COVID-19 Diagnostics, Tools, and Prevention

Aptamer-based assembly systems for SARS-CoV-2 detection and therapeutics

Nucleic acid aptamers are oligonucleotide chains with molecular recognition properties. Compared with antibodies, aptamers show advantages given that they are readily produced via chemical synthesis and elicit minimal immunogenicity in biomedicine applications. Notably, aptamer-encoded nucleic acid assemblies further improve the binding affinity of aptamers with the targets due to their multivalent synergistic interactions. Specially, aptamers can be engineered with special topological arrangements in nucleic acid assemblies, which demonstrate spatial and valence matching towards antigens on viruses, thus showing potential in the detection and therapeutic applications of viruses. This review presents the recent progress on the aptamers explored for SARS-CoV-2 detection and infection treatment, wherein applications of aptamer-based assembly systems are introduced in detail. Screening methods and chemical modification strategies for aptamers are comprehensively summarized, and the types of aptamers employed against different target domains of SARS-CoV-2 are illustrated. The evolution of aptamer-based assembly systems for the detection and neutralization of SARS-CoV-2, as well as the construction principle and characteristics of aptamer-based DNA assemblies are demonstrated. The typically representative works are presented to demonstrate how to assemble aptamers rationally and elaborately for specific applications in SARS-CoV-2 diagnosis and neutralization. Finally, we provide deep insights into the current challenges and future perspectives towards aptamer-based nucleic acid assemblies for virus detection and neutralization in nanomedicine.

Potential Use of the Cholesterol Transfer Inhibitor U18666A as a Potent Research Tool for the Study of Cholesterol Mechanisms in Neurodegenerative Disorders

Cholesterol is an essential component of mammalian cell membranes and a precursor for crucial signaling molecules. The brain contains the highest level of cholesterol in the body, and abnormal cholesterol metabolism links to many neurodegenerative disorders. The results indicate that faulty cholesterol metabolism is a common feature among people living with neurodegenerative conditions. The researchers suggest that restoring cholesterol levels may become a beneficial new strategy in treating certain neurodegenerative conditions. Several neurodegenerative disorders, such as Alzheimer's disease (AD), Niemann-Pick type C (NPC) disease, and Parkinson's disease (PD), have been connected to abnormalities in brain cholesterol metabolism. Consequently, using a lipid research tool is vital to study further and understand the effect of lipids in neurodegenerative disorders such as NPC, AD, PD, and Huntington's disease (HD). U18666A, also known as 3-(2-(diethylamino) ethoxy) androst-5-en-17-one, is a pharmaceutical drug that suppresses cholesterol trafficking and is a well-known class-2 amphiphile. U18666A has performed many functions, allowing for essential discoveries in lipid studies and shedding light on the pathophysiology of neurodegenerative disorders. Additionally, U18666A prevented the downregulation of low-density lipoprotein (LDL) receptors that are induced by LDL and led to the buildup of cholesterol in lysosomes. Numerous studies show that U18666A impacts the function of cholesterol trafficking to control the metabolism and transport of amyloid precursor proteins (APPs). Treating cortical neurons with U18666A may provide a new in vitro model system for studying the underlying molecular process of NPC, AD, HD, and PD. In this article, we review the mechanism and function of U18666A as a vital tool for studying cholesterol mechanisms in neurological diseases related to abnormal cholesterol metabolism, such as AD, NPC, HD, and PD.

Frontline Health Care Workers' Mental Health and Well-Being During the First Year of the COVID-19 Pandemic: Analysis of Interviews and Social Media Data

BACKGROUND

The COVID-19 pandemic has shed light on fractures in health care systems worldwide and continues to have a significant impact, particularly in relation to the health care workforce. Frontline staff have been exposed to unprecedented strain, and delivering care during the pandemic has affected their safety, mental health, and well-being.

OBJECTIVE

This study aimed to explore the experiences of health care workers (HCWs) delivering care in the United Kingdom during the COVID-19 pandemic to understand their well-being needs, experiences, and strategies used to maintain well-being (at individual and organizational levels).

METHODS

We analyzed 94 telephone interviews with HCWs and 2000 tweets about HCWs' mental health during the first year of the COVID-19 pandemic.

RESULTS

The results were grouped under 6 themes: redeployment, clinical work, and sense of duty; well-being support and HCW's coping strategies; negative mental health effects; organizational support; social network and support; and public and government support.

CONCLUSIONS

These findings demonstrate the need for open conversations, where staff's well-being needs and the strategies they adopted can be shared and encouraged, rather than implementing top-down psychological interventions alone. At the macro level, the findings also highlighted the impact on HCW's well-being of public and government support as well as the need to ensure protection through personal protective equipment, testing, and vaccines for frontline workers.

Lessons learnt from the first wave of COVID-19 in Damascus, Syria: a multicentre retrospective cohort study

OBJECTIVES

The decade-long Syrian war led to fragile health infrastructures lacking in personal and physical resources. The public health of the Syrian population was, therefore, vulnerable to the COVID-19 pandemic, which devastated even well-resourced healthcare systems. Nevertheless, the officially reported incidence and fatality rates were significantly lower than the forecasted numbers.

DESIGN

A retrospective cohort study.

SETTING

The four main responding hospitals in Damascus, which received most of the cases during the first pandemic wave in Syria (i.e., June-August 2020).

PARTICIPANTS

One thousand one hundred eighty-four patients who were managed as inpatient COVID-19 cases.

PRIMARY AND SECONDARY OUTCOME MEASURES

The records of hospitalised patients were screened for clinical history, vital signs, diagnosis modality, major interventions and status at discharge.

RESULTS

The diagnostic and therapeutic preparedness for COVID-19 was significantly heterogeneous among the different centres and depleted rapidly after the arrival of the first wave. Only 32% of the patients were diagnosed based on positive reverse transcription-PCR tests. Five hundred twenty-six patients had an indication for intensive care unit admission, but only 82% of them received it. Two hundred fifty-seven patients needed mechanical ventilation, but ventilators were not available to 14% of them, all of whom died. Overall mortality during hospitalisation reached 46% and no significant difference was found in fatality between those who received and did not receive these care options.

CONCLUSIONS

The Syrian healthcare system expressed minor resilience in facing the COVID-19 pandemic, as its assets vanished swiftly with a limited number of cases. This forced physicians to reserve resources (e.g., ventilators) for the most severe cases, which led to poor outcomes of in-hospital management and limited the admission capacity for milder cases. The overwhelmed system additionally suffered from constrained coordination, suboptimal allocation of the accessible resources and a severe inability to informatively report on the catastrophic pandemic course in Syria.

