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COVID-19 has caused global health problems, and so rapid diagnosis is crucial to slow spread of the disease. Herein, a novel lab-on-paper screening method for SARS-CoV-2 Omicron BA.2 variant was developed using a gold nanoparticle-based colorimetric biosensor along with sensitive detection of SARS-CoV-2 antigen using laser desorption ionization-mass spectrometry (LDI-MS). As a result of antigen–antibody interaction, in the presence of SARS-CoV-2 antigen the gold nanoparticles undergo aggregation and change color from red to light purple, allowing for rapid determination of SARS-CoV-2 antigen with the naked eye. Furthermore, the lab-on-paper method can be directly applied as a substrate for sensitive quantitation of SARS-CoV-2 antigen in saliva using LDI-MS without the use of a conventional organic matrix and sample preparation. LDI-MS offers early diagnosis with high sensitivity, rapidity without sample preparation and lower cost per test compared with reverse transcriptase-PCR, which is crucial for preventing mortality in patients with underlying conditions. This method showed linearity over 0.01–1 μg mL−1 covering the cut-off value of 0.048 μg mL−1 for COVID-19 detection in human saliva. Moreover, a colorimetric sensor for urea was also fabricated in-parallel, for prediction of COVID-19 severity in patients with chronic kidney disease. The color change upon increasing urea concentration directly reflected kidney damage, which is related to increasing risk of mortality among patients with COVID-19. Hence, this platform might be a potential device for non-invasive diagnosis of SARS-CoV-2 Omicron BA.2 variant, which is the variant of most concern because it is transmitted more rapidly than the original SARS-CoV-2 virus and the Delta variant. [ABSTRACT FROM AUTHOR]

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data. [ABSTRACT FROM AUTHOR]

The low cost, simplicity, and ease of use of paper microfluidic devices have made them valuable medical diagnostics for applications from pregnancy testing to COVID-19 screening. Meanwhile, the increasing complexity of paper-based microfluidic devices is driving the need to produce new tools and methodologies that enable more robust biological diagnostics and potential therapeutic applications. A new design framework is being used to facilitate both research and fabrication of paper-based microfluidic biological devices to accelerate the investigative process and reduce material utilization and manpower. In this work we present a methodology for this framework to dynamically place and route microfluidic components in a nondiscrete design space where fluid volume usage, surface area utilization, and the timing required to perform specified biological assays are accounted for and optimized while also accelerating the development of potentially lifesaving new devices. [ABSTRACT FROM AUTHOR]

Summary: Coronavirus disease (COVID‐19) has spread all across the world. Low‐ and medium‐income countries are more affected economically and socially compared to developed countries due to the lack of a rapid, robust, and affordable testing infrastructure. Furthermore, the high cost of real‐time polymerase chain reaction (PCR) system, sophisticated user‐handling procedure, and high expense of the conventional clinical tests are the root causes of the less accessibility of the testing systems to the users. In this study, a COVID‐19 Point‐of‐Care (POC) ecosystem model is proposed for the low‐ and medium‐income countries (or energy deprived countries) that will facilitate the technological development with locally available fabrication components. In addition, the nontechnological development phases have also been discussed, which encompasses the collaboration among academia, local as well as government bodies, and entrepreneurial ventures. In addition, a hypothetical design of a microfluidic paper‐based analytical (μPADs) POC platform is proposed to detect COVID‐19 analyte using unprocessed patient‐derived saliva, which is a miniaturized form‐factor of conventional real‐time polymerase chain reaction (PCR) technique. The device contains four major reaction zones, which are sample zone, buffer zone, loop‐mediated isothermal amplification (LAMP) Master Mix zone, Ethylenediamine tetraacetic acid (EDTA) zone, and sensor zone. To obtain quicker test results and easier operation, a handheld image acquisition technique is introduced in this study. It is hypothesized that in a remote setting, the proposed design could be used as an initial guideline to develop a POC COVID‐19 testing system, which may be simple, easy‐to‐use, and cost‐effective. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has become a public health emergency and widely spread around the world. Rapid, accurate and early diagnosis of COVID-19 infection plays a crucial role in breaking this pandemic. However, the detection accuracy is limited for current single-gene diagnosis of SARS-CoV-2. Herein, we develop an autonomous lab-on-paper platform for multiplex gene diagnosis of SARS-CoV-2 by combining reverse transcription recombinase polymerase amplification (RT-RPA) and CRISPR–Cas12a detection. The autonomous lab-on-paper is capable of simultaneously detecting nucleoprotein (N) gene and spike (S) gene of SARS-CoV-2 virus as well as human housekeeping RNAse P gene (an internal control) in a single clinical sample. With the developed platform, 102 copies viral RNA per test can be detected within one hour. Also, the lab-on-paper platform has been used to detect 21 swab clinical samples and obtains a comparable performance to the conventional RT-PCR method. Thus, the developed lab-on-paper platform holds great potential for rapid, sensitive, reliable, multiple molecular diagnostics of COVID-19 and other infectious diseases in resource-limited settings. [ABSTRACT FROM AUTHOR]

Addressing the pressing demand for rapid and inexpensive coagulation testing in cardiovascular care, this study introduces a novel application of repurposed COVID-19 rapid antigen tests (RATs) as paper-based lateral flow assays (LFAs) combined with machine learning for coagulation status evaluation. By further developing a mobile app prototype, we present a platform that enables clinicians to perform immediate and accurate anticoagulant dosing adjustments using existing post-pandemic resources. Our proof-of-concept employs a random forest machine learning classifier to interpret image feature variations on RAT NC membrane, correlating red blood cell (RBC) wicked diffusion distance in recalcified citrated whole blood with changes in coagulative viscosity, easily interpreted. Enhanced by confocal imaging studies of paper microfluidics, our approach provides insights into the mechanisms dissecting coagulation components, achieving high classification precision, recall, and F1-scores. The inverse relationship between RBC wicked diffusion distance and enoxaparin concentration paves the way for machine learning to inform real-time dose prescription adjustments, aligning with individual patient profiles to optimize therapeutic outcomes. This study not only demonstrates the potential of leveraging surplus RATs for coagulation management but also exemplifies a cost-effective, rapid, and smart strategy to enhance clinical decision-making in the post-pandemic era. Highlights: • Repurposed COVID-19 RATs provide an ideal platform for observing differences in blood coagulability. • Random Forest image classification algorithms can facilitate rapid coagulation status assessment on a paper-based LFA platform. [ABSTRACT FROM AUTHOR]

