There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15–30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results ( n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.

Resumo Objetivo Analisar a associação entre determinantes sociais e morbidades para os desfechos de internação, internação em unidade de terapia intensiva e óbito por COVID-19 no Espírito Santo, Brasil. Métodos Estudo transversal, com dados secundários de casos confirmados de COVID-19 notificados no Sistema de Informação de Agravos de Notificação. Utilizou-se regressão de Poisson para estimar as razões de prevalências. Resultados Foram estudados 104.384 casos, notificados entre 28 de fevereiro e 1º de setembro de 2020. Os desfechos em estudo foram mais frequentes entre indivíduos do sexo masculino, idosos, de raça/cor da pele amarela ou preta, sem escolaridade, com multimorbidade. Todas as morbidades associaram-se a maior risco de desfechos desfavoráveis. Observou-se maior risco de óbito entre pessoas com idade superior a 60 anos (RP=56,31 - IC95% 34,24;92,61), multimorbidades (RP=3,63 - IC95% 3,16;4,17), doença renal (RP=3,42 - IC95% 2,81;4,15) e neoplasias (RP=3,15 - IC95% 2,41;4,13). Conclusão Evidencia-se o efeito dos determinantes sociais e morbidades em internação e óbitos por COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew’s correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm –1 ), protein (∼1400 cm –1 ), and nucleic acid (∼1200–950 cm –1 ) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.

There is an urgent need for ultra-rapid testing regimens to detect the SARS-CoV-2 [Severe Acute Respiratory Syndrome Coronavirus 2] virus infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15 to 30 min processing time and are challenging to handle. Despite increased PCR-test and antigen-test efforts the pandemic has entered the worldwide second stage. Herein, we applied a superfast reagent-free and non-destructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis to the interrogation of virus-infected samples. Contrived samples with inactivated gamma-irradiated Covid-19 virus particles at levels down to 1582 copies/ml generated infrared (IR) spectra with good signal-to-noise ratio. Predominant virus spectral peaks are associated with nucleic acid bands, including RNA. At low copy numbers, the presence of virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Following on, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorisation of swab samples into negative versus positive Covid-19 infection was based on symptoms and PCR results. Following training and validation of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within two minutes and is applicable in areas with increased people traffic that require sudden test results such as airports, events or gate controls.

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Muscle damage affects the blood leukocyte profile. Resistance exercise (RE) with blood flow restriction (BFR) attenuates exercise-induced muscle damage (EIMD). Purpose To evaluate muscle damage and the leukocyte profile in response to RE+BFR and to compare with high intensity RE. Methods Twenty volunteers performed the RE in the leg press apparatus in the following groups: RE80, 80% of 1RM (3 × until concentric muscle failure); RE40+BFR, 40% of 1RM with BFR (same total work of RE80 group). The BFR applied was 80% of the total occlusion pressure. Results There were no differences in the blood leukocyte profile among groups despite the lower exercise-induced muscle damage (EIMD) in the RE40+BFR group (RE80: 10.07 ± 2.67 vs. RE40+BFR: 8.25 ± 0.96; cell × 10 3 /mm 3 ). Both groups showed leukocytosis (RE80: 7.59 ± 1.48 vs. 10.07 ± 2.67 and RE40+BFR: 6.57 ± 1.50 vs. 8.25 ± 0.96; cell × 10 3 /mm 3 ) and lymphocytosis (RE80: 2.48 ± 0.83 vs. 3.65 ± 1.31 and RE40+BFR: 2.22 ± 0.23 vs. 3.03 ± 0.65; cell × 10 3 /mm 3 ) immediately after exercise. Leukocytosis (ES 1.12 vs. ES 1.33) and lymphocytosis (ES 1.11 vs. ES 1.76) was greater in the RE40+BFR group. Conclusion RE associated with BFR was accompanied by a greater leukocytosis and lymphocytosis immediately after exercise, with no difference in neutrophils. This leukocyte blood profile may be related to less muscle damage, as well as faster muscle recovery after 24 and 48 h post-exercise.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 μL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

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