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Real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-based assays are the gold standard for virus diagnosis. Point-of-care (POC) technologies have shown great progress during this period. Herein, we propose a novel fuchsine dye-loaded polymersome for a colorimetric paper-based dot blot spike protein diagnostic assay for COVID-19 via smartphone-assisted sensing. The prepared platform aimed to create an adaptable tool that competes with traditional nanoparticle-based assays employing gold and silver. Analytical characterization and application of the testing platform showed high sensitivity (10 times better than gold nanoparticles), stability, fast turnaround, and reproducibility. The potential and possibilities demonstrated by the current platform could be observed in its adaptability for different markers and pathologies. In addition, smartphone-assisted sensing emphasizes the ability to use the tool at home by common peoples which can lower the burden on the healthcare facilities and reach more underdeveloped regions. • Facile, quick, and sensitive colorimetric paper-based biosensor for COVID-19. • Platform is based on functionalized dye-loaded polymersome. • Polymersomes were more sensitive than silver-enhanced gold nanoparticles. • Higher stability, repeatability, and reproducibility. • The biosensor is adjustable for other diseases. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is causing considerable morbidity and mortality worldwide. Multiple reports have suggested that patients with heart failure (HF) are at a higher risk of severe disease and mortality with COVID-19. Moreover, evaluating and treating HF patients with comorbid COVID-19 represents a formidable clinical challenge as symptoms of both conditions may overlap and they may potentiate each other. Limited data exist regarding comprehensive management of HF patients with concomitant COVID-19. Since these issues pose serious new challenges for clinicians worldwide, HF specialists must develop a structured approach to the care of patients with COVID-19 and be included early in the care of these patients. Therefore, the Heart Failure Association of the European Society of Cardiology and the Chinese Heart Failure Association & National Heart Failure Committee conducted web-based meetings to discuss these unique clinical challenges and reach a consensus opinion to help providers worldwide deliver better patient care. The main objective of this position paper is to outline the management of HF patients with concomitant COVID-19 based on the available data and personal experiences of physicians from Asia, Europe and the United States. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

AIMS: This review is focused on the laboratory diagnoses of the coronavirus disease 2019 (COVID-19) by recognizing the antigen of the causative agent SARS-CoV-2 virus. Various antigen tests are available in this moment and these tests are being further developed in order to reach a better diagnostic value. The issue is reviewed in a complex view. METHODS: In this work, a complex survey of the current literature was made. The relevant and recent papers related to antigen tests of COVID-19 are discussed and cited. Basic specifications of the antigen tests and competitive methods were also scrutinized in the current literature. RESULTS: The survey of the current literature (years 2019 - 2021) was made and diagnostic methods like lateral flow tests (lateral flow immunochromatographic assay) and various types of biosensors were specified as tools for COVID-19 diagnosis and their application to be used as a point-of-care test is considered. CONCLUSIONS: Small hand-held assays applicable in the point-of-care conditions for diagnosis of COVID-19 by analysis of SARS-CoV-2 antigen are the means of a growing interest and these means undergo a significant development leading to the improvements of their specifications and applicability to the current praxis. Merit of the assays is discussed in this paper. [ABSTRACT FROM AUTHOR]

COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent. [ABSTRACT FROM AUTHOR]

The novel coronavirus illness (COVID-19) outbreak, which began in a seafood market in Wuhan, Hubei Province, China, in mid-December 2019, has spread to almost all countries, territories, and places throughout the world. And since the fault in diagnosis of a disease causes a psychological impact, this was very much visible in the spread of COVID-19. This research aims to address this issue by providing a better solution for diagnosis of the COVID-19 disease. The paper also addresses a very important issue of having less data for disease prediction models by elaborating on data handling techniques. Thus, special focus has been given on data processing and handling, with an aim to develop an improved machine learning model for diagnosis of COVID-19. Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support vector machine, and Deep Neural network (DNN) models are developed using the Hospital Israelita Albert Einstein (in São Paulo, Brazil) dataset to diagnose COVID-19. The dataset is pre-processed and distributed DT is applied to rank the features. Data augmentation has been applied to generate datasets for improving classification accuracy. The DNN model dominates overall techniques giving the highest accuracy of 96.99%, recall of 96.98%, and precision of 96.94%, which is better than or comparable to other research work. All the algorithms are implemented in a distributed environment on the Spark platform. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID-19) pandemic, has raised a serious situation across world human population and has become serious threat as contagious outbreak. This paper aims to overview the recently intelligent systems based on Artificial Intelligence using different medical imaging modalities like Computer Tomography (CT) and X-ray. This paper specifically discusses the machine learning techniques developed for COVID-19 diagnosis and provides insights on well-known data sets used to train these AI based networks. It also highlights the use of AI in COVID detection and classification at faster process where normal COVID testing takes couple of days to produce. Finally, we conclude by addressing the challenges associated with the use of Machine Leaming methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways machine learning techniques are used and how they potentially further work in com batting the outbreak of CO VID-19. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%. [ABSTRACT FROM AUTHOR]

