COVID-19 impedimetric biosensor based on polypyrrole nanotubes, nickel hydroxide and VHH antibody fragment: specific, sensitive, and rapid viral detection in saliva samples.

SARS-CoV-2 rapid spread required urgent, accurate, and prompt diagnosis to control the virus dissemination and pandemic management. Several sensors were developed using different biorecognition elements to obtain high specificity and sensitivity. However, the task to achieve these parameters in combination with fast detection, simplicity, and portability to identify the biorecognition element even in low concentration remains a challenge. Therefore, we developed an electrochemical biosensor based on polypyrrole nanotubes coupled via Ni(OH)2 ligation to an engineered antigen-binding fragment of heavy chain-only antibodies (VHH) termed Sb#15. Herein we report Sb#15-His6 expression, purification, and characterization of its interaction with the receptor-binding domain (RBD) of SARS-CoV-2 in addition to the construction and validation of a biosensor. The recombinant Sb#15 is correctly folded and interacts with the RBD with a dissociation constant (KD) of 27.1 ± 6.4 nmol/L. The biosensing platform was developed using polypyrrole nanotubes and Ni(OH)2, which can properly orientate the immobilization of Sb#15-His6 at the electrode surface through His-tag interaction for the sensitive SARS-CoV-2 antigen detection. The quantification limit was determined as 0.01 pg/mL using recombinant RBD, which was expressively lower than commercial monoclonal antibodies. In pre-characterized saliva, both Omicron and Delta SARS-CoV-2 were accurately detected only in positive samples, meeting all the requirements recommended by the World Health Organization for in vitro diagnostics. A low sample volume of saliva is needed to perform the detection, providing results within 15 min without further sample preparations. In summary, a new perspective allying recombinant VHHs with biosensor development and real sample detection was explored, addressing the need for accurate, rapid, and sensitive biosensors.

YOUTUBEASASOURCE OFINFORMATIONABOUT UNPROVENDRUGSFOR COVID-19: the role of the mainstream media and recommendation algorithms in promoting misinformation

In this study, we address how YouTube videos promote misinformation about hydroxychloroquine in Brazil. We follow two research questions. RQ1: How is pro-hydroxychloroquine content propagated on YouTube? RQ2: How does YouTube's recommendation system suggest videos about hydroxychloroquine on the platform? We use mixed methods (content analysis and social network analysis) to analyze 751 YouTube videos. We found that most pro-HCQ videos in our dataset are posted by mainstream media channels (RQ1) and that YouTube was more likely to recommend pro-HCQ videos than anti-HCQ videos (RQ2). Consequently, the Brazilian mainstream media and YouTube's algorithms fueled the spread of pro-HCQ content. © 2022 Associacao Brasileira de Pesquisadores de Jornalismo. All rights reserved.

Artificial Neural Networks and BPPC Features for Detecting COVID-19 and Severity Level

Since vaccination started, the COVID-19 scenario has improved. On the other hand, although the number of deaths has significantly dropped, the number of new cases is still a concern. Thus, patient tracking and follow-up are essential tasks, and chest X-ray examination is the first-order tool. While several studies using CXR and computing have been developed, they did not translate into clinical applications yet. One of the reasons is the computational effort required to run huge deep learning models and its high cost to be adopted in community clinics. Therefore, this work proposes a lightweight (few computational resources needed), fast (training and inference time), and reasoned solution for automatic COVID-19 detection and assessment of its severity. Our method is based on extracting features by Binary Pattern of Phase Congruency (BPPC) in segmented CXR images. Radiomic features are extracted from the segmented CXR image, and an SVM-based selection process is used to build two models of a shallow Feed-Forward network. The results surpass previous studies, with an average accuracy for COVID-19 detection of 98.71%. For images without evidence of infection but with a positive PCR test, an accuracy of 94.74% is reached. In a second task, the severity level of COVID 19 is estimated with an AUC of 98.92%. This high performance helps improve the speed and accuracy of diagnosis and severity assessment of COVID19 infection, proving to be a viable option in transitioning from a research field to a clinical environment. © 2022 IEEE.

