Managing indoor air quality in nursery and primary schools with low-cost solutions

This study aimed to evaluate the feasibility of using low-cost solutions to monitor and mitigate PM2.5 and PM10 concentrations in nursery and primary schools in Porto (Portugal). Three periods were considered: i) early 2020 (before COVID-19 pandemic), ii) early 2021 (during COVID-19 pandemic, with mitigation measures to prevent SARS-CoV-2 spread);and iii) in the middle of 2021 (additionally using a low-cost portable air cleaner). PM2.5 and PM10 were continuously monitored with a low-cost sensing device for at least two consecutive days in five classrooms. In general, the lowest PM concentrations were observed in the third period. Concentrations reduced up to 63% from the second to the third period. The application of low-cost solutions for monitoring and mitigating PM levels seems to be an effective tool for managing indoor air in schools. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Spread of Gamma (P.1) Sub-Lineages Carrying Spike Mutations Close to the Furin Cleavage Site and Deletions in the N-Terminal Domain Drives Ongoing Transmission of SARS-CoV-2 in Amazonas, Brazil

The Amazonas was one of the most heavily affected Brazilian states by the COVID-19 epidemic. Despite a large number of infected people, particularly during the second wave associated with the spread of the Variant of Concern (VOC) Gamma (lineage P.1), SARS-CoV-2 continues to circulate in the Amazonas. To understand how SARS-CoV-2 persisted in a human population with a high immunity barrier, we generated 1,188 SARS-CoV-2 whole-genome sequences from individuals diagnosed in the Amazonas state from 1st January to 6th July 2021, of which 38 were vaccine breakthrough infections. Our study reveals a sharp increase in the relative prevalence of Gamma plus (P.1+) variants, designated Pango Lineages P.1.3 to P.1.6, harboring two types of additional Spike changes: deletions in the N-terminal (NTD) domain (particularly Delta 144 or Delta 41-144) associated with resistance to anti-NTD neutralizing antibodies or mutations at the S1/S2 junction (N679K or P681H) that probably enhance the binding affinity to the furin cleavage site, as suggested by our molecular dynamics simulations. As lineages P.1.4 (S:N679K) and P.1.6 (S:P681H) expanded (Re > 1) from March to July 2021, the lineage P.1 declined (Re < 1) and the median Ct value of SARS-CoV-2 positive cases in Amazonas significantly decreases. Still, we did not find an increased incidence of P.1 variants among breakthrough cases of fully vaccinated patients (71%) in comparison to unvaccinated individuals (93%). This evidence supports that the ongoing endemic transmission of SARS-CoV-2 in the Amazonas is driven by the spread of new local Gamma/P.1 sublineages that are more transmissible, although not more efficient to evade vaccine-elicited immunity than the parental VOC. Finally, as SARS-CoV-2 continues to spread in human populations with a declining density of susceptible hosts, the risk of selecting more infectious variants or antibody evasion mutations is expected to increase. IMPORTANCE The continuous evolution of SARS-CoV-2 is an expected phenomenon that will continue to happen due to the high number of cases worldwide. The present study analyzed how a Variant of Concern (VOC) could still circulate in a population hardly affected by two COVID-19 waves and with vaccination in progress. Our results showed that the answer behind that was a new generation of Gamma-like viruses, which emerged locally carrying mutations that made it more transmissible and more capable of spreading, partially evading prior immunity triggered by natural infections or vaccines. With thousands of new cases daily, the current pandemics scenario suggests that SARS-CoV-2 will continue to evolve and efforts to reduce the number of infected subjects, including global equitable access to COVID-19 vaccines, are mandatory. Thus, until the end of pandemics, the SARS-CoV-2 genomic surveillance will be an essential tool to better understand the drivers of the viral evolutionary process.

Deep Learning-Based COVID-19 Diagnostics of Low-Quality CT Images

Mass testing of the population is among the most effective measures to combat the COVID-19 pandemic. Among existing diagnostic methods, deep learning-based solutions have the potential to be affordable, quick and accurate. However, these techniques often rely on high-quality datasets, which are not always available in medical scenarios. In this work, we use convolutional neural networks to diagnose COVID-19 on computed tomography images from the COVIDx-CT dataset [6]. The available scans often present noisy artifacts, originated from sensor- and capturing-related errors, that can negatively impact the performance of the model if left untreated. In this sense, we explore several preprocessing strategies to reduce their impact and obtain a more accurate method. Our best model, a ResNet50 fine-tuned with preprocessed images, obtained 97.84 % accuracy when prompted with a single image and 99.50 % when processing multiple images from the same patient. In addition to achieving high accuracy, interpretability experiments show that the network correctly learned features from the lung and chest area. © 2021, Springer Nature Switzerland AG.