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.

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.