Background: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.Methods: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.Results: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).Conclusions: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises. [ABSTRACT FROM AUTHOR]

Early detection and diagnosis of COVID-19, as well as the exact separation of non-COVID-19 cases in a non-invasive manner in the earliest stages of the disease, are critical concerns in the current COVID-19 pandemic. Convolutional Neural Network (CNN) based models offer a remarkable capacity for providing an accurate and efficient system for the detection and diagnosis of COVID-19. Due to the limited availability of RT-PCR (Reverse transcription-polymerase Chain Reaction) tests in developing countries, imaging-based techniques could offer an alternative and affordable solution to detect COVID-19 symptoms. This paper reviewed the current CNN-based approaches and investigated a custom-designed CNN method to detect COVID-19 symptoms from CT (Computed Tomography) chest scan images. This study demonstrated an integrated method to accelerate the process of classifying CT scan images. In order to improve the computational time, a hardware-based acceleration method was investigated and implemented on a reconfigurable platform (FPGA). Experimental results highlight the difference between various approximations of the design, providing a range of design options corresponding to both software and hardware. The FPGA-based implementation involved a reduced pre-processed feature vector for the classification task, which is a unique advantage of this particular application. To demonstrate the applicability of the proposed method, results from the CPU-based classification and the FPGA were measured separately and compared retrospectively. [ABSTRACT FROM AUTHOR]

Novel Coronavirus disease (COVID-19) has been widely described as the cause for a proinflammatory and hypercoagulable state. The inflammatory process involving the alveolar vascular endothelium in the respiratory system, is a determining factor for the onset of primary Pulmonary Artery Thrombosis (PAT) even in patients with heparin prophylactic treatment. Little is known about the efficacy of the anticoagulant therapy during the course of PAT caused by COVID-19. In this paper we describe the results obtained in patients with moderate COVID-19 disease, previously threated with prophylactic enoxaparin, who then received full Anticoagulant treatment after diagnosis of PAT. After three months Computed Tomography Pulmonary Angiography demonstrated a complete resolution of the vascular obstructive lesions in all patients, while all the coagulation tests were normal. [ABSTRACT FROM AUTHOR]

COVID‐19‐associated cutaneous manifestations are one of the most important and relatively common extra‐respiratory presentations of SARS‐COV‐2 infection. The exact identification and classification of these lesions can facilitate the accurate diagnosis and treatment. There are several case reports and small case series which describe cutaneous lesions in hands and feet. Currently, there is no scoping review about acral skin manifestations associated with COVID‐19. This paper covers the COVID‐related acral skin manifestations in 10 entities including acral papulo‐vesicular eruption, acral urticarial lesion, acral non‐inflammatory purpura and necrosis, acro‐ischemia associated COVID‐19, acral vasculitis, chilblain‐like lesion (COVID Toe), acral erythema multiform (EM) like lesion, hand and foot skin lesions associated with multisystem inflammatory syndrome in children (MISC), acral peeling conditions and red half‐moon nail sign. Future studies should focus on exact investigation of etiologies of these lesions including role of immune senescence, environment, gender, immunogenetics and relation of these lesion with major organ involvements. [ABSTRACT FROM AUTHOR]