The current approaches of diagnostic platforms for detecting SARS-CoV-2 infections mostly relied on adapting the existing technology. In this work, a simple and low-cost electrochemical sensing platform for detecting SAR-CoV-2 antigen was established. The proposed sensor combined the innovative disposable paper-based immunosensor and cost-effective plant-based anti-SARS-CoV-2 monoclonal antibody CR3022, expressed in Nicotiana benthamiana. The cellulose nanocrystal was modified on screen-printed graphene electrode to provide the abundant COOH functional groups on electrode surface, leading to the high ability for antibody immobilization. The quantification of the presence receptor binding domain (RBD) spike protein of SARS-CoV-2 was performed using differential pulse voltammetry by monitoring the changing current of [Fe(CN) 6 ]3-/4- redox solution. The current change of [Fe(CN) 6 ]3-/4- before and after the presence of target RBD could be clearly distinguished, providing a linear relationship with RBD concentration in the range from 0.1 pg/mL to 500 ng/mL with the minimum limit of detection of 2.0 fg/mL. The proposed platform was successfully applied to detect RBD in nasopharyngeal swab samples with satisfactory results. Furthermore, the paper-based immunosensor was extended to quantify the RBD level in spiked saliva samples, demonstrating the broadly applicability of this system. This electrochemical paper-based immunosensor has the potential to be employed as a point-of-care testing for COVID-19 diagnosis. [Display omitted] • The electrochemical paper-based device integrated with a label-free immunoassay was developed for detecting SARS-CoV-2. • A novel plant-based mAb CR3022 offers a remarkable simplicity and cost-effectiveness in terms of Ab production. • CNC containing the abundant COOH functional groups has the potential to be used as the immobilizing material. • The proposed immunosensor was successfully applied to detect SARS-CoV-2 from nasopharyngeal swabs and saliva samples. [ABSTRACT FROM AUTHOR]

COVID‐19 has been ravaging the world for a long time, and although its effects are currently the same as those of a cold or a fever, timely diagnosis of COVID‐19 in the elderly and in patients with related illnesses is still a matter of great urgency. To address this challenge, we propose a model that combines the strengths of the Swin Transformer and ResNet34 architectures to efficiently diagnose COVID‐19 in elderly and vulnerable patients. In this paper, we design a model that integrates Swin transformer and resnet34, which not only integrates the advantages of transformer and CNN but also achieves excellent performance in this image classification problem. Moreover, a pre‐processing method is also proposed to increase the accuracy of the model to 99.08%. In this paper, experiments were conducted on Kaggle's publicly available three‐classification and four‐classification datasets, respectively, and on the three main evaluation metrics of Accuracy, Precision, and Recall, the first dataset obtained 98.81%, 99.49%, and 97.99%, while the second dataset obtained 88.82%, 88.92%, and 86.38%. These findings highlight the validity and potential of our proposed model for diagnosing the presence or absence of COVID‐19 in elderly and vulnerable patients. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to compare the relative physical recovery and symptoms after SARS-CoV-2 infection between groups confirmed positive or negative to early strains of COVID-19. Methods: A prospective, longitudinal cohort study compared outcomes of metropolitan adults polymerase chain reaction-tested for COVID-19 between March and November 2020 in Western Australia. Control matching was attempted: inpatients (gender, age) and ambulatory clinic (gender, age, asthma, chronic pulmonary disease). One-year follow-up involved three repeated measures: physical function (grip strength and 1-min sit-to-stand) and patient-reported outcomes (Fatigue Severity Scale, modified Medical Research Council dyspnoea scale and Euroqol-5D-5L). Results: Three hundred and forty-four participants were recruited (154 COVID+, age 54 ± 18 years, 75 females [49%]); 190 COVID−, age 52 ± 16 years, 67 females [35%]) prior to national vaccination roll-out. No between-group differences in physical function measures were evident at any time point. Fatigue (OR 6.62, 95% CI 2.74–15.97) and dyspnoea (OR 2.21, 95% CI 1.14–4.30) were higher in the COVID+ group at second assessment (T2). On Euroqol-5D-5L, no between-group differences were evident in the physical function domains of self-care, mobility or usual activities at any time point. However, COVID+ participants were less likely to report an absence of anxiety or depression symptoms at T2 (OR 0.41, 95% CI 0.19–0.89). Conclusions: Neither statistical nor clinically meaningful differences in physical function were evident between COVID+ and COVID− participants to 12-months after acute illness. Symptoms of fatigue, dyspnoea, anxiety or depression were more prevalent in the COVID+ group til ~8 months after illness with between-group differences no longer evident at 1 year. What is known about the topic? COVID-19 evokes multi-system sequelae including persistent physical and mental health symptoms, functional impairments and poorer quality of life. What does this paper add? A COVID+ group had equivalent physical recovery over 12 months; but higher prevalence of fatigue, dyspnoea and anxiety/depression symptoms at ~8 months compared to a COVID− group. What are the implications for practitioners? Health authorities and employers should know that while adequate physical function may return within months, post-acute sequelae of COVID-19 impair sufferers for over 6 months, likely necessitating healthcare support and workplace adjustments to optimise timely return to participation. [ABSTRACT FROM AUTHOR]