Pocket-Sized Portable Labs: Control Engineering Practice Made Easy in Covid-19 Pandemic Times

New pocket-sized laboratories are proving to be an excellent tool as complementary equipment that students and lecturers can deploy to test control engineering design techniques. Here, the description and outcome results of an IFAC activity funded project entitled as Pocket-Sized Portable Labs: Control Engineering Practice Made Easy are presented. The project was executed in Portugal, from January 2021 to the end of June 2021, during the SARS-CoV2 pandemic. The global aim of this project was to motivate preuniversity students to enroll in control engineering courses by showing and demonstrating that simple practical experiments may be easily accomplished using portable pocket-size laboratories. © 2022 The Authors.

Non-generalisability of biomarkers for mortality in SARS-CoV-2

P169 Figure 1ConclusionsBiomarker effectiveness varies significantly by geographical location. To track these changes we have mapped the root studies on the following website (https://covid19.cimr.cam.ac.uk/) This has significant implications for prognosticating SARS-CoV-2 and also for future pandemics.

Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline

As long as the COVID-19 pandemic is still active in most countries worldwide, rapid diagnostic continues to be crucial to mitigate the impact of seasonal infection waves. Commercialized rapid antigen self-tests proved they cannot handle the most demanding periods, lacking availability and leading to cost rises. Thus, developing a non-invasive, costless, and more decentralized technology capable of giving people feedback about the COVID-19 infection probability would fill these gaps. This paper explores a sound-based analysis of vocal and respiratory audio data to achieve that objective. This work presents a modular data-centric Machine Learning pipeline for COVID-19 identification from voice and respiratory audio samples. Signals are processed to extract and classify relevant segments that contain informative events, such as coughing or breathing. Temporal, amplitude, spectral, cepstral, and phonetic features are extracted from audio along with available metadata for COVID-19 identification. Audio augmentation and data balancing techniques are used to mitigate class disproportionality. The open-access Coswara and COVID-19 Sounds datasets were used to test the performance of the proposed architecture. Obtained sensitivity scores ranged from 60.00% to 80.00% in Coswara and from 51.43% to 77.14% in COVID-19 Sounds. Although previous works report higher accuracy on COVID-19 detection, this research focused on a data-centric approach by validating the quality of the samples, segmenting the speech events, and exploring interpretable features with physiological meaning. As the pandemic evolves, its lessons must endure, and pipelines such as the proposed one will help prepare new stages where quick and easy disease identification is essential.

Children's Impressions of Early Spelling Assessment through Handwriting on Tablets vs. Paper-based Method

Traditional paper-based children's spelling assessments were hampered due to Covid-19 because existing technologies did not provide strategic signals to teachers, such as the child's handwriting direction and how they read what they write. Our project emerged as a novel method to assess children's spelling by touchscreens in this context. Hence, this paper aims to extend community knowledge concerning children's experience and perception of handwriting spelling on tablet devices. The experiment consisted in presenting three handwriting methods (paper and pencil, finger and pen writing) and was conducted with eight Brazilian children between 4.5 and 7 years old. In addition to observation, in our experimental protocol we adopted the Fun Sorter, Again-Again Table, and the Smileyometer as evaluation tools. Our results show children were excited about handwriting using a touch pen on the tablet. Most of them even revealed they prefer the pen tablet mode to the traditional paper and pencil mode. However, the majority of children did not feel comfortable writing by finger, and it required more time than other methods. Furthermore, we observed child's handwriting using finger looks different when compared to paper and pencil, while the tracing using a touch pen is similar to the registration produced on paper. © 2022 Owner/Author.