Visual analysis of social events and stock market volatility in China and the USA during the pandemic

The COVID-19 pandemic is one of the most severe infectious diseases in recent decades, and has had a significant impact on the global economy, and the stock market. Most existing studies on stock market volatility during the pandemic have been conducted from a data science perspective, with statistical analysis and mathematical models often revealing the superficial relationship between Covid and the stock market at the data level. In contrast, few studies have explored the relationship between more specialised aspects of the pandemic. Specifically, the relationship found between major social events and the stock market. In this work, a multi-source, data-based relationship analysis method is proposed, that collects historical data on significant social events and related stock data in China and the USA, to further explore the potential correlation between stock market index fluctuations and the impact of social events by analysing cross-timeline data. The results suggest and offer more evidence that social events do indeed impact equity markets, and that the indices in both China and the USA were also affected more by the epidemic in 2020 than in 2021, and these indices became less affected by the epidemic as it became the world adapted. Moreover, these relationships may also be influenced by a variety of other factors not covered in this study. This research, so far, is in its initial stage, and the methodology is not rigorous and cannot be applied as an individual tool for decision; however, it could potentially serve as a supplementary tool and provide a multi-dimensional basis for stock investors and policymakers to make decisions.

Therapeutic and diagnostic applications of nanoparticles in the management of COVID-19: a comprehensive overview

In December 2019, Coronavirus Disease 2019 (COVID-19) was reported in Wuhan, China. Comprehensive strategies for quick identification, prevention, control, and remedy of COVID-19 have been implemented until today. Advances in various nanoparticle-based technologies, including organic and inorganic nanoparticles, have created new perspectives in this field. These materials were extensively used to control COVID-19 because of their specific attribution to preparing antiviral face masks, various safety sensors, etc. In this review, the most current nanoparticle-based technologies, applications, and achievements against the coronavirus were summarized and highlighted. This paper also offers nanoparticle preventive, diagnostic, and treatment options to combat this pandemic.

Developing a conceptual framework for the health protection of United Nations peacekeepers against the COVID-19 pandemic from global health perspectives

The coronavirus disease 2019 (COVID-19) pandemic has posed particular health risks to United Nations peacekeepers, which require prompt responses and global attention. Since the health protection of United Nations peacekeepers against the COVID-19 pandemic is a typical global health problem, strategies from global health perspectives may help address it. From global health perspectives, and referring to the successful health protection of the Chinese Anti-Ebola medical team in Liberia, a conceptual framework was developed for the health protection of United Nations peacekeepers against the COVID-19 pandemic. Within this framework, the features include multiple cross-borders (cross-border risk factors, impact, and actions); multiple risk factors (Social Determinants of Health), multiple disciplines (public health, medicine, politics, diplomacy, and others), and extensive interdepartmental cooperation. These strategies include multiple phases (before-deployment, during-deployment, and post-deployment), multi-level cooperation networks (the United Nations, host countries, troop-contributing countries, the United Nations peacekeeping team, and United Nations peacekeepers), and concerted efforts from various dimensions (medical, psychological, and social).

Translating diagnostics and drug delivery technologies to low-resource settings

Diagnostics and drug delivery technologies engineered for low-resource settings aim to meet their technical design specifications using strategies that are compatible with limited equipment, infrastructure, and operator training. Despite many preclinical successes, very few of these devices have been translated to the clinic. Here, we identify factors that contribute to the clinical success of diagnostics and drug delivery systems for low-resource settings, including the need to engage key stakeholders at an early stage, and provide recommendations for the clinical translation of future medical technologies.

Review of the impact of COVID-19 on male reproduction, and its implications on assisted reproductive technology services

The announcement in 2019 of a new coronavirus disease that quickly became a major pandemic, is an exceptional challenge to healthcare systems never seen before. Such a public health emergency can largely influence various aspects of people’s health as well as reproductive outcome. IVF specialists should be vigilant, monitoring the situation whilst contributing by sharing novel evidence to counsel patients, both pregnant women and would-be mothers. Coronavirus infection might adversely affect pregnant women and their offspring. Consequently, this review paper aims to analyse its potential risks for reproductive health, as well as potential effects of the virus on gamete function and embryo development. In addition, reopening fertility clinics poses several concerns that need immediate addressing, such as the effect of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) on reproductive cells and also the potential risk of cross-contamination and viral transmission. Therefore, this manuscript summarizes what is currently known about the effect of the SARS-CoV-2 infection on medically assisted reproductive treatments and its effect on reproductive health and pregnancy.

Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities

Microfluidic diagnostic (μDX) technologies miniaturize sensors and actuators to the length-scales that are relevant to biology: the micrometer scale to interact with cells and the nanometer scale to interrogate biology's molecular machinery. This miniaturization allows measurements of biomarkers of disease (cells, nanoscale vesicles, molecules) in clinical samples that are not detectable using conventional technologies. There has been steady progress in the field over the last three decades, and a recent burst of activity catalyzed by the COVID-19 pandemic. In this time, an impressive and ever-growing set of technologies have been successfully validated in their ability to measure biomarkers in clinical samples, such as blood and urine, with sensitivity and specificity not possible using conventional tests. Despite our field's many accomplishments to date, very few of these technologies have been successfully commercialized and brought to clinical use where they can fulfill their promise to improve medical care. In this paper, we identify three major technological trends in our field that we believe will allow the next generation of μDx to have a major impact on the practice of medicine, and which present major opportunities for those entering the field from outside disciplines: 1. the combination of next generation, highly multiplexed μDx technologies with machine learning to allow complex patterns of multiple biomarkers to be decoded to inform clinical decision points, for which conventional biomarkers do not necessarily exist. 2. The use of micro/nano devices to overcome the limits of binding affinity in complex backgrounds in both the detection of sparse soluble proteins and nucleic acids in blood and rare circulating extracellular vesicles. 3. A suite of recent technologies that obviate the manual pre-processing and post-processing of samples before they are measured on a μDX chip. Additionally, we discuss economic and regulatory challenges that have stymied μDx translation to the clinic, and highlight strategies for successfully navigating this challenging space.

Evaluation of a fully closed real time PCR platform for the detection of SARS-CoV-2 in nasopharyngeal swabs: a pilot study

AIMS

To date, reverse transcriptase PCR (RT-PCR) on nasopharyngeal swabs is the 'gold standard' approach for the diagnosis of COVID-19. The need to develop easy to use, rapid, robust and with minimal hands-on time approaches are warranted. In this setting, the Idylla SARS-CoV-2 Test may be a valuable option. The aim of our study is to evaluate the analytical and clinical performance of this assay on previously tested SARS-CoV-2 people by conventional RT-PCR based approach in different settings, including initial diagnosis and clinical follow-up.