Accurate and early diagnosis of COVID-19 can reduce the mortality rate caused by the disease across the globe. Computer-aided diagnosis (CAD) helps radiologists efficiently extract and diagnose the abnormal portions. The healthcare market is currently experiencing rapid development owing to the Internet of Things (IoT). This paper proposes a framework that integrates machine learning and intelligence-based e-Health service systems that can be used as an application of the Internet of Medical Things (IoMT) for the early diagnosis of COVID-19 disease. This framework consists of a classification approach for diagnosing the abnormalities in lung CT images using a whale optimisation algorithm (WOA) optimised wavelet neural network (WNN). WOA optimises the input features, initial weights, hidden nodes, momentum constant, and learning parameters of a WNN in the proposed system. The proposed approach extracts the Laws 16 Texture Energy Measures (LTEM) from the preprocessed CT lung images and classifies the abnormal regions with the help of a WNN classifier. The proposed framework is evaluated using a publicly available COVID-19 dataset that contains both theCOVID-19 and non-COVID-19 cases. The result shows that theproposed approach has a sensitivity of 82%, a specificity of 73.3%, and an accuracy of 84.8%. [ABSTRACT FROM AUTHOR]

Background: Severe infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or influenza virus can cause patients to be admitted to intensive care units (ICUs). It is necessary to understand the differences in clinical characteristics and outcomes between these two types of critically ill patients. Methods: We searched Embase, PubMed, and Web of Science for articles and performed a meta-analysis using Stata 14.0 with a random-effects model. This paper was written in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Thirty-five articles involving 131,692 ICU patients with coronavirus disease 2019 (COVID-19) and 30,286 ICU patients with influenza were included in our meta-analysis. Compared with influenza patients, COVID-19 patients were more likely to be male (odds ratio (OR) = 1.75, 95% CI: 1.54-1.99) and older (standardized mean difference (SMD) = 0.16, 95% CI: 0.03-0.29). In terms of laboratory test results, COVID-19 patients had higher lymphocyte (SMD = 0.38, 95% CI: 0.17-0.59) and platelet counts (SMD = 0.52, 95% CI: 0.29-0.75) but lower creatinine (SMD = −0.29, 95% CI: −0.55-0.03) and procalcitonin levels (SMD = −0.78, 95% CI: −1.11-0.46). Diabetes (SMD = 1.27, 95% CI: 1.08-1.48) and hypertension (SMD = 1.30, 95% CI: 1.05-1.60) were more prevalent in COVID-19 patients, while influenza patients were more likely to have cancer (OR = 0.52, 95% CI: 0.44-0.62), cirrhosis (OR = 0.52, 95% CI: 0.44-0.62), immunodepression (OR = 0.38, 95% CI: 0.25-0.58), and chronic pulmonary diseases (OR = 0.35, 95% CI: 0.24-0.52). We also found that patients with COVID-19 had longer ICU stays (SMD = 0.20, 95% CI: 0.05-0.34), were more likely to develop acute respiratory distress syndrome (OR = 4.90, 95% CI: 2.77-8.64), and had higher mortality (OR = 1.35, 95% CI: 1.17-1.55). Conclusions: There are some differences in the basic characteristics, comorbidities, laboratory test results and complications between ICU patients with COVID-19 and ICU patients with influenza. Critically ill patients with COVID-19 often require more medical resources and have worse clinical outcomes. PROSPERO Registration Number: CRD42023452238 [ABSTRACT FROM AUTHOR]

In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10−⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO's superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic. [ABSTRACT FROM AUTHOR]

This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14–88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably. [Display omitted] • An optoelectronic tongue was fabricated by non-specific receptors for detection of serum metabolites. • The sensor was used to discriminate patients infected by COVID-19 from healthy controls. • The discrimination analysis was achieved by comparison between the total responses of patient and healthy classes. • The severity of the disease and the viral load associated with PCR analysis were estimated. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has created an urgent need for rapid, effective, and low-cost SARS-CoV-2 diagnostic testing. Here, we describe COV-ID, an approach that combines RT-LAMP with deep sequencing to detect SARS-CoV-2 in unprocessed human saliva with a low limit of detection (5-10 virions). Based on a multi-dimensional barcoding strategy, COV-ID can be used to test thousands of samples overnight in a single sequencing run with limited labor and laboratory equipment. The sequencing-based readout allows COV-ID to detect multiple amplicons simultaneously, including key controls such as host transcripts and artificial spike-ins, as well as multiple pathogens. Here, we demonstrate this flexibility by simultaneous detection of 4 amplicons in contrived saliva samples: SARS-CoV-2, influenza A, human STATHERIN, and an artificial SARS calibration standard. The approach was validated on clinical saliva samples, where it showed excellent agreement with RT-qPCR. COV-ID can also be performed directly on saliva absorbed on filter paper, simplifying collection logistics and sample handling. [ABSTRACT FROM AUTHOR]

Accurate and rapid detection and isolation become indispensable to restrict the spread of COVID-19. Since the start of COVID-19 pandemic in December 2019, many indisposal diagnostic tools are being developed incessantly. Out of all presently used tools, the gold standard rRT- PCR tool having very high sensitivity and specificity is a time consuming complicated molecular technique having requirements of special expensive equipment. Here, the main focus of this work is to develop rapid disposal paper capacitance sensor having simple and easy detection. We discovered a strong interaction between limonin and Spike-glycoprotein of SARS-COV-2 in comparison to its interaction with other similar viruses such as HCOV-OC43, HCOV-NL63, HCOV-HKU1, Influenza B and A viruses. The antibody free capacitive sensor having comb electrode structure was fabricated on whatman paper with drop coating of limonin (extracted using green method from pomelo seeds) and calibrated with known swab samples. The Blind test with unknown swab samples shows high sensitivity of 91.5% and high specificity of 88.37%. Requiring low sample volume and detection time and using biodegradable materials in the sensor fabrication assure the potential application as a point of care disposal diagnostic tool. [Display omitted] • A rapid COVID-19 detection kit based on capacitive sensor based has been developed. • Limonin extracted from plant seeds has been used as sensing material. • The sensor exhibited high sensitivity of 91.5%(clinical) with high specificity of 88.37%. • Detection time for the sensor is ∼182s with sample volume requirement of 10 μL. • This paper-based sensor assures inexpensive, easy, onsite detection of COVID-19. [ABSTRACT FROM AUTHOR]