Invasive pulmonary aspergillosis (IPA) has always been a challenging diagnosis and risk factors an important guide to investigate specific population, especially in Intensive Care Unit. Traditionally recognized risk factors for IPA have been haematological diseases or condition associated with severe immunosuppression, lately completed by chronic conditions (such as obstructive pulmonary disease, liver cirrhosis, chronic kidney disease and diabetes), influenza infection and Intensive Care Unit (ICU) admission. Recently, a new association with SARS-CoV2 infection, named COVID-19-associated pulmonary aspergillosis (CAPA), has been reported worldwide, even if its basic epidemiological characteristics have not been completely established yet. In this narrative review, we aimed to explore the potential risk factors for the development of CAPA and to evaluate whether previous host factors or therapeutic approaches used in the treatment of COVID-19 critically ill patients (such as mechanical ventilation, intensive care management, corticosteroids, broad-spectrum antibiotics, immunomodulatory agents) may impact this new diagnostic category. Reviewing all English-language articles published from December 2019 to December 2020, we identified 21 papers describing risk factors, concerning host comorbidities, ICU management, and COVID-19 therapies. Although limited by the quality of the available literature, data seem to confirm the role of previous host risk factors, especially respiratory diseases. However, the attention is shifting from patients' related risk factors to factors characterizing the hospital and intensive care course, deeply influenced by specific features of COVID treatment itself. Prolonged invasive or non-invasive respiratory support, as well as the impact of corticosteroids and/or immunobiological therapies seem to play a pivotal role. ICU setting related factors, such as environmental factors, isolation conditions, ventilation systems, building renovation works, and temporal spread with respect to pandemic waves, need to be considered. Large, prospective studies based on new risk factors specific for CAPA are warranted to guide surveillance and decision of when and how to treat this particular population. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak poses a major global threat to the public health worldwide. The infectious disease caused by the virus that affected the entire world was named as the Coronavirus disease-2019 (COVID-19). The knowledge regarding the wide clinico-biological aspects of the COVID-19 continues to evolve very rapidly, given the growing data from all over the world. During this complicated process, healthcare professionals have benefited from each other's experiences in combatting against the COVID-19 syndrome. COVID-19 related studies have been performed by a wide variety of research groups in Turkey as well as the rest of the world. The aim of this paper is to outline Turkish COVID-19 research indexed in the LitCovid system. LitCovid is a curated literature hub for tracking up-to-date scientific data about the SARS-CoV-2. COVID-19's first case was detected in Turkey, on March 11th, 2020. Six months after the first case was observed, the total number of COVID-19 patients was reported to be as 286,455, and the total number of deaths due to SARS-CoV-2 was 6895. The genetic sequence of the novel coronavirus showed significant identity to SARS-CoV and MERS-CoV. Numerous drugs including lopinavir/ritonavir, favipiravir, neuraminidase inhibitors, remdesivir, umifenovir, azithromycin, and chloroquine have been suggested for the management of COVID-19 although the exact treatment is yet to be determined. [ABSTRACT FROM AUTHOR]

AIMS: Aims: The Coronavirus Disease 2019 (COVID-19) caused not only global pandemic, but it also pointed at unprepared health care systems and countermeasures were introduced under the pressure of urgent circumstances. This review is focused on discussion and critical evaluation of instrumental tools for COVID-19 diagnosis that were developed in the last months. METHODS: Survey of actual literature and scientific reports was made. The most substantial analytical and diagnostical methods were identified and described. Principles and limitations of the methods are described, and actual papers are cited in this review. RESULTS: Analytical and diagnostical methods like Polymerase Chain Reaction (PCR), Loop-mediated isothermal Amplification (LAMP), Lateral Flow Immunochromatography Assay (LFIA), Enzyme-Linked Immunosorbent Assay (ELISA), biosensors and ChemiLuminescence ImmunoAssay (CLIA) are discussed for assay of viral particles, antigens and specific host antibodies in blood, serum, plasma, nasopharyngeal swab and other samples in order to diagnose COVID-19. CONCLUSIONS: The Coronavirus Disease 2019 (COVID-19) is an emerging disease that has spread over the world since the end of year 2019. The global epidemic pointed at the necessity to introduce sensitive methods for instrumental diagnosis of COVID-19 and distinguishing it from the other viral diseases. (Tab. 2, Ref. 96). [ABSTRACT FROM AUTHOR]