METHODS

To evaluate the sensitivity and specificity of the Idylla SARS-CoV-2 Test, we retrieved 55 nasopharyngeal swabs, previously analysed by a fully validated assay, from symptomatic patients or from people who have been in close contact with COVID-19 positive cases. Discordant or high discrepant cases were further analysed by a third technique. In addition, a second subset of 14 nasopharyngeal swab samples with uncertain results (cycle threshold between 37 and 40), by using the fully validated assay, from patients with viral infection beyond day 21, were retrieved.

RESULTS

Overall, Idylla showed a sensitivity of 93.9% and a specificity of 100.0%. In addition, in the additional 14 nasopharyngeal swab samples, only five (35.7%) featured a positive result by the Idylla SARS-CoV-2 Test.

CONCLUSIONS

We demonstrated that the Idylla SARS-CoV-2 Test may represent a valid, fast, highly sensitive and specific RT-PCR test for the identification of SARS-CoV-2 infection.

COVID-19 Diagnosis on Chest Radiographs with Enhanced Deep Neural Networks

The COVID-19 pandemic has caused a devastating impact on the social activity, economy and politics worldwide. Techniques to diagnose COVID-19 cases by examining anomalies in chest X-ray images are urgently needed. Inspired by the success of deep learning in various tasks, this paper evaluates the performance of four deep neural networks in detecting COVID-19 patients from their chest radiographs. The deep neural networks studied include VGG16, MobileNet, ResNet50 and DenseNet201. Preliminary experiments show that all deep neural networks perform promisingly, while DenseNet201 outshines other models. Nevertheless, the sensitivity rates of the models are below expectations, which can be attributed to several factors: limited publicly available COVID-19 images, imbalanced sample size for the COVID-19 class and non-COVID-19 class, overfitting or underfitting of the deep neural networks and that the feature extraction of pre-trained models does not adapt well to the COVID-19 detection task. To address these factors, several enhancements are proposed, including data augmentation, adjusted class weights, early stopping and fine-tuning, to improve the performance. Empirical results on DenseNet201 with these enhancements demonstrate outstanding performance with an accuracy of 0.999%, precision of 0.9899%, sensitivity of 0.98%, specificity of 0.9997% and F1-score of 0.9849% on the COVID-Xray-5k dataset.

Dynamic Classification of Imageless Bioelectrical Impedance Tomography Features with Attention-Driven Spatial Transformer Neural Network

Point-of-Care monitoring devices have proven to be pivotal in the timely screening and intervention of critical care patients. The urgent demands for their deployment in the COVID-19 pandemic era has translated into the escalation of rapid, reliable, and low-cost monitoring systems research and development. Electrical Impedance Tomography (EIT) is a highly promising modality in providing deep tissue imaging that aids in patient bedside diagnosis and treatment. Motivated to bring forth an accurate and intelligent EIT screening system, we bypassed the complexity and challenges typically associated with its image reconstruction and feature identification processes by solely focusing on the raw data output to extract the embedded knowledge. We developed a novel machine learning architecture based on an attention-driven spatial transformer neural network to specifically accommodate for the patterns and dependencies within EIT raw data. Through elaborate precision-mapped phantom experiments, we validated the reproduction and recognition of features with systemically controlled changes. We demonstrated over 95% accuracy via state-of-the-art machine learning models, and an enhanced performance using our adapted transformer pipeline with shorter training time and greater computational efficiency. Our approach of using imageless EIT driven by a novel attention-focused feature learning algorithm is highly promising in revolutionizing conventional EIT operations and augmenting its practical usage in medicine and beyond.

Exhaled VOCs can discriminate subjects with COVID-19 from healthy controls

COVID-19 detection currently relies on testing by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing. However, SARS-CoV-2 is expected to cause significant metabolic changes in infected subjects due to both metabolic requirements for rapid viral replication and host immune responses. Analysis of volatile organic compounds (VOCs) from human breath can detect these metabolic changes and is therefore an alternative to RT-PCR or antigen assays. To identify VOC biomarkers of COVID-19, exhaled breath samples were collected from two sample groups into Tedlar bags: negative COVID-19 (n= 12) and positive COVID-19 symptomatic (n= 14). Next, VOCs were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Subjects with COVID-19 displayed a larger number of VOCs as well as overall higher total concentration of VOCs (p< 0.05). Univariate analyses of qualified endogenous VOCs showed approximately 18% of the VOCs were significantly differentially expressed between the two classes (p< 0.05), with most VOCs upregulated. Machine learning multivariate classification algorithms distinguished COVID-19 subjects with over 95% accuracy. The COVID-19 positive subjects could be differentiated into two distinct subgroups by machine learning classification, but these did not correspond with significant differences in number of symptoms. Next, samples were collected from subjects who had previously donated breath bags while experiencing COVID-19, and subsequently recovered (COVID Recovered subjects (n= 11)). Univariate and multivariate results showed >90% accuracy at identifying these new samples as Control (COVID-19 negative), thereby validating the classification model and demonstrating VOCs dysregulated by COVID are restored to baseline levels upon recovery.

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19

With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.

Speech as a Biomarker for COVID-19 Detection Using Machine Learning

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

Potential therapeutic effect of oxygen-ozone in controlling of COVID-19 disease

Atmospheric ozone is produced when nitrogen oxides react with volatile organic compounds. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome contains a unique N-terminal fragment in the Spike protein, which allows it to bind to air pollutants in the environment. 'Our approach in this review is to study ozone and its effect on the SARS-CoV-2 virus and patients with coronavirus disease 2019 (COVID-19). Article data were collected from PubMed, Scopus, and Google Scholar databases. Ozone therapy has antiviral properties, improves blood flow, facilitates the transfer of oxygen in hypoxemic tissues, and reduces blood coagulation phenomena in COVID-19 patients. Ozone has immunomodulatory effects by modulating cytokines (reduction of interleukin-1, interleukin-6, tumor necrosis factor-α, and interleukin-10), induction of interferon-γ, anti-inflammatory properties by modulating NOD-, LRR- and pyrin domain-containing protein 3, inhibition of cytokine storm (blocking nuclear factor-κB and stimulating nuclear factor erythroid 2-related factor 2 pathway), stimulates cellular/humoral immunity/phagocytic function and blocks angiotensin-converting enzyme 2. In direct oxygen-ozone injection, oxygen reacts with several biological molecules such as thiol groups in albumin to form ozonoids. Intravenous injection of ozonated saline significantly increases the length of time a person can remain hypoxic. The rectal ozone protocol is rectal ozone insufflation, resulting in clinical improvement in oxygen saturation and biochemical improvement (fibrinogen, D-dimer, urea, ferritin, LDH, interleukin-6, and C-reactive protein). In general, many studies have shown the positive effect of ozone therapy as a complementary therapy in the recovery of COVID-19 patients. All the findings indicate that systemic ozone therapy is nontoxic and has no side effects in these patients.