The Black church has long been seen as a crucial partner in addressing public health issues. This paper describes the development, implementation, and evaluation of a community-engaged church intervention addressing COVID-19 vaccine hesitancy in underserved Black communities in Jefferson County, Alabama. We partnered with churches to implement and evaluate the intervention between March and June of 2022 and found that our church partners were capable of significant messaging reach, particularly through electronic means. (Am J Public Health. 2024;114(S5):S392–S395. https://doi.org/10.2105/AJPH.2024.307683) [ABSTRACT FROM AUTHOR]

Rapid, sensitive, and quantitative detection of biomarkers is needed for early diagnosis of disease and surveillance of infectious outbreaks. Here, we exploit a plasmonic syringe filter and surface-enhanced Raman spectroscopy (SERS) in the development of a rapid detection system, using human IgG as a model diagnostic biomarker. The novel assay design facilitates multiple passages of the sample and labeling solution through the detection zone enabling us to investigate and maximize sampling efficiency to the capture substrate. The vertical flow immunoassay process in this study involves the utilization of filter paper embedded with gold nanoparticles (AuNPs) to form a plasmonic substrate. Capture antibody (anti-human IgG) is then immobilized onto the prepared plasmonic paper and inserted into a vertical flow device (syringe filter holder). Sample solution is passed through the filter paper and the target antigen (human IgG) is selectively captured by the immobilized antibody to form an antibody–antigen complex. Next, functionalized AuNPs as extrinsic Raman labels (ERLs) are passed through the filter paper to label the captured biomarker molecules forming a layered structure. This sandwiched geometry enhances plasmonic coupling and SERS signal to provide highly sensitive detection of biomolecules. Systematic studies to investigate the impact of multiple infuse/withdraw cycles of the sample and labeling solutions reveal that antigen and ERL binding are maximized with 10 and 20 cycles, respectively. The optimized assay achieves a detection limit of ∼0.2 ng mL−1 for human IgG with a total assay time of less than 5 minutes, meeting the demands for rapid point of care diagnostics. Additionally, the optimized platform was implemented in the quantitative analysis of the SARS-CoV-2 nucleocapsid protein, the typical target in commercial, FDA-approved rapid antigen tests for COVID-19. [ABSTRACT FROM AUTHOR]

Measuring the colorimetric signals produced by the biospecific accumulation of colorimetric probes and recording the results is a key feature for next-generation paper-based rapid tests. Manual processing of these tests is time-consuming and prone to a loss of accuracy when interpreting faint and patchy signals. Proprietary, closed-source readers and software companies offering automated smartphone-based assay readings have both been criticized for interoperability issues. Here, we introduce a minimal reader prototype composed of open-source hardware and open-source software that has the benefits of automatic assay quantification while avoiding the interoperability issues associated with closed-source readers. An image-processing algorithm was developed to automate the selection of an optimal region of interest and measure the average pixel intensity. When used to quantify signals produced by lateral flow immunoassays for detecting antibodies against SARS-CoV-2, results obtained with the proposed algorithm were comparable to those obtained with a manual method but with the advantage of improving the precision and accuracy when quantifying small spots or faint and patchy signals. [ABSTRACT FROM AUTHOR]

The importance of user-friendly, inexpensive, sensitive, and selective detection of viruses has been highlighted again due to the recent Coronavirus disease 2019 (COVID-19) pandemic. Among the analytical tools, paper-based devices (PADs) have become a leading alternative for point-of-care (POC) testing. In this review, we discuss the recent development strategies and applications in nucleic acid-based, antibody/antigen-based and other affinity-based PADs using optical and electrochemical detection methods for sensing viruses. In addition, advantages and drawbacks of presented PADs are identified. Current state and insights towards future perspectives are presented regarding developing POC diagnosis platform for COVID-19. This review considers state-of-the-art technologies for further development and improvement in PADs performance for virus detection. [Display omitted] • Reviewed developments in PADs for optical and electrochemical detection of viruses. • Highlighted recent applications in antibody/antigen-, nucleic acid-, aptamer- and CRISPR-based PADs for viral detection. • Emphasized low-cost POC diagnostic tools to control the global COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Long COVID-19 (LC-19) is a condition that has affected a high percentage of the population that recovered from the initial disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). LC-19 diagnosis is currently poorly defined because of its variable, multisystem, episodic symptoms, and lack of uniformity in the critical time points associated with the disease. Considering the number of cases, workers' compromised efficiency or inability to return to their duties can affect organizations and impact economies. LC-19 represents a significant burden on multiple levels and effectively reduces quality of life. These factors necessitate the establishment of firm parameters of diagnoses to provide a foundation for ongoing and future studies of clinical characteristics, epidemiology, risk factors, and therapy. In this scoping review, we conducted a literature search across multiple publication sites to identify papers of interest regarding the diagnosis of LC-19. We identified 225 records of interest and categorized them into seven categories. Based on our findings, there are only 11 original papers that outline the diagnostic process in detail with little overlap. This scoping review highlights the lack of consensus regarding the definition and, thereby, the LC-19 diagnosis processes. Due to no clear directive and considering the many unknowns surrounding the natural history of the disease and further recovery/sequelae from COVID-19, continued discussion and agreement on a definition/diagnosis will help future research and management of these patients. [ABSTRACT FROM AUTHOR]