AIMS: Aims: The Coronavirus Disease 2019 (COVID-19) caused not only global pandemic, but it also pointed at unprepared health care systems and countermeasures were introduced under the pressure of urgent circumstances. This review is focused on discussion and critical evaluation of instrumental tools for COVID-19 diagnosis that were developed in the last months. METHODS: Survey of actual literature and scientific reports was made. The most substantial analytical and diagnostical methods were identified and described. Principles and limitations of the methods are described, and actual papers are cited in this review. RESULTS: Analytical and diagnostical methods like Polymerase Chain Reaction (PCR), Loop-mediated isothermal Amplification (LAMP), Lateral Flow Immunochromatography Assay (LFIA), Enzyme-Linked Immunosorbent Assay (ELISA), biosensors and ChemiLuminescence ImmunoAssay (CLIA) are discussed for assay of viral particles, antigens and specific host antibodies in blood, serum, plasma, nasopharyngeal swab and other samples in order to diagnose COVID-19. CONCLUSIONS: The Coronavirus Disease 2019 (COVID-19) is an emerging disease that has spread over the world since the end of year 2019. The global epidemic pointed at the necessity to introduce sensitive methods for instrumental diagnosis of COVID-19 and distinguishing it from the other viral diseases. (Tab. 2, Ref. 96). [ABSTRACT FROM AUTHOR]

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease. Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool. Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation. Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed. [ABSTRACT FROM AUTHOR]

Background: Highly sensitive real-time reverse transcription polymerase chain reaction (RT-qPCR) methods have been developed for the detection of SARS-CoV-2. However, they are costly. Loop-mediated isothermal amplification (LAMP) assay has emerged as a novel alternative isothermal amplification method for the detection of nucleic acid. Methods: A rapid, sensitive and specific real-time reverse transcription LAMP (RT-LAMP) assay was developed for SARS-CoV-2 detection. Results: This assay detected one copy/reaction of SARS-CoV-2 RNA in 30 min. Both the clinical sensitivity and specificity of this assay were 100%. The RT-LAMP showed comparable performance with RT-qPCR. Combining simplicity and cost-effectiveness, this assay is therefore recommended for use in resource resource-limited settings. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 (COVID-19) has today multiplied globally and various governments are attempting to stop the outbreak of the disease escalation into a worldwide health crisis. At this juncture, readiness, candor, clarity, and partaking of data are of paramount importance to speed up factual evaluation and starting pattern control activities, including serendipitous findings. Owing to the involvement of COVID-19, many facts regarding virulence, pathogenesis, and the real viral infection source and/or transmission mode still need to be addressed. The infected patients often present clinical symptoms with fever, dyspnea, fatigue, diarrhea, vomiting, and dry cough, as well as pulmonary, infiltrates on imaging. Extensive measures to decrease person-to-person transmission of COVID-19 are being implemented to prevent, recognize, and control the current outbreak as it is very similar to SARS-CoV in its clinical spectrum, epidemiology, and pathogenicity. In response to this fatal disease and disruptive outbreak, it is extremely vital to expedite the drug development process to treat the disease and vaccines for the prevention of COVID-19 that would help us defeat this pandemic expeditiously. This paper sums up and unifies the study of virological aspects, disease transmission, clinically administered techniques, therapeutics options, managements, future directions, designing of vaccines, and news dissemination pertaining to COVID-19. [ABSTRACT FROM AUTHOR]

The global spread of COVID-19 has caused a substantial societal burden and become a major global public health issue. The COVID-19 elderly population with hypertension, diabetes, cardiovascular, and cerebrovascular diseases are at risk. Mortality rates are highest in these individuals if infected with COVID-19. Although the lungs are the main organs involved in acute respiratory distress syndrome caused by COVID-19 infection, COVID-19 triggers inflammatory and immune mechanisms, inducing a "cytokine storm" that aggravates disease progression and may lead to death. Presently, effective drugs are lacking, although current studies have confirmed that drugs with therapeutic potential include redaciclovir, lopinavir/ritonavir combined with interferon-β, convalescent plasma, and monoclonal antibodies. Currently, the most reasonable and effective way to prevent COVID-19 is to control the source of infection, terminate routes of transmission, and protect susceptible populations. With the rise of COVID-19 in China and worldwide, further prevention, diagnosis, and treatment measures are a critical unmet need. Cerebrovascular disease has high incidence, disability rate, and fatality rate. COVID-19 patient outcomes may also be complicated with acute stroke. This paper summarizes the influence of COVID-19 on cerebrovascular disease and discusses possible pathophysiological mechanisms to provide new angles for the prevention and diagnosis of this disease. [ABSTRACT FROM AUTHOR]