Meta-analysis on the effect of combining Lianhua Qingwen with Western medicine to treat coronavirus disease 2019

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become a worldwide life-threatening pandemic. Lianhua Qingwen is believed to possess the ability to treat or significantly improve the symptoms of COVID-19. These claims make it important to systematically evaluate the effects of using Lianhua Qingwen with Western medicine to treat COVID-19.

OBJECTIVE

To evaluate the safety and efficacy of combination therapy, employing Lianhua Qingwen with Western medicine, to treat COVID-19, using a meta-analysis approach.

SEARCH STRATEGY

China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Embase, and Cochrane Library databases were searched for studies evaluating the effect of Lianhua Qingwen-Western medicine combination therapy in the treatment of COVID-19.

INCLUSION CRITERIA

(1) Research object: hospitalized patients meeting the diagnostic criteria of COVID-19 were included. (2) Intervention measures: patients in the treatment group received Lianhua Qingwen treatment combined with Western medicine, while the control group received either Western medicine or Chinese medicine treatment. (3) Research type: randomized controlled trials and retrospective study were included.

DATA EXTRACTION AND ANALYSIS

Two researchers extracted the first author, the proportion of males and females, age, body temperature, course of treatment, rate of disappearance of main symptoms, duration of fever, adverse reactions, and total effectiveness from the literature. Odds ratio (OR) and 95% confidence interval (CI) were used as the effect value for count data, and mean difference (MD) and 95% CI were used as the effect value for measurement data.

RESULTS

Six articles met the inclusion criteria, including a total of 856 COVID-19 patients. The meta-analysis showed that Lianhua Qingwen combination therapy achieved higher rates of fever reduction (OR = 3.43, 95% CI [1.78, 6.59], P = 0.0002), cough reduction (OR = 3.39, 95% CI [1.85, 6.23], P < 0.0001), recovery from shortness of breath (OR = 10.62, 95% CI [3.71, 30.40], P < 0.0001) and recovery from fatigue (OR = 2.82, 95% CI [1.44, 5.53], P = 0.003), higher total effectiveness rate (OR = 2.51, 95% CI [1.73, 3.64], P < 0.00001), and shorter time to recovery from fever (MD = -1.00, 95% CI [-1.04, 0.96], P < 0.00001), and did not increase the adverse reaction rate (OR = 0.65, 95% CI [0.42, 1.01], P = 0.06), compared to the single medication control.

CONCLUSION

The Lianhua Qingwen and Western medicine combination therapy is highly effective for COVID-19 patients and has good clinical safety. As only a small number of studies and patients were included in this review, more high-quality, multicenter, large-sample-size, randomized, double-blind, controlled trials are still needed for verification.

Nanotechnology-Based Strategies for Effective and Rapid Detection of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic has gained worldwide attention and has prompted the development of innovative diagnostics, therapeutics, and vaccines to mitigate the pandemic. Diagnostic methods based on reverse transcriptase-polymerase chain reaction (RT-PCR) technology are the gold standard in the fight against COVID-19. However, this test might not be easily accessible in low-resource settings for the early detection and diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The lack of access to well-equipped clinical laboratories, requirement for the high level of technical competence, and the cost of the RT-PCR test are the major limitations. Moreover, RT-PCR is unsuitable for application at the point-of-care testing (PoCT) as it is time-consuming and lab-based. Due to emerging mutations of the virus and the burden it has placed on the health care systems, there is a growing urgency to develop sensitive, selective, and rapid diagnostic devices for COVID-19. Nanotechnology has emerged as a versatile technology in the production of reliable diagnostic tools for various diseases and offers new opportunities for the development of COVID-19 diagnostic systems. This review summarizes some of the nano-enabled diagnostic systems that were explored for the detection of SARS-CoV-2. It highlights how the unique physicochemical properties of nanoparticles were exploited in the development of novel colorimetric assays and biosensors for COVID-19 at the PoCT. The potential to improve the efficiency of the current assays, as well as the challenges associated with the development of these innovative diagnostic tools, are also discussed.

Meta-analysis of arbidol versus lopinavir/ritonavir in the treatment of coronavirus disease 2019

Objectives

To systematically evaluate the efficacy and safety of arbidol and lopinavir/ritonavir (LPV/r) in the treatment of coronavirus disease 2019 (COVID‐19) using a meta‐analysis method.

Methods

The China Knowledge Network, VIP database, WanFang database PubMed database, Embase database, and Cochrane Library were searched for a collection of comparative studies on arbidol and lopinavir/ritonavir in the treatment of COVID‐19. Meta‐analysis was used to evaluate the efficacy and safety of Arbidol and lopinavir/ritonavir in the treatment of COVID‐19.

Results

The results of the systematic review indicated that Arbidol had a higher positive‐to‐negative conversion rate of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) nucleic acid on Day 7 (p = 0.03), a higher positive‐to‐negative conversion rate of SARS‐CoV‐2 nucleic acid on Day 14 (p = 0.006), a higher improvement rate of chest computed tomography on Day 14 (p = 0.02), a lower incidence of adverse reactions (p = 0.002) and lower rate of mortality (p = 0.007). There was no difference in the rate of cough disappearance on Day 14 (p = 0.24) or the rate of severe/critical illness (p = 0.07) between the two groups.

Conclusions

Arbidol may be superior to lopinavir/ritonavir in the treatment of COVID‐19. However, due to the small number of included studies and the number of patients, high‐quality multicenter large‐sample randomized double‐blind controlled trials are still needed for verification.

Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital

Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia.

Determining quarantine length and testing frequency for international border opening during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic has resulted in the closure or partial closure of international borders in almost all countries. Here, we investigate the efficacy of imported case detection considering quarantine length and different testing measures for travellers on arrival.

METHODS

We examine eight broad border control strategies from utilizing quarantine alone, pre-testing, entry and exit testing, and testing during quarantine. In comparing the efficacy of these strategies, we calculate the probability of detecting travellers who have been infected up to 2 weeks pre-departure according to their estimated incubation and infectious period. We estimate the number of undetected infected travellers permitted entry for these strategies across a prevalence range of 0.1-2% per million travellers.

