New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning.

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.

In silico and in vitro arboviral MHC class I-restricted-epitope signatures reveal immunodominance and poor overlapping patterns.

Introduction: The present work sought to identify MHC-I-restricted peptide signatures for arbovirus using in silico and in vitro peptide microarray tools. Methods: First, an in-silico analysis of immunogenic epitopes restricted to four of the most prevalent human MHC class-I was performed by identification of MHC affinity score. For that, more than 10,000 peptide sequences from 5 Arbovirus and 8 different viral serotypes, namely Zika (ZIKV), Dengue (DENV serotypes 1-4), Chikungunya (CHIKV), Mayaro (MAYV) and Oropouche (OROV) viruses, in addition to YFV were analyzed. Haplotype HLA-A*02.01 was the dominant human MHC for all arboviruses. Over one thousand HLA-A2 immunogenic peptides were employed to build a comprehensive identity matrix. Intending to assess HLAA*02:01 reactivity of peptides in vitro, a peptide microarray was designed and generated using a dimeric protein containing HLA-A*02:01. Results: The comprehensive identity matrix allowed the identification of only three overlapping peptides between two or more flavivirus sequences, suggesting poor overlapping of virus-specific immunogenic peptides amongst arborviruses. Global analysis of the fluorescence intensity for peptide-HLA-A*02:01 binding indicated a dose-dependent effect in the array. Considering all assessed arboviruses, the number of DENV-derived peptides with HLA-A*02:01 reactivity was the highest. Furthermore, a lower number of YFV-17DD overlapping peptides presented reactivity when compared to non-overlapping peptides. In addition, the assessment of HLA-A*02:01-reactive peptides across virus polyproteins highlighted non-structural proteins as "hot-spots". Data analysis supported these findings showing the presence of major hydrophobic sites in the final segment of non-structural protein 1 throughout 2a (Ns2a) and in nonstructural proteins 2b (Ns2b), 4a (Ns4a) and 4b (Ns4b). Discussion: To our knowledge, these results provide the most comprehensive and detailed snapshot of the immunodominant peptide signature for arbovirus with MHC-class I restriction, which may bring insight into the design of future virus-specific vaccines to arboviruses and for vaccination protocols in highly endemic areas.

Timeline Kinetics of Systemic and Airway Immune Mediator Storm for Comprehensive Analysis of Disease Outcome in Critically Ill COVID-19 Patients.

In the present study, the levels of serum and airway soluble chemokines, pro-inflammatory/regulatory cytokines, and growth factors were quantified in critically ill COVID-19 patients (total n=286) at distinct time points (D0, D2-6, D7, D8-13 and D>14-36) upon Intensive Care Unit (ICU) admission. Augmented levels of soluble mediators were observed in serum from COVID-19 patients who progress to death. An opposite profile was observed in tracheal aspirate samples, indicating that systemic and airway microenvironment diverge in their inflammatory milieu. While a bimodal distribution was observed in the serum samples, a unimodal peak around D7 was found for most soluble mediators in tracheal aspirate samples. Systems biology tools further demonstrated that COVID-19 display distinct eccentric soluble mediator networks as compared to controls, with opposite profiles in serum and tracheal aspirates. Regardless the systemic-compartmentalized microenvironment, networks from patients progressing to death were linked to a pro-inflammatory/growth factor-rich, highly integrated center. Conversely, patients evolving to discharge exhibited networks of weak central architecture, with lower number of neighborhood connections and clusters of pro-inflammatory and regulatory cytokines. All in all, this investigation with robust sample size landed a comprehensive snapshot of the systemic and local divergencies composed of distinct immune responses driven by SARS-CoV-2 early on severe COVID-19.

Ex-vivo mucolytic and anti-inflammatory activity of BromAc in tracheal aspirates from COVID-19.

COVID-19 is a lethal disease caused by the pandemic SARS-CoV-2, which continues to be a public health threat. COVID-19 is principally a respiratory disease and is often associated with sputum retention and cytokine storm, for which there are limited therapeutic options. In this regard, we evaluated the use of BromAc®, a combination of Bromelain and Acetylcysteine (NAC). Both drugs present mucolytic effect and have been studied to treat COVID-19. Therefore, we sought to examine the mucolytic and anti-inflammatory effect of BromAc® in tracheal aspirate samples from critically ill COVID-19 patients requiring mechanical ventilation. METHOD: Tracheal aspirate samples from COVID-19 patients were collected following next of kin consent and mucolysis, rheometry and cytokine analysis using Luminex kit was performed. RESULTS: BromAc® displayed a robust mucolytic effect in a dose dependent manner on COVID-19 sputum ex vivo. BromAc® showed anti-inflammatory activity, reducing the action of cytokine storm, chemokines including MIP-1alpha, CXCL8, MIP-1b, MCP-1 and IP-10, and regulatory cytokines IL-5, IL-10, IL-13 IL-1Ra and total reduction for IL-9 compared to NAC alone and control. BromAc® acted on IL-6, demonstrating a reduction in G-CSF and VEGF-D at concentrations of 125 and 250 µg. CONCLUSION: These results indicate robust mucolytic and anti-inflammatory effect of BromAc® ex vivo in tracheal aspirates from critically ill COVID-19 patients, indicating its potential to be further assessed as pharmacological treatment for COVID-19.

A chimeric HLA-A2:ß2M:Ig fusion protein for the study of virus-specific CD8+ T-cells.

INTRODUCTION: The response mediated by CD8+ T-cells in the context of infection and vaccination has been thoroughly investigated and represents one of the most important branches that allow for the development of immunity against intracellular pathogens and, thus, the establishment of robust antiviral responses. However, there is a lack of methods to assess antigen-specific CD8+ T-cells. OBJECTIVE: Search for the ideal assays to assess the function of antigen-specific CD8+ T-cells. METHODS: In the present study a chimeric HLA-A2:ß2M:Ig fusion protein was produced, purified, and evaluated in functional CD8+ T-cell response studies using samples from Influenza A patients and humanized mice upon adenoviral vaccination. RESULTS: The HLA-A2:ß2M:Ig molecule, bound to immunodominant viral peptides by passive transfer, was able to induce robust antiviral CD8+ T-cell responses mediated by IFN-γ. The in vitro IFN-γ release assay using the chimeric HLA-A2:ß2M:Ig fusion protein detected bona fide human CD8+ T-cells, demonstrating superior production of IFN-γ by human CD8+ T-cells induced by Influenza A immunodominant GILGFVFTL peptide. Removal of antigen-presenting cells and CD8+ T-cell enrichment improved significantly the IFN-γ production. The chimeric HLA-A2:ß2M:Ig fusion protein also triggered HLA-A2-restricted CD8+ T-cell response in a humanized mouse model upon vaccination with adenovirus encoding HLA-A2-restricted HIV p24 antigen. The results strongly suggest the use of tailor-made assays for detecting HLA-A2-restricted CD8+ T-cell Responses in the Humanized Mouse Model. CONCLUSION: The chimeric HLA-A2:ß2M:Ig fusion protein-based assays provided a sensitive tool that may be paramount to measure virus-specific CD8+ T-cell response in a range of viral infections of clinical relevance.