How we turned fully digital due to covid-19: Two control engineering teaching experiences

While living in a digital era, both teachers and students of Engineering Courses were not ready for the drastic change associated with the Covid-19 first confinement (March 2020). This forced change from a presential mode to a fully online mode provided teaching/leaning difficulties as well as new opportunities. Moreover, as most engineering courses require laboratory practice, on-line teaching raised additional challenges. This paper reports two different experiences in two different Control Engineering university courses in the North of Portugal. The goal is to share some learning tools that are particularly relevant in the pandemic time we are living: pocket-sized laboratory kits that students can easily take home and experience real-world control contents;an open Mural that can serve as an exchange of knowledge. Perceptions received both from students and lecturers regarding these two experiments are presented. © 2021 IEEE.

Bolsonaro and the Far Right: How Disinformation About COVID-19 Circulates on Facebook in Brazil

This article tackles the circulation of disinformation and compares it to fact-checking links about COVID-19 on Facebook in Brazil. Through a mixed-methods approach, we use disinformation and fact-checking links provided by the International Fact-Checking Network/Poynter, which we looked for in CrowdTangle. Using this data set, we explore (1) which types of public groups/pages spread disinformation and fact-checking content on Facebook;(2) the role of political ideology in this process;and (3) the network dynamics of how disinformation and fact-checking circulate on Facebook. Our results show that disinformation tend to circulate more on political pages/groups aligned with the far right and Brazilian President Jair Bolsonaro, on religious and conspiracy theory pages/groups and alternative (hyperpartisan) media. On the other hand, fact-checking circulates more on leftists’ pages/groups. This implicates that the discussion about COVID-19 in Brazil is influenced by a structure of asymmetric polarization, as disinformation spread is fueled by radicalized far-right groups © 2022 (Raquel Recuero, Felipe Bonow Soares, Otávio Vinhas, Taiane Volcan, Luís Ricardo Goulart Hüttner, and Victória Silva). Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd). Available at http://ijoc.org

Virtual Lab Virtues in Distance Learning

Since the start of the COVID-19 pandemic, Portuguese schools have been closed twice. This fact causes teachers to quickly adapt the content of learning to the characteristics of distance learning. In some courses, this adaption is straightforward, although all activities on lab were limited to videos of experiments without any interaction by the students. These aspects could be very prejudicial to the learning process since the success of students in lab is proportional to the experimentation and training. To address this problem, a design science research-based project was launched to develop a prototype of a virtual lab that could allow the students the training that is as much as possible a replication of the real experiences’ protocols. The preliminary results of the use of this virtual lab prototype were conducted by chemical teachers (as expert judgment). At the same time, we have implemented in the prototype checking points to give feedback to the student that are spread from the solid quantity calculation for the solution through the techniques of prevention of contamination of the solution in the process. Next step in this research will be the evaluation of vLab in large scale, addressing the evaluation of virtual lab versus real lab context, user friendly and early adoption of technology (students and teachers). Besides this, the prototype will still be developed to evolve for a virtual lab with haptics. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Screening of viral pneumonia and COVID-19 in chest X-ray using classical machine learning

Governments, civil society, health professionals, and scientists have been facing a relentless fight against the pandemic of the COVID-19 disease;however, there are already about 150 million people infected worldwide and more than 3 million lives claimed, and numbers keep rising. One of the ways to combat this disease is the effective screening of infected patients. However, COVID-19 provides a similar pattern with diseases, such as pneumonia, and can misguide even very well-trained physicians. In this sense, a chest X-ray (CXR) is an effective alternative due to its low cost, accessibility, and quick response. Thus, inspired by research on the use of CXR for the diagnosis of COVID-19 pneumonia, we investigate classical machine learning methods to assist in this task. The main goal of this work is to present a robust, lightweight, and fast technique for the automatic detection of COVID-19 from CXR images. We extracted radiomic features from CXR images and trained classical machine learning models for two different classification schemes: i) COVID-19 pneumonia vs. Normal ii) COVID-19 vs. Normal vs. Viral pneumonia. Several evaluation metrics were used and comparison with many studies is presented. Our experimental results are equivalent to the state-of-the-art for both classification schemes. The solution’s high performance makes it a viable option as a computer-aided diagnostic tool, which can represent a significant gain in the speed and accuracy of the COVID-19 diagnosis. © 2021 IEEE.

