Detection Of Coronavirus Disease 2019 Research Articles

Plasma SARS-CoV-2 N antigen is a powerful molecular marker for early detection of severe COVID-19 in patients and monitoring disease progression

Background and aimsClear and effective indicators for early detection of severe coronavirus disease 2019 (COVID-19) are insufficient. We investigated the clinical value of the plasma SARS-CoV-2 N antigen (plasma N antigen) for severe COVID-19 early identification and disease progression monitoring. Materials and methodsA cross-sectional study compared the diagnostic value of plasma N antigen levels detected within two days after hospital admission in 957 patients with COVID-19 during the BA2.2 outbreak in Shanghai (April 6–June 15, 2022). A follow-up study analyzed the plasma N antigen prognostic value in 274 non-severe patients, and a longitudinal study evaluated its continuous monitoring value in 16 patients with COVID-19 grade changes. ResultsPlasma N antigen concentrations were significantly higher in severely ill than in non-severely ill patients. The plasma N antigen was superior to nasopharyngeal nucleic acid CT values and established COVID-19 blood biomarkers in identifying severe COVID-19. Patients with high plasma N-antigen concentrations at initial admission were more prone to developing severe COVID-19. The changes in plasma N antigen concentrations were consistent with disease progression. Two logistic regression models, including and excluding plasma N antigen, were established, with model 1 (including plasma N antigen) (AUC = 0.971, 0.958–0.980) yielding a better diagnostic value for severe COVID-19 than Model 2 (plasma N antigen excluded). ConclusionThe plasma N antigen is superior to nasopharyngeal nucleic acids and established COVID-19 blood biomarkers for severe COVID-19 early recognition and progression monitoring, enabling the most accurate patient triaging and efficient utilization of medical resources.

Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning

The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.

Experience with the safe admission of breast and thyroid cancer patients in non-endemic areas during an epidemic outbreak.

The outbreaks of infectious diseases, such as coronavirus disease 2019 (COVID-19), have seriously affected the normal work and life of the public, as well as the normal diagnosis and treatment of other diseases due to their strong infectivity, high population susceptibility, and diverse clinical manifestations. Breast and thyroid specialists in non-hotspot epidemic areas of COVID-19 must consider factors, including epidemic prevention and control, breast and thyroid cancers and diseases diagnosis and treatment, and access to medical resources to make a reasonable treatment choice and optimize the treatment process. A cohort study was designed under our center's epidemic prevention and control strategy. The study was conducted between February 3 and April 19, 2020, to explore the safety of clinical diagnosis and treatment of breast and thyroid cancer patients during the epidemic. All the outpatients, inpatients, day-time chemotherapy patients, targeted therapy patients, and relevant medical staff in the observation period in the Department of Breast and Thyroid Surgery in Southwest Hospital in Chongqing municipality, China, were included to investigate the detection and infection rate of COVID-19 and suspected patients. During the observation period, 27,117 patients were admitted to the outpatient unit. We performed 394 inpatient surgeries and 411 day-time surgeries. In our center, 1,046 and 663 patients received day-time chemotherapy and targeted therapy, respectively. All the patients were diagnosed and treated promptly and safely. Three suspected COVID-19 patients were identified in the outpatient unit. Healthcare staff achieved a "zero" infection of COVID-19. The spread and cross-infection of COVID-19 can be avoided in non-hotspot epidemic areas based on scientific prevention and control, and cancer patients can be diagnosed and treated on time. The prevention and control measure implemented in the COVID-19 epidemic for diagnosing and treating cancer patients was effective and can be referenced for other infectious disease outbreaks.

Development of monoclonal antibody-based blocking ELISA for detecting SARS-CoV-2 exposure in animals.