RESULTS

At 14-day quarantine, on average 2.2% (range: 0.5-8.2%) of imported infections are missed across the strategies, leading to 22 (5-82) imported cases at 0.1% prevalence per million travellers, increasing up to 430 (106-1641) at 2%. The strategy utilizing exit testing results in 3.9% (3.1-4.9%) of imported cases being missed at 7-day quarantine, down to 0.4% (0.3-0.7%) at 21-day quarantine, and the introduction of daily testing, as the most risk averse strategy, reduces the proportion further to 2.5-4.2% at day 7 and 0.1-0.2% at day 21 dependent on the tests used. Rapid antigen testing every 3 days in quarantine leads to 3% being missed at 7 days and 0.7% at 14 days, which is comparable to PCR testing with a 24-hour turnaround.

CONCLUSIONS

Mandatory testing, at a minimal of pre-testing and on arrival, is strongly recommended where the length of quarantining should then be determined by the destination country's level of risk averseness, pandemic preparedness and origin of travellers. Repeated testing during quarantining should also be utilized to mitigate case importation risk and reduce the quarantining duration required.

Behaviour of aerosols and their role in the transmission of SARS-CoV-2; a scoping review

Covid‐19 has triggered an unprecedented global health crisis. The highly contagious nature and airborne transmission route of SARS‐CoV‐2 virus requires extraordinary measures for its containment. It is necessary to know the behaviour of aerosols carrying the virus to avoid this contagion. This paper describes the behaviour of aerosols and their role in the transmission of SARS‐CoV‐2 according to published models using a scoping review based on the PubMed, Scopus, and WOS databases. From an initial 530 references, 9 papers were selected after applying defined inclusion criteria. The results reinforce the airborne transmission route as a means of contagion of the virus and recommend the use of face masks, extending social distance to more than 2 metres, and natural ventilation of enclosed spaces as preventive measures. These results contribute to a better understanding of SARS‐CoV‐2 and help design effective strategies to prevent its spread.

Nutraceuticals and Herbs in Reducing the Risk and Improving the Treatment of COVID-19 by Targeting SARS-CoV-2

The worldwide transmission of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a deadly or devastating disease is known to affect thousands of people every day, many of them dying all over the planet. The main reason for the massive effect of COVID-19 on society is its unpredictable spread, which does not allow for proper planning or management of this disease. Antibiotics, antivirals, and other prescription drugs, necessary and used in therapy, obviously have side effects (minor or significant) on the affected person, there are still not clear enough studies to elucidate their combined effect in this specific treatment, and existing protocols are sometimes unclear and uncertain. In contrast, it has been found that nutraceuticals, supplements, and various herbs can be effective in reducing the chances of SARS-CoV-2 infection, but also in alleviating COVID-19 symptoms. However, not enough specific details are yet available, and precise scientific studies to validate the approved benefits of natural food additives, probiotics, herbs, and nutraceuticals will need to be standardized according to current regulations. These alternative treatments may not have a direct effect on the virus or reduce the risk of infection with it, but these products certainly stimulate the human immune system so that the body is better prepared to fight the disease. This paper aims at a specialized literary foray precisely in the field of these “cures” that can provide real revelations in the therapy of coronavirus infection

Isothermal amplification and fluorescent detection of SARS-CoV-2 and SARS-CoV-2 variant virus in nasopharyngeal swabs

The COVID-19 pandemic caused by the SARS-CoV-2 is a serious health threat causing worldwide morbidity and mortality. Real-time reverse transcription PCR (RT-qPCR) is currently the standard for SARS-CoV-2 detection. Although various nucleic acid-based assays have been developed to aid the detection of SARS-CoV-2 from COVID-19 patient samples, the objective of this study was to develop a diagnostic test that can be completed in 30 minutes without having to isolate RNA from the samples. Here, we present an RNA amplification detection method performed using reverse transcription loop-mediated isothermal amplification (RT-LAMP) reactions to achieve specific, rapid (30 min), and sensitive (<100 copies) fluorescent detection in real-time of SARS-CoV-2 directly from patient nasopharyngeal swab (NP) samples. When compared to RT-qPCR, positive NP swab samples assayed by fluorescent RT-LAMP had 98% (n = 41/42) concordance and negative NP swab samples assayed by fluorescent RT-LAMP had 87% (n = 59/68) concordance for the same samples. Importantly, the fluorescent RT-LAMP results were obtained without purification of RNA from the NP swab samples in contrast to RT-qPCR. We also show that the fluorescent RT-LAMP assay can specifically detect live virus directly from cultures of both SARS-CoV-2 wild type (WA1/2020), and a SARS-CoV-2 B.1.1.7 (alpha) variant strain with equal sensitivity to RT-qPCR. RT-LAMP has several advantages over RT-qPCR including isothermal amplification, speed (<30 min), reduced costs, and similar sensitivity and specificity.

Asymmetric impact of temperature on COVID-19 spread in India: Evidence from quantile-on-quantile regression approach

The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.

IoT-based analysis for controlling & spreading prediction of COVID-19 in Saudi Arabia

Presently, novel coronavirus outbreak 2019 (COVID-19) is a major threat to public health. Mathematical epidemic models can be utilized to forecast the course of an epidemic and cultivate approaches for controlling it. This paper utilizes the real data of spreading COVID-19 in Saudi Arabia for mathematical modeling and complex analyses. This paper introduces the Susceptible, Exposed, Infectious, Recovered, Undetectable, and Deceased (SEIRUD) and Machine learning algorithm to predict and control COVID-19 in Saudi Arabia.This COVID-19 has initiated many methods, such as cloud computing, edge-computing, IoT, artificial intelligence. The use of sensor devices has increased enormously. Similarly, several developments in solving the COVID-19 crisis have been used by IoT applications. The new technology relies on IoT variables and the roles of symptoms using wearable sensors to forecast cases of COVID-19. The working model involves wearable devices, occupational therapy, condition control, testing of cases, suspicious and IoT elements. Mathematical modeling is useful for understanding the fundamental principle of the transmission of COVID-19 and providing guidance for possible predictions. The method suggested predicts whether COVID-19 would expand or die in the long term in the population. The mathematical study results and related simulation are described here as a way of forecasting the progress and the possible end of the epidemic with three forms of scenarios: 'No Action,' 'Lockdowns and New Medicine.' The lock case slows it down the peak by minimizing infection and impacts area equality of the infected deformation. This study familiarizes the ideal protocol, which can support the Saudi population to breakdown spreading COVID-19 in an accurate and timely way. The simulation findings have been executed, and the suggested model enhances the accuracy ratio of 89.3%, prediction ratio of 88.7%, the precision ratio of 87.7%, recall ratio of 86.4%, and F1 score of 90.9% compared to other existing methods.