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Background: Corona Virus Disease 2019(COVID-19)is a global pandemic novel coronavirus infection disease caused by Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Although rapid, large-scale testing plays an important role in patient management and slowing the spread of the disease. However, there has been no good and widely used drug treatment for infection and transmission of SARS-CoV-2. Key findings: Therefore, this review updates the body of knowledge on viral structure, infection routes, detection methods, and clinical treatment, with the aim of responding to the large-section caused by SARS-CoV-2. This paper focuses on the structure of SARS-CoV-2 viral protease, RNA polymerase, serine protease and main proteinase-like protease as well as targeted antiviral drugs. Conclusion: In vitro or clinical trials have been carried out to provide deeper thinking for the pathogenesis, clinical diagnosis, vaccine development and treatment of SARS-CoV-2.

A diagnosis of paroxysmal nocturnal hemoglobinuria (PNH) was elicited during acute COVID-19 infection. COVID-19 spike proteins trigger the alternative pathway of complement. Acute SARS-CoV-2 infection possibly expanded an existing PIG-A mutation. [ABSTRACT FROM AUTHOR]

Background: Real-Time Reverse-Transcription Polymerase Chain Reaction (RT-PCR) is currently the only recommended diagnostic method for SARS-CoV-2. However, rapid immunoassays for SARS-CoV-2 antigen could significantly reduce the COVID-19 burden currently weighing on laboratories around the world. Methods: We evaluated the performance of two rapid fluorescence immunoassays (FIAs), SOFIA SARS Antigen FIA (Quidel Corporation, San Diego, CA, USA) and STANDARD F COVID-19 Ag FIA (SD Biosensor Inc., Gyeonggi-do, Republic of Korea), which use an automated reader. The study used 64 RT-PCR characterized clinical samples (32 positive; 32 negative), which consisted of nasopharyngeal swabs in universal transport medium. Results: Of the 32 positive specimens, all from patients within 5 days of symptom onset, the Quidel and SD Biosensor assays detected 30 (93.8%) and 29 (90.6%) samples, respectively. Among the 27 samples with high viral loads (Ct ≤ 25), the two tests had a sensitivity of 100%. Specificity was 96.9% for both kits. Conclusion: The high performance of the evaluated FIAs indicates a potential use as rapid and PCR-independent tools for COVID-19 diagnosis in early stages of infection. The excellent sensitivity to detect cases with viral loads above ~106 copies/ mL (Ct values ≤ 25), the estimated threshold of contagiousness, suggests that the assays might serve to rapidly identify infective individuals. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

Maskne, a newly coined term deriving from the contraction of mask-related acne, is a form of mechanical acne resulting from continuous textile–skin adherence and friction. Prolonged mask use heats up the face environment, thus modifying skin microbiota and sebum production. Although effective prevalence is unknown, since the pandemic began and the prolonged use of masks was extended to the general population, maskne has been a frequent topic of consultation among dermatologists. This term has been successfully introduced into common language, with more than 200,000 hashtags on social media, where it is also possible to find "home remedies" that can worsen this dermatosis. The aim of this paper is to discuss the pathogenesis, address clinicians on the correct differential diagnoses among facial dermatoses, and move towards the correct therapy. [ABSTRACT FROM AUTHOR]