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a significant threat to public health. Besides humans, SARS-CoV-2 can infect several animal species. Highly sensitive and specific diagnostic reagents and assays are urgently needed for rapid detection and implementation of strategies for prevention and control of the infection in animals. In this study, we initially developed a panel of monoclonal antibodies (mAbs) against SARS-CoV-2 nucleocapsid protein. To detect SARS-CoV-2 antibodies in a broad spectrum of animal species, an mAb-based blocking enzyme-linked immunosorbent assay (bELISA) was developed. Test validation using a set of animal serum samples with known infection status obtained an optimal percentage of inhibition cut-off value of 17.6% with diagnostic sensitivity of 97.8% and diagnostic specificity of 98.9%. The assay demonstrates high repeatability as determined by a low coefficient of variation (7.23%, 4.89%, and 3.16%) between-runs, within-run, and within-plate, respectively. Testing of samples collected over time from experimentally infected cats showed that the bELISA was able to detect seroconversion as early as 7 days post-infection. Subsequently, the bELISA was applied for testing pet animals with coronavirus disease 2019 (COVID-19)-like symptoms and specific antibody responses were detected in two dogs. The panel of mAbs generated in this study provides a valuable tool for SARS-CoV-2 diagnostics and research. The mAb-based bELISA provides a serological test in aid of COVID-19 surveillance in animals. IMPORTANCE Antibody tests are commonly used as a diagnostic tool for detecting host immune response following infection. Serology (antibody) tests complement nucleic acid assays by providing a history of virus exposure, no matter symptoms developed from infection or the infection was asymptomatic. Serology tests for COVID-19 are in high demand, especially when the vaccines become available. They are important to determine the prevalence of the viral infection in a population and identify individuals who have been infected or vaccinated. ELISA is a simple and practically reliable serological test, which allows high-throughput implementation in surveillance studies. Several COVID-19 ELISA kits are available. However, they are mostly designed for human samples and species-specific secondary antibody is required for indirect ELISA format. This paper describes the development of an all species applicable monoclonal antibody (mAb)-based blocking ELISA to facilitate the detection and surveillance of COVID-19 in animals.

COVID Variants, Villain and Victory: A Bioinformatics Perspective.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

PERFORMANCE OF RAPID ANTIGEN TEST IN COMPARISON TO REAL-TIME RT-PCR IN DIAGNOSIS OF COVID-19 INFECTION: A SHORT STUDY

Objective: The global pandemic of Coronavirus Disease 2019 (COVID-19) is still spreading. Because of this, it is urgently necessary to develop quick, easy, and accurate assays to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. For the purpose of diagnosing COVID-19 cases, the quick SARS-CoV-2 antigen detection test’s performance attributes should be assessed and contrasted with the gold standard real-time reverse transcription-polymerase chain reaction (RT-PCR) assay. Methods: A retrospective analysis was done during January to May, 2022 at IPGME&R and SSKM Hospital, Kolkata, India. 2842 paired samples were taken from all patients reporting to the hospital for Rapid Antigen Test (RAT) and RT-PCR. The sensitivity and specificity were calculated to evaluate the performance of the RAT. Results: Of 2842 samples, 229 (8%) were positive, and 2613 (92%) were negative for SARS-CoV-2 RNA by real-time RT-PCR assay. The rapid SARS-CoV-2 antigen detection test’s sensitivity and specificity were 77.29% (177 out of 229) and 99.54% (2601 out of 2613), respectively. Positive predictive value was 93.65% and negative predictive value was 93% with the Ct value ≤35 of real-time RT-PCR result. Conclusion: RAT-positive samples are also positive by real-time RT-PCR test though RAT-negative patient with symptom, should further test for real-time RT-PCR as sensitivity is bit low particularly in symptomatic patient respect to real-time RT-PCR result. Hence, we can conclude that specificity- and sensitivity-wise real-time RT-PCR method is best, but during pandemic/outbreak situation, RAT is good alternative for fast screening and point-of-care test of COVID-19 detection.

Simultaneous screening for COVID-19 and tuberculosis, India.