Background: Public health initiatives require coordinated efforts from healthcare, social services and other service providers. Organisational theory tells us that trust is essential for reaching collaborative effectiveness. This paper explores the drivers for initiating and sustaining trust in a temporary public health partnership, in response to a sudden health threat. Methods: This qualitative study analysed the formation process of a multisector partnership for a Covid-19 contact tracing service. Data was collected through 12 interviews, two focus groups, one feedback workshop, and an online survey with workforce members from all seven partner organisations. Purposive maximum variation sampling was used to capture the reflections and experiences of workforce members from all seven partner organisations. A deductive code scheme was used to identify drivers for building and sustaining trust in inter-organisational collaboration. Results: Relational mechanisms emanating from the commitment to the common aim, shared norms and values, and partnership structures affected trust-building. Shared values and the commitment to the common aim appeared to channel partners' behaviour when interacting, resulting in being perceived as a fair, reliable and supportive partner. Shared values were congruent with the design of the partnership in terms of governance structure and communication lines reflecting flat hierarchies and shared decision-making power. Tensions between partner organisations arose when shared values were infringed. Conclusions: When managing trust in a collaboration, partners should consider structural components like governance structure, organisational hierarchy, and communication channels to ensure equal power distribution. Job rotation, recruitment of candidates with the desired personality traits and attitudes, as well as training and development, encourage inter-organisational networking among employees, which is essential for building and strengthening relationships with partner organisations. Partners should also be aware of managing relational dynamics, channelling behaviours through shared values, objectives and priorities and fostering mutual support and equality among partner organisations. [ABSTRACT FROM AUTHOR]

COVID-19 is a lung disease caused by a coronavirus family virus. Due to its extraordinary prevalence and associated death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of coronavirus and the participation of physicians in all countries, information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively. [ABSTRACT FROM AUTHOR]

This review aims to explore the role of professional diagnostic rapid testing of acute respiratory infections (ARIs), especially COVID-19 and influenza, ensuring proper disease management and treatment in Europe, and particularly in Czech Republic, Poland, and Romania. The paper was constructed based on a review of scientific evidence and national and international policies and recommendations, as well as a process of validation by four experts. The development of new testing technologies, treatment options, and increased awareness of the negative multidimensional impact of ARI profiles transformed differential diagnosis into a tangible and desirable reality. This review covers the following topics: (1) the multidimensional impact of ARIs, (2) ARI rapid diagnostic testing platforms and their value, (3) the policy landscape, (4) challenges and barriers to implementation, and (5) a set of recommendations illustrating a path forward. The findings indicate that rapid diagnostic testing, including at the point of care (POC), can have a positive impact on case management, antimicrobial and antibiotic stewardship, epidemiological surveillance, and decision making. Integrating this strategy will require the commitment of governments and the international and academic communities, especially as we identified room for improvement in the access and expansion of POC rapid testing in the focus countries and the inclusion of rapid testing in relevant policies. [ABSTRACT FROM AUTHOR]

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage. [ABSTRACT FROM AUTHOR]

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome with similar traits to common pneumonia. This major pandemic has affected nations both socially and economically, disturbing everyday life and urging the scientific community to develop solutions for the diagnosis and prevention of COVID-19. Reverse transcriptase-polymerase chain reaction (RT–PCR) is the conventional approach used for detecting COVID-19. Nevertheless, the initial stage of the infection is less predictable in PCR tests, making early prediction challenging. A robust and alternative diagnostic method based on digital computerised technologies to support conventional methods would greatly help society. Therefore, this paper reviews recent research based on using machine and federated learning techniques on publicly available datasets comprising Computed Tomography (CT) images, Chest X-ray (CXR) and ultrasound of COVID-19 patients. This paper also analyses the analytical efficiency such as accuracy, sensitivity, specificity and F1-score of models to determine the efficacy. Based on our study, we observed that Machine Learning (ML) was proposed widely in COVID-19 prediction and diagnosis methods. But this method has challenges due to less dataset availability and privacy concerns. However, federated learning-based COVID-19 detection overcame the challenge and provided better efficacy with low datasets and supported medical data privacy. Thus, based on the advantage observed, federated learning-based COVID-19 detection systems should be developed in the future. [ABSTRACT FROM AUTHOR]

In this paper, we present a systematic review and meta-analysis that aims to evaluate the reliability of coronavirus disease diagnostic tests in 2019 (COVID-19). This article seeks to describe the scientific discoveries made because of diagnostic tests conducted in recent years during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Between 2020 and 2021, searches for published papers on the COVID-19 diagnostic were made in the PubMed database. Ninety-nine scientific articles that satisfied the requirements were analyzed and included in the meta-analysis, and the specificity and sensitivity of the diagnostic accuracy were assessed. When compared to serological tests such as the enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay (CLIA), lateral flow immunoassay (LFIA), and chemiluminescent microparticle immunoassay (CMIA), molecular tests such as reverse transcription polymerase chain reaction (RT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR) performed better in terms of sensitivity and specificity. Additionally, the area under the curve restricted to the false-positive rates (AUCFPR) of 0.984 obtained by the antiviral neutralization bioassay (ANB) diagnostic test revealed significant potential for the identification of COVID-19. It has been established that the various diagnostic tests have been effectively adapted for the detection of SARS-CoV-2; nevertheless, their performance still must be enhanced to contain potential COVID-19 outbreaks, which will also help contain potential infectious agent outbreaks in the future. [ABSTRACT FROM AUTHOR]

It is well and widely known that sample pooling could provide an effective and efficient way for fast coronavirus testing among massive asymptomatic individuals. The method of multi-level acceleration for asymptomatic COVID-19 screening has been introduced, and for one and two levels, the optimal group sizes have been obtained. However, there are still multiple challenges. First, it is not clear how to find the optimal group sizes for three or more levels. Second, there is lack of closed-form expressions for the optimal group sizes for two or more levels. Third, it is not clear how to determine the optimal number of levels. And last, it is not known what the maximum achievable speedup is. The motivation of this paper is to address all the above challenges. The optimization of a hierarchical pooling strategy includes its number of levels and the group size of each level. In this paper, based on multi-variable optimization and Taylor approximation, we are able to derive closed-form expressions for the optimal number of levels d ∗ = ln ⁡ (1 / ln ⁡ (1 / q 0)) − 1 , the optimal group sizes m 1 ∗ = e d ∗ = 1 / (e p 0) , m 2 ∗ = e d ∗ − 1 = 1 / (e 2 p 0) ,..., m d ∗ ∗ = e = 1 / (e d ∗ p 0) , and the maximum possible speedup of a hierarchical pooling strategy of 1 / (e p 0 ln ⁡ (1 / p 0)) , where p 0 is the fraction of infected people. The above speedup is nearly a linear function of the reciprocal of p 0 , in the sense that it is asymptotically greater than any sub-linear function (1 / p 0) 1 − ϵ of the reciprocal of p 0 for any small ϵ > 0. Using the results in this paper, we can quickly and easily predict the performance of an optimal hierarchical pooling strategy. For instance, if the fraction of infected people is 0.0001, an 8-level hierarchical pooling strategy can achieve speedup of nearly 400. [ABSTRACT FROM AUTHOR]

