COVID-19 WAS NOT TRIGGER FOR RHEUMATIC DISEASE ACTIVITY AFTER 6 MONTHS FOLLOW-UP: RESULTS FROM REUMACOV BRASIL REGISTER

BackgroundThe COVID-19 pandemic has brought uncertainties to rheumatology practice, mainly related to the possibility of triggering disease activity after infection in immune mediated rheumatic diseases (IMRD). To date, there are few data in the literature specifically evaluating this issue.ObjectivesEvaluate the disease activity in IMRD patients after 6 months of the infection, compared to pre infection status.MethodsReumaCoV Brasil is a longitudinal study performed at 35 study centers designed to follow-up IMRD patients for 6 months after clinical or laboratorial COVID-19 diagnosis (cases), comparing with patients with IMRD who had not had the infection at the time of inclusion (controls). Demographic data such as age, sex, comorbidities, clinical characteristics, treatment, evolution of COVID-19 and disease activity status were collected using a Research Eletronic Data Capture (REDCap) database on three consecutive visits (inclusion and 6 months). The analysis was carried out on the four diseases with the highest inclusion number in the study: systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA). In addition to specific disease activity assessment metrics, we used patient's global assessment of disease activity (PGA), ranging from 0 to 10, at all visits, with 0 being no activity and 10 being intense activity. All conclusions were drawn considering the significance level of 5%. This study was registered at the Brazilian Registry of Clinical Trials—REBEC, RBR-33YTQC. All patients read and signed the informed consent form before inclusion.ResultsBetween May 2020 and January 2021, 2032 patients were included in the registry, and of these, 1322 patients (721 cases and 601 controls), completed 6 months of follow-up, being 550 SLE (42.0%), 497 RA (37.6%) and 176 SpA (13.3%) and 99 (7.4%) PsA. Most patients were female (82.0%);the median age was 46.7 (13.8). Disease activity at the time of enrollment, according to the PGA, was similar between cases and controls, except for patients with RA and AS, where it was higher in controls. After the follow up time, no worsening of activity was observed in any of the diseases evaluated in the case group (Table 1). Despite this, worsening of disease symptoms after COVID-19 was reported by 23.3%, 24.6%, 25.0% and 25.8% of patients with SLE, RA, AS and PsA respectively, not related with disease activity.ConclusionIn patients with IMRD, no worsening of disease activity was observed after COVID-19 in this cohort of Brazilian patients. Despite this, many patients noticed worsening of symptoms, possibly associated not with the triggering of the activity, but with the so-called long COVID syndrome.Table 1.Comparison of disease activity, according to PGA, comparing disease activity status at inclusion and after 6 months of follow up, in cases and controlsINCLUSIONAFTER 6 MONTHSCasesControlsp-valueCasesControlsp-valueSLE2 (0-4,5)2 (0-4)0,8102 (0-5)2 (0-4)0,172RA3 (1-5)4 (2-6)0.0013 (1-5)3 (1-5,5)0,731AS2 (0-5)4 (1-6)0,0022 (0-5)3,5 (1-6)0,044PsA2 (0-4)2 (0-5)0,8162 (0-5)2 (0-5)0,939*Median and interquatile range;Student t test;CI 95%AcknowledgementsReumaCoV Brasil researchers, Brazilian Society of Rheumatology and National Council for Ccientific and Technological Development.Disclosure of InterestsNone Declared.

Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

In December 2019 an outbreak of a new disease happened, in Wuhan city, China, in which the symptoms were very similar to pneumonia. The disease was attributed to SARS-CoV-2 as the infectious agent and it was called the new coronavirus or Covid-19. In March 2020, the World Health Organization declared a worldwide pandemic of the new coronavirus. We have already counted more than 110 million cases and almost 2.5 million deaths worldwide. In order to assist in decision-making to contain the disease, several scientists around the world have engaged in various efforts, and they have proposed a lot of systems and solutions for tracking, monitoring, and predicting confirmed cases and deaths from Covid-19. Mathematical models help to analyze and understand the evolution of the disease, but understanding the disease was not enough, it was necessary to understand the problem in a quantitative way to lead the decision-making during the pandemic. Several initiatives have made use of Artificial Intelligence, and models were designed using machine learning algorithms with features for temporal and spatio-temporal investigation and prediction of cases of Covid-19. Among the algorithms used are Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Graph Neural Networks (GNNs), Ecological Niche Models (ENMs), Long-Short Term Memory Networks (LSTM), linear regression, and others. And these had good results, and to analyze them, the Root Mean Squared Error (RMSE), Log Root Mean Squared Error (RMSLE), correlation coefficient, and others were used as metrics. Covid-19 presents a huge problem to public health worldwide, so it is of utmost importance to investigate it, and with these two approaches it is possible to track not only how the disease evolves but also to know which areas are at risk. And these solutions can help in supporting decision-making by health managers to make the best decisions for the disease that is in the outbreak. This chapter aims to present a literature review and a brief contribution to the use of machine learning methods for temporal and spatio-temporal prediction of Covid-19, using Brazil and its federative units as a case study. From canonical methods to deep networks and hybrid committee-based, approaches will be investigated. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Related factors for health behaviors among young students during the pandemic of COVID-19