To evaluate the implementation of new operational workflows for simultaneous screening of coronavirus disease 2019 (COVID-19) and tuberculosis at four high-volume COVID-19 testing centres located in tertiary hospitals in Mumbai, India. Each centre already offering antigen-detecting rapid diagnostic tests were equipped with a rapid molecular testing platform for COVID-19 and tuberculosis, sufficient laboratory staff, and reagents and consumables for screening. Using a verbal tuberculosis questionnaire, a patient follow-up agent screened individuals visiting the COVID-19 testing centres. Presumptive tuberculosis patients were asked to provide sputum samples for rapid molecular testing. Subsequently, we reversed our operational workflow to also screen patients visiting tuberculosis outpatient departments for COVID-19, using rapid diagnostic tests. From March to December 2021, we screened 14 588 presumptive COVID-19 patients for tuberculosis, of whom 475 (3.3%) were identified as having presumptive tuberculosis. Of these, 288 (60.6%) were tested and 32 individuals (11.1%) were identified as tuberculosis positive (219 cases per 100 000 individuals screened). Of the tuberculosis-positive individuals, three had rifampicin-resistant tuberculosis. Among the remaining 187 presumptive tuberculosis cases not tested, 174 reported no symptoms at follow-up and 13 individuals either refused testing or could not be traced. Of the 671 presumptive tuberculosis cases screened for COVID-19, 17 (2.5%) were positive by antigen rapid diagnostic tests, and five (0.7%) who tested negative, later tested positive on the molecular testing platform (2483 COVID-19 cases per 100 000 individuals screened). Simultaneous screening for COVID-19 and tuberculosis in India is operationally feasible and can improve real-time on-site detection of COVID-19 and tuberculosis.

Standardization of in-house anti-IgG and IgA ELISAs for the detection of COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). RT-PCR detection of viral RNA represents the gold standard method for diagnosis of COVID-19. However, multiple diagnostic tests are needed for acute disease diagnosis and assessing immunity during the COVID-19 outbreak. Here, we developed in-house anti-RBD IgG and IgA enzyme-linked immunosorbent assays (ELISAs) using a well-defined serum sample panel for screening and identification of human SARS-CoV-2 infection. We found that our in-house anti-SARS-CoV-2 IgG ELISA displayed a 93.5% sensitivity and 98.8% specificity whereas our in-house anti-SARS-CoV-2 IgA ELISA provided assay sensitivity and specificity at 89.5% and 99.4%, respectively. The agreement kappa values of our in-house anti-SARS-CoV-2 IgG and IgA ELISA assays were deemed to be excellent and fair, respectively, when compared to RT-PCR and excellent for both assays when compared to Euroimmun anti-SARS-CoV-2 IgG and IgA ELISAs. These data indicate that our in-house anti-SARS-CoV-2 IgG and IgA ELISAs are compatible performing assays for the detection of SARS-CoV-2 infection.

Paper-based colorimetric detection of COVID-19 using aptasenor based on biomimetic peroxidase like activity of ChF/ZnO/CNT nano-hybrid

Corona Virus Disease 2019 (COVID-19) as the infectious disease caused the pandemic disease around the world through infection by SARS-CoV-2 virus. The common diagnosis approach is Quantitative RT-PCR (qRT-PCR) which is time consuming and labor intensive. In the present study a novel colorimetric aptasensor was developed based on intrinsic catalytic activity of chitosan film embedded with ZnO/CNT (ChF/ZnO/CNT) on 3,3′,5,5′-tetramethylbenzidine (TMB) substrate. The main nanocomposite platform was constructed and functionalized with specific COVID-19 aptamer. The construction subjected with TMB substrate and H2O2 in the presence of different concentration of COVID-19 virus. Separation of aptamer after binding with virus particles declined the nanozyme activity. Upon addition of virus concentration, the peroxidase like activity of developed platform and colorimetric signals of oxidized TMB decreased gradually. Under optimal conditions the nanozyme could detect the virus in the linear range of 1–500 pg mL and LOD of 0.05 pg mL. Also, a paper-based platform was used for set up the strategy on applicable device. The paper-based strategy showed a linear range between 50 and 500 pg mL with LOD of 8 pg mL. The applied paper based colorimetric strategy showed reliable results for sensitive and selective detection of COVID-19 virus with the cost-effective approach.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