The impact of the COVID-19 pandemic caused by the SARS-CoV-2 virus underscored the crucial role of laboratorial tests as a strategy to control the disease, mainly to indicate the presence of specific antibodies in human samples from infected patients. Therefore, suitable recombinant antigens are relevant for the development of reliable tests, and so far, single recombinant proteins have been used. In this context, B-cell epitopes-based chimeric proteins can be an alternative to obtain tests with high accuracy through easier and cheaper production. The present study used bioinformatics tools to select specific B-cell epitopes from the spike (S) and the nucleocapsid (N) proteins from the SARS-CoV-2 virus, aiming to produce a novel recombinant chimeric antigen (N4S11-SC2). Eleven S and four N-derived B-cell epitopes were predicted and used to construct the N4S11-SC2 protein, which was analyzed in a recombinant format against serum and urine samples, by means of an in house-ELISA. Specific antibodies were detected in the serum and urine samples of COVID-19 patients, which were previously confirmed by qRT-PCR. Results showed that N4S11-SC2 presented 83.7% sensitivity and 100% specificity when using sera samples, and 91.1% sensitivity and 100% specificity using urine samples. Comparable findings were achieved with paired urine samples when compared to N and S recombinant proteins expressed in prokaryotic systems. However, better results were reached for N4S11-SC2 in comparison to the S recombinant protein when using paired serum samples. Anti-N4S11-SC2 antibodies were not clearly identified in Janssen Ad26.COV2.S COVID-19-vaccinated subjects, using serum or paired urine samples. In conclusion, this study presents a new chimeric recombinant antigen expressed in a prokaryotic system that could be considered as an alternative diagnostic marker for the SARS-CoV-2 infection, with the potential benefits to be used on serum or urine from infected patients. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, which originated in Hubei, China, in December 2019, has had a profound impact on global public health. With the elucidation of the SARS-CoV-2 virus structure, genome type, and routes of infection, a variety of diagnostic methods have been developed for COVID-19 detection and surveillance. Although the pandemic has been declared over, we are still significantly affected by it in our daily lives in the post-pandemic era. Among the various diagnostic methods, nanomaterials, especially metallic nanomaterials, have shown great potential in the field of bioanalysis due to their unique physical and chemical properties. This review highlights the important role of metallic nanosensors in achieving accurate and efficient detection of COVID-19 during the pandemic outbreak and spread. The sensing mechanisms of each diagnostic device capable of analyzing a range of targets, including viral nucleic acids and various proteins, are described. Since SARS-CoV-2 is constantly mutating, strategies for dealing with new variants are also suggested. In addition, we discuss the analytical tools needed to detect SARS-CoV-2 variants in the current post-pandemic era, with a focus on achieving rapid and accurate detection. Finally, we address the challenges and future directions of metallic nanomaterial-based COVID-19 detection, which may inspire researchers to develop advanced biosensors for COVID-19 monitoring and rapid response to other virus-induced pandemics based on our current achievements. [ABSTRACT FROM AUTHOR]

Background: SARS-CoV-2 which causes COVID-19 can infect people with moderate to severe illnesses. In the months following the pandemic's initial detection in China in December 2019, it has quickly spread over the globe. The virus has continued to be prevalent in numerous countries with different mutated strains associated with variable degrees of clinical symptoms despite significant attempts to limit the disease. A coordinated strategy incorporating precise epidemiology, surveillance, prophylaxis, and surveillance is necessary to limit this pandemic. Therefore, accurate diagnosis utilizing quick technology is essential. The precise and timely diagnosis of SARS-CoV-2 is crucial for the efficient care and prevention of COVID-19 patients as well as to stop the transmission of the disease in light of the rising incidence of COVID-19 cases. The various diagnostic techniques for the diagnosis of COVID-19 infection in both clinical and research settings are described in the current review. This article specifically describes the technical and instrumental aspects of the diagnostic techniques employed. The diagnostic methods are all covered in updated and comprehensive detail. [ABSTRACT FROM AUTHOR]

The COVID-19 infection caused by the new SARS-CoV-2 virus has been linked to a broad spectrum of symptoms, from a mild cough to life-threatening pneumonia. As we learn more about this unusual COVID-19 epidemic, new issues are emerging and being reported daily. Mucormycosis, also known as zygomycosis or phycomycosis, causes severe fungal illness to individuals with a weakened immune system. It is a devastating fungal infection, and the most frequent kind is the rhino cerebral type. As a devastating second wave of COVID-19 sweeps India, doctors report several instances involving a strange illness—sometimes known as the "black fungus"—among returning and recovered COVID-19 patients. This paper analyzes the existing statistical data to address the severity of prevalence and further notes the nano-based diagnostic parameters, clinical presentations, its connection with other conditions like diabetes, hypertension, and GI disorders, and the importance of anti-fungal therapy in treating the same. Anti-fungal therapies, as well as surgical interventions, are currently used for the treatment of the disease. Proper and timely diagnosis is necessary, along with the reduction in the spread of COVID-19. From the review, it was found that timely pharmacologic interventions and early diagnosis by using a nano-based diagnostic kit can help control the disease. Additionally, this paper provides novel information about the nanotechnology approaches such as fungal detection biosensors, nucleic acids-based testing, point-of-care tests, and galactomannans detection, in the diagnosis of mucormycosis, and thereby reinforces the need for further research on the topic. [ABSTRACT FROM AUTHOR]