Contributions of Smart City Solutions and Technologies to Resilience against the COVID-19 Pandemic: A Literature Review

Since its emergence in late 2019, the COVID-19 pandemic has swept through many cities around the world, claiming millions of lives and causing major socio-economic impacts. The pandemic occurred at an important historical juncture when smart solutions and technologies have become ubiquitous in many cities. Against this background, in this review, we examine how smart city solutions and technologies have contributed to resilience by enhancing planning, absorption, recovery, and adaptation abilities. For this purpose, we reviewed 147 studies that have discussed issues related to the use of smart solutions and technologies during the pandemic. The results were synthesized under four themes, namely, planning and preparation, absorption, recovery, and adaptation. This review shows that investment in smart city initiatives can enhance the planning and preparation ability. In addition, the adoption of smart solutions and technologies can, among other things, enhance the capacity of cities to predict pandemic patterns, facilitate an integrated and timely response, minimize or postpone transmission of the virus, provide support to overstretched sectors, minimize supply chain disruption, ensure continuity of basic services, and offer solutions for optimizing city operations. These are promising results that demonstrate the utility of smart solutions for enhancing resilience. However, it should be noted that realizing this potential hinges on careful attention to important issues and challenges related to privacy and security, access to open-source data, technological affordance, legal barriers, technological feasibility, and citizen engagement. Despite this, this review shows that further development of smart city initiatives can provide unprecedented opportunities for enhancing resilience to the pandemic and similar future events.

Scientific research and innovation response to the COVID-19 pandemic in Peru

Background: COVID-19 has shaken countries at all levels, putting public health at risk. Global efforts have allocated funding for the development of research for the development of vaccines, digital tools, epidemiologic, social, and economic studies. Although these efforts have been developed worldwide, not all countries have prioritized the same topics, and may have a different impact on solving problems and containing the spread of COVID-19. Methods: A descriptive study was conducted with secondary data of "Special Projects COVID-19" in order to analyze the prioritization of proposals and projects to Peruvian needs in the face of pandemic. Two calls were made by the Peruvian research council (CONCyTec); the first with five areas and second with seven. The global amounts financed by each call were 342,857 USD (1,200,000 soles) and 700,000 USD (1,750,000 soles), respectively. Results: A total of 1,101 research projects were presented, 600 (54.5%) in the first call. In this call, 176 (29.3%) projects were from the technological development and innovation and 29 were winners (with a global budget of 1,711,907.25 USD /6,077,270.75 soles). In the second call, 120 (23.9%) projects were from the area of Social and economic research and 21 were winners (global budget of 1,284,002.25 USD/558,208.55 soles) (p=0.043). The largest proportion of winning projects in both calls was 12 (41.4%) in Technological developments and innovation, then five (17.2%) each in telehealth and mobile health, and epidemiological and social studies. Across both calls, 214 (55.8%) and 160 (51.9%) projects were of private organizations and universities, respectively. Conclusions: This research shows ~2% of rapid response "Special Projects COVID-19" were financed by the CONCyTec call with over a million dollars of funds. Although the main topics were technological innovation, detection systems, and vaccines, these priorities have not had a global impact on the epidemiological development of the pandemic in Peru.

Scientific research and innovation response to the COVID-19 pandemic in Peru

Background: COVID-19 has shaken countries at all levels, putting public health at risk. Global efforts have allocated funding for the development of research for the development of vaccines, digital tools, epidemiologic, social, and economic studies. Although these efforts have been developed worldwide, not all countries have prioritized the same topics and may have a different impact on solving problems and containing the spread of COVID-19. Methods: A descriptive study was conducted with secondary data of "Special Projects COVID-19" in order to analyze the prioritization of proposals and projects to Peruvian needs in the face of pandemic. Two calls were made by the Peruvian research council (CONCyTec); the first with five areas and the second with seven. The global amounts financed by each call were 342,857 USD (1,200,000 soles) and 700,000 USD (1,750,000 soles), respectively. Results: A total of 1,101 research projects were presented, 600 (54.5%) in the first call. In this call, 176 (29.3%) projects were from technological development and innovation and 29 were winners (with a global budget of 1,711,907.25 USD /6,077,270.75 soles). In the second call, 120 (23.9%) projects were from the area of Social and economic research and 21 were winners (global budget of 1,284,002.25 USD/558,208.55 soles) (p=0.043). The largest proportion of winning projects in both calls was 12 (41.4%) in Technological developments and innovation, then five (17.2%) each in telehealth and mobile health, and epidemiological and social studies. Across both calls, 214 (55.8%) and 160 (51.9%) projects were of private organizations and universities, respectively. Conclusions: This research shows ~2% of rapid response "Special Projects COVID-19" were financed by the CONCyTec call with over a million dollars of funds. Although the main topics were technological innovation, detection systems, and vaccines, these priorities have not had a global impact on the epidemiological development of the pandemic in Peru.

Review of Current COVID-19 Diagnostics and Opportunities for Further Development

Diagnostic testing plays a critical role in addressing the coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Rapid and accurate diagnostic tests are imperative for identifying and managing infected individuals, contact tracing, epidemiologic characterization, and public health decision making. Laboratory testing may be performed based on symptomatic presentation or for screening of asymptomatic people. Confirmation of SARS-CoV-2 infection is typically by nucleic acid amplification tests (NAAT), which requires specialized equipment and training and may be particularly challenging in resource-limited settings. NAAT may give false-negative results due to timing of sample collection relative to infection, improper sampling of respiratory specimens, inadequate preservation of samples, and technical limitations; false-positives may occur due to technical errors, particularly contamination during the manual real-time polymerase chain reaction (RT-PCR) process. Thus, clinical presentation, contact history and contemporary phyloepidemiology must be considered when interpreting results. Several sample-to-answer platforms, including high-throughput systems and Point of Care (PoC) assays, have been developed to increase testing capacity and decrease technical errors. Alternatives to RT-PCR assay, such as other RNA detection methods and antigen tests may be appropriate for certain situations, such as resource-limited settings. While sequencing is important to monitor on-going evolution of the SARS-CoV-2 genome, antibody assays are useful for epidemiologic purposes. The ever-expanding assortment of tests, with varying clinical utility, performance requirements, and limitations, merits comparative evaluation. We herein provide a comprehensive review of currently available COVID-19 diagnostics, exploring their pros and cons as well as appropriate indications. Strategies to further optimize safety, speed, and ease of SARS-CoV-2 testing without compromising accuracy are suggested. Access to scalable diagnostic tools and continued technologic advances, including machine learning and smartphone integration, will facilitate control of the current pandemic as well as preparedness for the next one.