Objectives: The COVID-19 pandemic showed distinct waves where cases ebbed and flowed. While each country had slight, nuanced differences, lessons from each wave with country-specific details provides important lessons for prevention, understanding medical outcomes and the role of vaccines. This paper compares key characteristics from the five different COVID-19 waves in Pakistan. Methods: Data was sourced from daily national situation reports (Sitreps) prepared by the National Emergency Operations Centre (NEOC) in Islamabad. We use specific criteria to define COVID-19 waves. The start of each COVID-19 wave is marked by the day of the lowest number of daily cases preceding a sustained increase, while the end is the day with the lowest number of cases following a 7-days decline, which should be lower than the 7 days following it. Key variables such as COVID-19 tests, cases, and deaths with their rates of change to the peak and then to the trough are used to draw descriptive comparisons. Additionally, a linear regression model estimates daily new COVID-19 deaths in Pakistan. Results: Pakistan saw five distinct waves, each of which displayed the typical topology of a complete infectious disease epidemic. The time from wave-start to peak became progressively shorter, and from wave-peak to trough, progressively longer. Each wave appears to also be getting shorter, except for wave 4, which lasted longer than wave 3. A one percent increase in vaccinations decreased deaths by 0.38% (95% CI: -0.67, -0.08) in wave 5 and the association is statistically significant. Conclusion: Each wave displayed distinct characteristics that must be interpreted in the context of the level of response and the variant driving the epidemic. Key indicators suggest that COVID-19 preventive measures kept pace with the disease. Waves 1 and 2 were mainly about prevention and learning how to clinically manage patients. Vaccination started late during wave 3 and its impact on hospitalizations and deaths became visible in wave 5. The impact of highly virulent strains Alpha/B.1.1.7 and Delta/B.1.617.2 variants during wave 3 and milder but more infectious Omicron/B.1.1.529 during wave 5 are apparent. [ABSTRACT FROM AUTHOR]

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience. [ABSTRACT FROM AUTHOR]

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing. [ABSTRACT FROM AUTHOR]

The worldwide spread of the coronavirus (COVID‐19) outbreak has proven devastating to public health. The severity of pneumonia relies on a rapid and accurate diagnosis of COVID‐19 in CT images. Accordingly, a computed tomography (CT) scan is an excellent screening tool for detecting COVID‐19. This paper proposes a deep learning‐based strategy for recognizing and segmenting a COVID‐19 lesion from chest CT images, which would introduce an accurate computer aided decision criteria for the physicians about the severity rate of the patients. Two main stages have been proposed for detecting COVID‐19; first, a convolutional neural network (CNN) deep structure recognizes and classifies COVID‐19 from CT images. Second, a U‐Net deep structure segments the COVID‐19 regions in a semantic manner. The proposed system is trained and evaluated on three different CT datasets for COVID‐19, two of which are used to illustrate the system's segmentation performance and the other is to demonstrate the system's classification ability. Experiment results reveal that the proposed CNN can achieve classification accuracy greater than 0.99, and the proposed U‐Net model outperforms the state‐of‐the‐art in segmentation with an IOU greater than 0.92. [ABSTRACT FROM AUTHOR]

Objective: Fangcang shelter hospital is a form of large temporary hospital developed in China to tackle public health events. Through the case study and analysis of managing a nursing unit in a huge Fangcang shelter hospital transformed from the National Exhibition and Convention Center during the Omicron wave in Shanghai, China between April 9, 2022 and May 24, 2022, this paper aimed to highlight critical implications of public health nurses in health emergencies. Design: A case study was conducted using data collected from a nursing unit with 570 beds. The five characteristics of management were organized as follows: human resource management, establishment and optimization of the core workflow, safety management of high‐risk patients, the grid cooperation mechanism with patient volunteers, as well as humanistic nursing. Results: Analysis of the data of the nursing unit indicated close team cooperation, efficient and orderly process scheduling, good outcomes of patients, and the indispensable role of volunteers. Conclusion: Practice indicated that nursing unit management in a large Fangcang shelter hospital is important to ensure medical order and efficiency. This practical experience can provide valuable reference and data to support for the nursing management of large‐scale public health events, such as infectious disease epidemics. [ABSTRACT FROM AUTHOR]

On March 16, 2020, the Director‐General of the World Health Organization said: "We have a simple message to all countries—test, test, test." This seems like sound advice, but what if limiting the number of tests has a positive effect on infection control? Although this may rarely be the case, the possibility raises an important ethical question that is closely related to a central tension between deontological and consequentialist approaches to ethics. In this paper, we first argue that early during the COVID‐19 pandemic, Japan offers an interesting case because it experienced few deaths due to COVID while the number of tests was limited, suggesting that there may be cases in which low testing contributes to infection control indeed. After that, we examine deontological constraints on such a "low‐testing" policy, focusing on issues related to a supposed "right to know," which is a central issue in medical ethics in general. [ABSTRACT FROM AUTHOR]

