathogenicity of MH-susceptible variants.
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The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. ⋯ All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.
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Despite unprecedented global efforts to rapidly develop SARS-CoV-2 treatments, in order to reduce the burden placed on health systems, the situation remains critical. Effective diagnosis, treatment, and prophylactic measures are urgently required to meet global demand: recombinant antibodies fulfill these requirements and have marked clinical potential. ⋯ The selected single and monomeric Nanobody, W25, binds to the SARS-CoV-2 S RBD with sub-nanomolar affinity and efficiently competes with ACE-2 receptor binding. Furthermore, W25 potently neutralizes SARS-CoV-2 wild type and the D614G variant with IC50 values in the nanomolar range, demonstrating its potential as antiviral agent.
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The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). ⋯ Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.
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The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has infected millions and killed more than 1.7 million people worldwide as of December 2020. Healthcare providers are at increased risk of infection when caring for patients with COVID-19. The mechanism of transmission of SARS-CoV-2 is beginning to emerge as airborne spread in addition to direct droplet and indirect contact as main routes of transmission. ⋯ A semi-sealed environment is created inside the BADGER, which is placed over the head of the patient and maintains at least 12-air changes per hour using in-wall vacuum suction. Multiple hand-ports enable healthcare providers to perform essential tasks on a patient's airway and head. Overall, the BADGER has the potential to contain large droplets and small airborne particles as demonstrated by simulated qualitative and quantitative assessments to provide an additional layer of protection for healthcare providers treating COVID-19 and future respiratory contagions.