COVID-19: A review and considerations for the resumption of activities in an IVF laboratory and clinic in Brazil

COVID-19 has caused radical effects on the daily lives of millions of people. The causal agent of the current pandemic is SARS-CoV-2, a virus that causes symptoms related to the respiratory system, leading to severe complications. In the in vitro fertilization (IVF) universe, there are several protocols for infection control and laboratory safety. Some professional associations have issued guidelines recommending measures involving patient flow and IVF practices. This study presents a review and considerations for the resumption of activities in IVF laboratories and clinics in Brazil during the COVID-19 pandemic, according to the guidelines and statements from professional organizations and societies in reproductive medicine.

Investigating the effect of materials and structures for negative pressure ventilators suitable for pandemic situation

The onset of the corona virus disease 2019 (COVID-19) pandemic caused shortages in mechanical ventilators (MVs) essential for the intensive care unit (ICU) in the hospitals. The increasing crisis prompted the investigation of ventilators which is low cost and offers lower health complications. Many researchers are revisiting the use of negative pressure ventilators (NPVs), due to the cost and complications of positive pressure ventilators (PPVs). This paper summarizes the evolution of the MVs, highlighting the limitations of popular positive and negative pressure ventilators and how NPV can be a cost-effective and lower health complication solution. This paper also provides a detailed investigation of the structure and material for the patient enclosure that can be used for a cost-effective NPV system using ANSYS simulations. The simulation results can confirm the selection and also help in developing a low cost while based on readily available materials. This can help the manufacturer to develop low-cost NPV and reduce the pressure on the healthcare system for any pandemic situation similar to COVID-19.

COnVIDa: COVID-19 multidisciplinary data collection and dashboard

Since the first reported case in Wuhan in late 2019, COVID-19 has rapidly spread worldwide, dramatically impacting the lives of millions of citizens. To deal with the severe crisis resulting from the pandemic, worldwide institutions have been forced to make decisions that profoundly affect the socio-economic realm. In this sense, researchers from diverse knowledge areas are investigating the behavior of the disease in a rush against time. In both cases, the lack of reliable data has been an obstacle to carry out such tasks with accuracy. To tackle this challenge, COnVIDa (https://convida.inf.um.es) has been designed and developed as a user-friendly tool that easily gathers rigorous multidisciplinary data related to the COVID-19 pandemic from different data sources. In particular, the pandemic expansion is analyzed with variables of health nature, but also social ones, mobility, etc. Besides, COnVIDa permits to smoothly join such data, compare and download them for further analysis. Due to the open-science nature of the project, COnVIDa is easily extensible to any other region of the planet. In this way, COnVIDa becomes a data facilitator for decision-making processes, as well as a catalyst for new scientific researches related to this pandemic.

COVID-19 quarantine: Two-way interaction between physical activity and mental health

Recent studies have revealed that physical activity significantly reduces the risk of coronavirus disease 2019 (COVID-19) infection by strengthening the immune system. Also, regular physical activity can reduce the risks of developing physical and mental health problems such as diabetes, hypertension, coronary heart disease, stress, anxiety, depression, etc. However, the two-way interaction between physical activity and psychological symptoms has not been well addressed yet. This paper is intended to examine various dimensions of this interaction and its effects on mental health at the time of COVID-19 quarantine.

Characteristics of Three Different Chemiluminescence Assays for Testing for SARS-CoV-2 Antibodies

Several tests based on chemiluminescence immunoassay techniques have become available to test for SARS-CoV-2 antibodies. There is currently insufficient data on serology assay performance beyond 35 days after symptoms onset. We aimed to evaluate SARS-CoV-2 antibody tests on three widely used platforms. A chemiluminescent microparticle immunoassay (CMIA; Abbott Diagnostics, USA), a luminescence immunoassay (LIA; Diasorin, Italy), and an electrochemiluminescence immunoassay (ECLIA; Roche Diagnostics, Switzerland) were investigated. In a multigroup study, sensitivity was assessed in a group of participants with confirmed SARS-CoV-2 (n = 145), whereas specificity was determined in two groups of participants without evidence of COVID-19 (i.e., healthy blood donors, n = 191, and healthcare workers, n = 1002). Receiver operating characteristic (ROC) curves, multilevel likelihood ratios (LR), and positive (PPV) and negative (NPV) predictive values were characterized. Finally, analytical specificity was characterized in samples with evidence of the Epstein–Barr virus (EBV) (n = 9), cytomegalovirus (CMV) (n = 7), and endemic common-cold coronavirus infections (n = 12) taken prior to the current SARS-CoV-2 pandemic. The diagnostic accuracy was comparable in all three assays (AUC 0.98). Using the manufacturers' cut-offs, the sensitivities were 90%, 95% confidence interval [84,94] (LIA), 93% [88,96] (CMIA), and 96% [91,98] (ECLIA). The specificities were 99.5% [98.9,99.8] (CMIA), 99.7% [99.3,99.9] (LIA), and 99.9% [99.5,99.98] (ECLIA). The LR at half of the manufacturers' cut-offs were 60 (CMIA), 82 (LIA), and 575 (ECLIA) for positive and 0.043 (CMIA) and 0.035 (LIA, ECLIA) for negative results. ECLIA had higher PPV at low pretest probabilities than CMIA and LIA. No interference with EBV or CMV infection was observed, whereas endemic coronavirus in some cases provided signals in LIA and/or CMIA. Although the diagnostic accuracy of the three investigated assays is comparable, their performance in low-prevalence settings is different. Introducing gray zones at half of the manufacturers' cut-offs is suggested, especially for orthogonal testing approaches that use a second assay for confirmation.

Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches

To judge the ability of convolutional neural networks (CNNs) to effectively and efficiently transfer image representations learned on the ImageNet dataset to the task of recognizing COVID-19 in this work, we propose and analyze four approaches. For this purpose, we use VGG16, ResNetV2, InceptionResNetV2, DenseNet121, and MobileNetV2 CNN models pre-trained on ImageNet dataset to extract features from X-ray images of COVID and Non-COVID patients. Simulations study performed by us reveal that these pre-trained models have a different level of ability to transfer image representation. We find that in the approaches that we have proposed, if we use either ResNetV2 or DenseNet121 to extract features, then the performance of these approaches to detect COVID-19 is better. One of the important findings of our study is that the use of principal component analysis for feature selection improves efficiency. The approach using the fusion of features outperforms all the other approaches, and with this approach, we could achieve an accuracy of 0.94 for a three-class classification problem. This work will not only be useful for COVID-19 detection but also for any domain with small datasets.