With growing demands for diagnosing COVID-19 definite cases, employing radiological images, i.e., the chest X-ray, is becoming challenging. Deep Convolutional Neural Networks (DCNN) propose effective automated models to detect COVID_19 positive cases. In order to improve the total accuracy, this paper proposes using the novel Trigonometric Function (TF) instead of the existing gradient descendent-based training method for training fully connected layers to have a COVID-19 detector with parallel implementation ability. The designed model gets then benchmarked on a verified dataset denominated COVID-Xray-5k. The results get investigated by qualified research with classic DCNN, BWC, and MSAD. The results confirm that the produced detector can present competitive results compared to the benchmark detection models. The paper also examines the class activation map theory to detect the areas probably infected by the Covid-19 virus. As experts confirm, the obtained results get correlated with the clinical recognitions. [ABSTRACT FROM AUTHOR]

Testing remains a key tool for managing health care and making health policy during the coronavirus pandemic, and it will probably be important in future pandemics. Because of false negative and false positive tests, the observed fraction of positive tests—the surface positivity—is generally different from the fraction of infected individuals (the incidence rate of the disease). In this paper a previous method for translating surface positivity to a point estimate for incidence rate, then to an appropriate range of values for the incidence rate consistent with the model and data (the test range), and finally to the risk (the probability of including one infected individual) associated with groups of different sizes is illustrated. The method is then extended to include asymptomatic infections. To do so, the process of testing is modeled using both analysis and Monte Carlo simulation. Doing so shows that it is possible to determine point estimates for the fraction of infected and symptomatic individuals, the fraction of uninfected and symptomatic individuals, and the ratio of infected asymptomatic individuals to infected symptomatic individuals. Inclusion of symptom status generalizes the test range from an interval to a region in the plane determined by the incidence rate and the ratio of asymptomatic to symptomatic infections; likelihood methods can be used to determine the contour of the rest region. Points on this contour can be used to compute the risk (defined as the probability of including one asymptomatic infected individual) in groups of different sizes. These results have operational implications that include: positivity rate is not incidence rate; symptom status at testing can provide valuable information about asymptomatic infections; collecting information on time since putative virus exposure at testing is valuable for determining point estimates and test ranges; risk is a graded (rather than binary) function of group size; and because the information provided by testing becomes more accurate with more tests but at a decreasing rate, it is possible to over-test fixed spatial regions. The paper concludes with limitations of the method and directions for future work. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted. [ABSTRACT FROM AUTHOR]

Digital discourse has emerged as a substantial focus of interest within the pragmatic field. Specifically, (im)politeness practices on social media have increasingly received scholarly attention in the last decade (Tagg, Caroline, Philip Seargeant & Amy Aisha Brown. 2017. Taking offence on social media. Conviviality and conviviality and communication on Facebook. Switzerland: Springer Nature, Palgrave McMillan; Tsoumou, Jean Mathieu. 2020. Analyzing speech acts in politically related Facebook communication. Journal of Pragmatics 167. 80–97). However, research combining COVID-19, Facebook and (im)politeness in a politically polarizing context is still scarce. This paper is an analysis of (im)politeness in Facebook comments posted as reactions to Giuliani's COVID diagnosis. Thus, by combining quantitative and qualitative approaches, the aim of the present paper is twofold: On the one hand, it intends to further our understanding of the manifestation of (im)politeness practices on Facebook through an analysis of reactive comments to Giuliani's Covid-19 diagnosis on BBC news Facebook page. On the other hand, the paper aims to examine how the struggle between impoliteness and politeness divides Facebook users between sympathizers and detractors of the patient. Through a metadiscursive analysis, the identified (im)politeness items are distributed in an uneven fashion, with impoliteness-oriented items prevailing as the dominant macro category against politeness-oriented ones. The findings suggest that users employ different strategies to express or intensify (im)politeness, favoring explicit expressions of impoliteness such as redress/agreement, insults, pointed criticisms/complaints, unpalatable questions and/or presuppositions over others like threats. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the role of the chromatic information in CT scans in COVID-19 detection and we aim to confirm the inclusion of the artificial intelligence findings in assisting COVID-19 diagnosis. This paper proposes a freezing-based convolutional neural network learning using a morphological transformation of CT images to classify COVID-19 cohorts to help in prognostication pneumonia disease monitoring. The experiments made on the collected CT images from previous works have proven to be a powerful aid to recognize the lesions in CT images which works at comprehensively greater accuracy and speed. The proposed CNN architecture has reflected the viral proliferation in infected patients and archives an accuracy of 87.56% with an improvement by 3% compared to the baseline method on the available database of CT images. [ABSTRACT FROM AUTHOR]

Healthcare services in many countries have been partially or completely disrupted by the Coronavirus (COVID-19) pandemic since its onset in the end of 2019. Amongst the most impacted are the elective medical and surgical services in order to conserve resources to care for COVID-19 patients. As the number of infected patients decrease across Canada, elective surgeries are being restarted in a staged manner. Since Otolaryngologists – Head & Neck Surgeons manage surgical diseases of the upper aerodigestive tract where the highest viral load reside, it is imperative that these surgeries resume in a safe manner. The aim of this document is to compile the current best evidence available and provide expert consensus on the safe restart of rhinologic and skull base surgeries while discussing the pre-operative, intra-operative, and post-operative care and tips. Risk assessment, patient selection, case triage, and pre-operative COVID-19 testing will be analyzed and discussed. These guidelines will also consider the optimal use of personal protective equipment for specific cases, general and specific operative room precautions, and practical tips of intra-operative maneuvers to optimize patient and provider safety. Given that the literature surrounding COVID-19 is rapidly evolving, these recommendations will serve to start our specialty back into elective rhinologic surgeries over the next months and they may change as we learn more about this disease. [ABSTRACT FROM AUTHOR]