Objective: to analyze factors related to health behaviors among young students during the pandemic of COVID-19. Methods: cross-sectional study conducted with 282 students aged 18 to 24 years from the Federal Network of Vocational and Technological Education. An electronic form and the Adolescent Health Promotion Scale (Brazilian version) instrument were applied for statistical analysis and multiple linear regressions were used. Results: the overall mean of the scale was 3.29 (Standard deviation (SD)=0.56);the subscales ranged from 2.40 (SD=1.0) physical exercise to 3.76 (SD=0.80) valuing life. We identified that the subscale physical exercise and some items of the subscales social support and responsibility for health presented scores below the mean. Conclusion: according to the theoretical model tested, students in technical courses showed a statistically significant negative relationship in the scores of the general health promotion scale. The subscales of social support, responsibility for health and physical exercise showed statistically significant relationships in relation to the variables gender, age group, family income, level of education and health problem. Contributions to practice: nurses should know the factors related to health behaviors among young students to expand and guide actions, contributing to the improvement of student assistance and care for young people.

Sleeping well project: dissemination of information on sleep health in the COVID 19 pandemic course

Introduction: With the confrontation of the COVID-19 pandemic, there was a drastic change in the daily life routines, with reflexes on the general health and impacts on the sleep quality of many Brazilians. This fact leads to a glimpse of the potential existing in virtual actions with a wide population reach, focused on health promotion and disease prevention as an alternative to minimize damage to quality of life. Objective: To disseminate safe information in a virtual environment to the general population in a simple and illustrative way about sleep health. Methods: The university extension project has been underway since October 2020 and has the participation of 10 undergraduate students from the Speech Therapy course and two advisor professors. The first stage of the project was aimed at training and updating the team, by remote means, on issues involving sleep health;in the second stage, there was the creation of a public profile on the social network and the elaboration/confection/publication of illustrative informative materials and with simple and comprehensive language (videos, posters, comics, leaflets, technical sheets and the like);the third stage featured/relies on live virtual actions focused on health promotion and prevention through lives, seminars and open classes conducted by professionals from different health areas and sleep specialists. Results: Between October 2020 and August 2021 the digital profile published 39 posts, IGTV or Reels with informative content about sleep, in addition to more than 200 stories. More than 3000 “likes or likes” were registered in the publications, 3,507 views on IGTV, 347 comments, 381 shares (of which 150 were saved by followers or visitors), 8,280 visits to the profile and 18,524 people/accounts were reached for a virtual audience of 1,731 people/following accounts located in Sergipe, São Paulo, Pernambuco and Bahia, in addition to a small percentage of Spanish, Portuguese, Colombian and German followers. It is also important to highlight that the project awakens in the students the relevance of disseminating information based on reliable sources. Conclusion: Through the execution of the project, important and safe information can be disseminated in a virtual environment to clarify, inform and popularize science with knowledge about sleep health and the prevention of its disorders to an expressive public.

Mesoscopic Evaluation of DNA Mismatches in PCR Primer-Target Hybridisation to Detect SARS-CoV-2 Variants of Concern

Mismatches are any type of base-pairs other than AT and CG. They are an expected occurrence in PCR primer-target hybridisation and may interfere with the amplification and in some cases even prevent the detection of viruses and other types of target. Given the natural occurrence of mutations it is expected that the number of primer-target mismatches increases which may result in a larger number of false-negative PCR diagnostics. However, mismatches may equally improve the primer-target hybridisation since some types of mismatches may stabilize the helix. Only very recently have thermodynamic parameters become available that would allow the prediction of mismatch effects at buffer conditions similar to that of PCR. Here we collected primers from WHO recommendation and aligned them to the genomes of the current variants of concern (VOC): Alpha, Beta, Gamma and Delta variants. We calculated the hybridisation temperatures taking into account up to three consecutive mismatches with the new parameters. We assumed that hybridisation temperatures to mismatched alignments within a range of 5 ∘ C of the non-mismatched temperature to still result in functional primers. In addition, we calculated strict and partial coverages for complete and mismatched alignments considering only single, double and triple consecutive mismatches. We found that if mismatches are taken into account, the coverage of WHO primers actually increase for VOCs and for the Delta variant it becomes 100%. This suggest that, at least for the moment, these primers should continue to be effective for the detection of VOCs. © 2021, Springer Nature Switzerland AG.

Human rights, vulnerability and vulneration of Brazilian indigenous peoples coping with Covid-19

Human rights, vulnerability and vulneration of Brazilian indigenous peoples coping with Covid-19 This theoretical essay aims to discuss the conditions of production of vulnerability and vulneration of indigenous peoples in Brazil coping with Covid-19. We begin with an overview of the processes of denial of rights and vulnerability that intensified in the Bolsonaro government. We finally highlight the components of vulnerability and vulneration that affect Brazilian indigenous peoples in confronting Covid-19. Results showed that indigenous peoples have multiple vulnerabilities related to their socio historical and cultural contexts, which in Brazil are aggravated by a neocolonialist and authoritarian politic. We clearly concluded that Brazilian indigenous peoples are not only exposed to the condition of vulnerability, but to vulneration facing with Covid 19. The study contributes for understanding the implications of the absence of equitable conditions of protection against the coronavirus regarding to the high mortality of indigenous people due to Covid-19.