Domiciliary Hospitalization through Wearable Biomonitoring Patches: Recent Advances, Technical Challenges, and the Relation to Covid-19

This article reviews recent advances and existing challenges for the application of wearable bioelectronics for patient monitoring and domiciliary hospitalization. More specifically, we focus on technical challenges and solutions for the implementation of wearable and conformal bioelectronics for long-term patient biomonitoring and discuss their application on the Internet of medical things (IoMT). We first discuss the general architecture of IoMT systems for domiciliary hospitalization and the three layers of the system, including the sensing, communication, and application layers. In regard to the sensing layer, we focus on current trends, recent advances, and challenges in the implementation of stretchable patches. This includes fabrication strategies and solutions for energy storage and energy harvesting, such as printed batteries and supercapacitors. As a case study, we discuss the application of IoMT for domiciliary hospitalization of COVID 19 patients. This can be used as a strategy to reduce the pressure on the healthcare system, as it allows continuous patient monitoring and reduced physical presence in the hospital, and at the same time enables the collection of large data for posterior analysis. Finally, based on the previous works in the field, we recommend a conceptual IoMT design for wearable monitoring of COVID 19 patients.

DFC, Medina E, Sinche F, Santiago Vispo N, Dahoumane SA, Alexis F. Biomedical Science to Tackle the COVID-19 Pandemic: Current Status and Future Perspectives

The coronavirus infectious disease (COVID-19) pandemic emerged at the end of 2019, and was caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has resulted in an unprecedented health and economic crisis worldwide. One key aspect, compared to other recent pandemics, is the level of urgency, which has started a race for finding adequate answers. Solutions for efficient prevention approaches, rapid, reliable, and high throughput diagnostics, monitoring, and safe therapies are needed. Research across the world has been directed to fight against COVID-19. Biomedical science has been presented as a possible area for combating the SARS-CoV-2 virus due to the unique challenges raised by the pandemic, as reported by epidemiologists, immunologists, and medical doctors, including COVID-19's survival, symptoms, protein surface composition, and infection mechanisms. While the current knowledge about the SARS-CoV-2 virus is still limited, various (old and new) biomedical approaches have been developed and tested. Here, we review the current status and future perspectives of biomedical science in the context of COVID-19, including nanotechnology, prevention through vaccine engineering, diagnostic, monitoring, and therapy. This review is aimed at discussing the current impact of biomedical science in healthcare for the management of COVID-19, as well as some challenges to be addressed.

COVID-19 infection, a potential threat to surgical patients and staff? A retrospective cohort study

Background

This study aimed to describe the epidemiologic and clinical characteristics of coronavirus disease 2019 (COVID-19) in surgical patients and medical staff.

Methods

A single-center case series of 1586 consecutive surgical patients was selected at our hospital from January 13 to March 12, 2020. The epidemiological and clinical characteristics of COVID-19 were analyzed and followed up to May 20, 2020. The transmission of COVID-19 between the surgical patients and medical staff was also recorded.

Results

Seventeen (1.07%) surgical patients were diagnosed with COVID-19, with a high incidence in the thoracic department (9.37%), and the median age was 58 years (IQR, 53–73). The median time from hospital admission to COVID-19 diagnosis was 9.0 days (7.0–12.0) and was 6.0 days (4.0–7.0) from the day of surgery to COVID-19 diagnosis. Eleven (64.70%) patients suffered from pulmonary infection before surgery. When COVID-19 was diagnosed, common symptoms were fever (82.35%) and cough (94.12%), and most (82.35%) neutrophil/lymphocyte ratios were high (>3.5). Chest computed tomography (CT) (82.35%) showed bilateral dense shadows. Surgical patients with COVID-19 stayed in the hospital for approximately 35.0 days (25.5–43.0), with a mortality rate of 11.76%. Sixteen medical staff were infected with COVID-19 in the early stage.

Conclusions

In this series of 1586 surgical patients, the COVID-19 infection rate was 1.07%, with an especially high incidence among patients with thoracic diseases. Middle-aged and elderly patients with preoperative pulmonary infection were more susceptible to COVID-19 infection after surgery. Medical staff were infected with COVID-19 and should take protective measures to protect themselves.

EDTA-Anticoagulated Whole Blood for SARS-CoV-2 Antibody Testing by Electrochemiluminescence Immunoassay (ECLIA) and Enzyme-Linked Immunosorbent Assay (ELISA)

While lateral flow test formats can be utilized with whole blood and low sample volumes, their diagnostic characteristics are inferior to immunoassays based on chemiluminescence immunoassay (CLIA) or enzyme-linked immunosorbent assay (ELISA) technology. CLIAs and ELISAs can be automated to a high degree but commonly require larger serum or plasma volumes for sample processing. We addressed the suitability of EDTA-anticoagulated whole blood as an alternative sample material for antibody testing against SARS-CoV-2 by electro-CLIA (ECLIA; Roche, Rotkreuz, Switzerland) and ELISA (IgG and IgA; Euroimmun, Germany). Simultaneously drawn venous serum and EDTA-anticoagulated whole blood samples from 223 individuals were included. Correction of the whole blood results for hematocrit led to a good agreement with the serum results for weakly to moderately positive antibody signals. In receiver-operating characteristic curve analysis, all three assays displayed comparable diagnostic accuracy (area under the curve (AUC)) using corrected whole blood and serum (AUCs: 0.97 for ECLIA and IgG ELISA; 0.84 for IgA ELISA). In conclusion, our results suggest that the investigated assays can reliably detect antibodies against SARS-CoV-2 in hemolyzed whole blood anticoagulated with EDTA. Correction of these results for hematocrit is suggested. This study demonstrates that the automated processing of whole blood for identification of SARS-CoV-2 antibodies with common ECLIA and ELISA methods is accurate and feasible.

Challenges for Microelectronics in Non-Invasive Medical Diagnostics

Microelectronics is emerging, sometimes with changing fortunes, as a key enabling technology in diagnostics. This paper reviews some recent results and technical challenges which still need to be addressed in terms of the design of CMOS analog application specific integrated circuits (ASICs) and their integration in the surrounding systems, in order to consolidate this technological paradigm. Open issues are discussed from two, apparently distant but complementary, points of view: micro-analytical devices, combining microfluidics with affinity bio-sensing, and gamma cameras for simultaneous multi-modal imaging, namely scintigraphy and magnetic resonance imaging (MRI). The role of integrated circuits is central in both application domains. In portable analytical platforms, ASICs offer miniaturization and tackle the noise/power dissipation trade-off. The integration of CMOS chips with microfluidics poses multiple open technological issues. In multi-modal imaging, now that the compatibility of the acquisition chains (thousands of Silicon Photo-Multipliers channels) of gamma detectors with Tesla-level magnetic fields has been demonstrated, other development directions, enabled by microelectronics, can be envisioned in particular for single-photon emission tomography (SPECT): a faster and simplified operation, for instance, to allow transportable applications (bed-side) and hardware pre-processing that reduces the number of output signals and the image reconstruction time.