Post-infectious myopathies related to COVID-19

Introduction: SARS-COV2 infection has come to focus in 2020, when COVID-19 was declared pandemic worldwide. In addition to interstitial pneumonia, its physiopathology involves multiple mechanisms, mainly inflammatory dysregulation and thrombo-inflammation, affecting most tissues and organs. Unexpected manifestations have been described, both in the acute phase and later, as a post-infectious disease. Case descriptions: Our study focuses on muscle biopsies obtained from 4 post-COVID-19 patients who had recovered from the infection, when muscle symptoms emerged, all with very high CPK levels. Two were females, 7th decade of life, presenting mild to moderate respiratory symptoms that resolved within a few days. About a month later they developed weakness and biopsies showed necrotic fibers in different proportions, one with features of rhabdomyolysis. Treatment with immunosuppressants and immunoglobulin resulted in significant improvement in motor and sensory conditions. The third patient, a 49-year-old hypertensive, obese and diabetic male, had severe respiratory symptoms requiring orotracheal intubation. Discharged without symptoms, he developed severe muscle weakness and tetraparesis one month after the COVID- 19 onset. Muscle biopsy showed degenerate and regenerating fibers and CD8 lymphocytic infiltration. Immunostaining for SARS-COV2 was negative. Treatment with methylprednisolone and immunoglobulin was followed by improvement of symptoms, of inflammatory markers, and decrease in muscle enzymes. The forth one, a 53-year-old hypertensive and diabetic male, developed weakness and skin lesions 3 - 4 weeks after COVID-19 diagnosis. Muscle biopsy showed perifascicular atrophy and myofiber vacuolization, suggestive of dermatomyositis. Conclusion: Our observations, in correlation with clinical history, biopsy findings and negative immunohistochemical results for COVID-19, strongly suggest the possibility of post-infectious myopathies.

Change in gap perception within current practices in assessing students learning mathematics

The COVID pandemic has touched many aspects of everyone’s life. Education is one of the fields greatly affected by it, as students and teachers were forced to move online and quickly adapt to the online environment. Assessment is a crucial part of education, especially in STEM fields. A gap analysis was performed by expert groups in the frame of an Erasmus+ project looking at the practices of six European countries. Specialists teaching university-grade mathematics in seven European institutions were asked about their perception of gaps in the assessment of students both before (2019) and during (2021) the pandemic. This qualitative study looks at the difference in perception of such gaps after almost one year of online teaching. The analysis of their responses showed that some gaps were present before the pandemic, as well as others that are specific to it. Some gaps, such as the lack of IT infrastructure and the need to adapt materials to an online environment, have been exacerbated by the outbreak. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Automatic Orientation Identification of Pediatric Chest X-Rays

Chest radiography (CXR) is one of the first choices in epidemiological analyses such as tuberculosis, cancer, pneumonia, and, recently, COVID-19. It provides crucial information for decision making, treatment, and monitoring the evolution of clinical cases from small to high complexity. Thus, it is a valuable source of information for the study, training, research, and development of computational support to medical diagnoses. In this work, we introduce a new method for chest X-ray adjustment to identifying and correcting radiographic images orientation. So, they can be automatically rotated to a standard position. Our proposal uses structural characteristics and statistics of pixel intensity patterns of CXR images. Divided into three steps, our method begins with the preparation of the photos, followed by a feature extraction strategy, and it ends with the X-ray image orientation identification. We use three different databases that include pediatric and adult radiographic imaging. A result showed 99.4% accuracy in the databases in our experiments. The code prepared by the authors is publicly available. © 2020 IEEE.

Off-label pharmaceutical drug prescription in Brazilian law: Medical liability and challenges in times of COVID-19

This text presents some suggestions regarding the indication of requirements that can serve to define the lawful use of medical practice in the off-label prescription of medications for the treatment of COVID-19, including with regard to patient consent. © 2020 Ibero-American Law Institute. All rights reserved.