At the end of 2019, COVID-19 outbreaks occurred one after another in countries worldwide. Managing the outbreak efficiently and stably is an essential public health issue facing countries worldwide. In this paper, based on the SEIR model, we propose a SCEIR model that incorporates close contacts (C) and self-protectors (P). Firstly, the epidemic data of China, the USA, and Italy are predicted and compared with the actual data. Secondly, sensitivity analysis of each parameter in the SCEIR model was conducted using Anylogic. The study shows that the SCEIR model established in this paper has a certain validity. The infection rate in contact with E () etc., has positive effects on the basic regeneration number (R0); the self-isolation rate (φ) etc., has a negative effect on the basic regeneration number (R0). Emergency management measures are proposed according to the influencing factors corresponding to the model parameters. These can provide theoretical guidance for developing effective epidemic prevention and control measures in areas where the epidemic has not yet been controlled. It also provides some reference for formulating prevention and control policies for similar epidemics that may occur in the future. [ABSTRACT FROM AUTHOR]

• Systematic mass testing in closed settings provides information on infection rates. • Test positivity was 11.6%, with only one-quarter reporting symptoms. • The prison wing handling new admissions reported the second-lowest positivity rate. • Testing uptake was higher in residents than in staff members. • Mass testing is a valuable tool to bring outbreaks under control quickly. The aim of this paper was to describe the results of mass asymptomatic testing for COVID-19 in a male prison in England following the declaration of an outbreak. It provides novel data on the implementation of a mass testing regime within a prison during the pandemic. The paper is an observational evaluation of the mass testing conducted for 6 months following the declaration of a COVID-19 outbreak within a prison. It investigated the incidence of positive cases in both staff and residents using polymerase chain reaction testing. Data from October 2020 until March 2021 was included. A total of 2170 tests were performed by 851 residents and 182 staff members; uptake was 48.3% for people living in prison and 30.4% for staff. Overall test positivity was 11.6% (14.3% for residents, 3.0% for staff), with around one-quarter of these reporting symptoms. The prison wing handling new admissions reported the second-lowest positivity rate (9.4%) of the eight wings. Mass testing for COVID-19 over a short space of time can lead to rapid identification of additional cases, particularly asymptomatic cases. Testing that relies on residents and staff reporting symptoms will underestimate the true extent of transmission and will likely lead to a prolonged outbreak. [ABSTRACT FROM AUTHOR]

At the end of 2019, the media cycle was taken over by the news about the coronavirus, soon the whole world was enveloped by the COVID-19 pandemic. According to the New York Times, currently, the number is 4,297,830 deaths worldwide, 202,872,928 total cases. In Brazil, so far, 20,165,672 cases have been registered. Despite the great emergence of this pandemic by the virus caused by SARS-CoV-2 in our country and in the world, several options of diagnostic methodologies have been treated to help us detect this agent, helping to prevent its spread, detecting those who have already had a disease and, in some cases, favoring early treatment. This paper aims at each of these methods, describing how each one of them works in the diagnosis of COVID-19. This work is a bibliographic study, descriptive of the type of integrative literature review, starting from the bibliographic survey and analysisof bibliographic data available from previous researches. [ABSTRACT FROM AUTHOR]

Massive-scale SARS-CoV-2 testing using the SwabSeq diagnostic platform came with quality assurance challenges due to the novelty and scale of sequencing-based testing. The SwabSeq platform relies on accurate mapping between specimen identifiers and molecular barcodes to match a result back to a patient specimen. To identify and mitigate mapping errors, we instituted quality control using placement of negative controls within a rack of patient samples. We designed 2-dimensional paper templates to fit over a 96-position rack of specimens with holes to show the control tube placements. We designed and 3-dimensionally printed plastic templates that fit onto 4 racks of patient specimens and provide accurate indications of the correct control tube placements. The final plastic templates dramatically reduced plate mapping errors from 22.55% in January 2021 to less than 1% after implementation and training in January 2021. We show how 3D printing can be a cost-effective quality assurance tool to mitigate human error in the clinical laboratory. [ABSTRACT FROM AUTHOR]

Covid-19 is a very dangerous disease as a result of the rapid and unprecedented spread of any previous disease. It is truly a crisis that threatens the world since its first appearance in December 2019 until our time. Due to the lack of a vaccine that has proved sufficiently effective so far, the rapid and more accurate diagnosis of this disease is extremely necessary to enable the medical staff to identify infected cases and isolate them from the rest to prevent further loss of life. In this paper, Covid-19 diagnostic strategy (CDS) as a new classification strategy that consists of two basic phases: Feature selection phase (FSP) and diagnosis phase (DP) has been introduced. During the first phase called FSP, the best set of features in laboratory test findings for Covid-19 patients will be selected using enhanced gray wolf optimization (EGWO). EGWO combines both types of selection techniques called wrapper and filter. Accordingly, EGWO includes two stages called filter stage (FS) and wrapper stage (WS). While FS uses many different filter methods, WS uses a wrapper method called binary gray wolf optimization (BGWO). The second phase called DP aims to give fast and more accurate diagnosis using a hybrid diagnosis methodology (HDM) based on the selected features from FSP. In fact, the HDM consists of two phases called weighting patient phase (WP2) and diagnostic patient phase (DP2). WP2 aims to calculate the belonging degree of each patient in the testing dataset to class category using naïve Bayes (NB) as a weight method. On the other hand, K-nearest neighbor (KNN) will be used in DP2 based on the weights of patients in the testing dataset as a new training dataset to give rapid and more accurate detection. The suggested CDS outperforms other strategies according to accuracy, precision, recall (or sensitivity) and F-measure calculations that are equal to 99%, 88%, 90% and 91%, respectively, as showed in experimental results. [ABSTRACT FROM AUTHOR]

Diagnosis of covid-19 using deep learning on CT scan images can play an important role in helping doctors. In this paper, by combining EfficientNet-B2 and vision transformers (ViT -- 1 -- 32) neural networks a new deep transfer learning is proposed. For evaluation, con-fusion matrix, precision, accuracy, recall, and F1 score are used. The experimental results are 0.9838 for validation accuracy, 0.9667 for test accuracy, and 0.9839 for accuracy. [ABSTRACT FROM AUTHOR]