Robust Combination Testing: Methods and Application to COVID-19 Detection

Simple and affordable testing tools are often not accurate enough to be operationally relevant. For coronavirus disease 2019 (COVID-19) detection, rapid point-of-care tests are cheap and provide results in minutes, but they largely fail policy makers’ accuracy requirements. We propose an analytical methodology, based on robust optimization, that identifies optimal combinations of results from cheap tests for increased predictive accuracy. This methodological tool allows policy makers to credibly quantify the benefits from combination testing and thus, break the trade-off between cost and accuracy. Our methodology is robust to noisy and partially missing input data and incorporates operational constraints—relevant considerations in practice. We apply our methodology to two data sets containing individual-level results of multiple COVID-19 rapid antibody and antigen tests, respectively, to generate Pareto-dominating receiver operating characteristic curves. We find that combining only three rapid tests increases out-of-sample area under the curve by 4% (6%) compared with the best-performing individual test for antibody (antigen) detection. We also find that a policy maker who requires a specificity of at least 0.95 can improve sensitivity by 8% and 2% for antibody and antigen testing, respectively, relative to available combination testing heuristics. Our numerical analysis demonstrates that robust optimization is a powerful tool to avoid overfitting, accommodate missing data, and improve out-of-sample performance. Based on our analytical and empirical results, policy makers should consider approving and deploying a curated combination of cheap point-of-care tests in settings where “gold standard” tests are too expensive or too slow. This paper was accepted by Stefan Scholtes, healthcare management. Supplemental Material: The data files and electronic companion are available at https://doi.org/10.1287/mnsc.2023.4826 .

Effect of SNPs in TMPRSS2 to severe COVID-19 in patients with prostate cancer.

e17043 Background: The cellular transmembrane protease serine 2 (TMPRSS2) cleaves the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to let viral cell entry. Patients (pts) with prostate carcinoma (PCa) are expected to be at higher risk for coronavirus disease 2019 (COVID-19). However, TMPRSS2 transcription is significantly down-regulated by luteinizing hormone releasing hormone agonists (LHRH-a) and enzalutamide used to treat different PCa stages and this could alter the ability of SARS-CoV-2 to enter target cells. This study aims to evaluate if Single Nucleotide Polymorphisms (SNPs) in the TMPRSS2 gene are associated to a different risk for developing severe COVID-19. Methods: This is the translational part of a large retrospective-prospective real world study designed to estimate whether anti-androgen therapy for PCa is associated to a lower severity of COVID-19. Blood samples were collected from volunteer pts affected with PCa treated at our Institution who developed COVID-19. To be included, pts should receive at least LHRH-a for locally advanced o metastatic hormone sensitive or resistant disease. Genomic DNA of PCa pts was genotyped with the Axiom Human Genotyping SARS-CoV-2 array. Raw genotyping data were analyzed using the Axiom Analysis Suite and genotypes of 219 TMPRSS2 variants were extracted. Identified SNPs were used to compare reference and alternative allele counts between PCa pts and a control cohort of healthy European subjects (n = 2848; DOI:10.1056/NEJMoa2020283) collected in the Milan area (Italy) with chi squared test. Uni- and multivariate logistic regressions were performed to investigate associations between such identified SNPs and severe COVID-19, defined as any between the need for hospitalization or access to an intensive care unit or oxygen therapy. Results: 45 pts were enrolled from November 2020 to May 2022. Median age at COVID-19 detection was 77 years (range 51-94). 10/45 (22%) pts had severe COVID-19, but none died. At COVID-19 diagnosis, 4/45 pts (9%) were receiving chemotherapy for PCa, 13/45 (29%) new generation hormonal agents, 24/45 (53%) LHRH-a, the remaining 4/45 (9%) other therapies. Compared to the control cohort, we identified 52 TMPRSS2 variants associated with the susceptibility to SARS-CoV-2 infection in the cohort of PCa pts (P-value < 0.05). Among them, 3 SNPs were associated with severity in univariable logistic regression models: 2 resulted to be protective (rs9974589, p 0.01, OR 0.26; rs28442123, p 0.04, OR 0.30), and one was associated with increased risk of severe COVID-19 (rs2070788, p 0.01, OR 3.8). A multivariate logistic model (p = 0.0004, AUC = 0.88) was performed considering rs11908828 (p = 0,0109, OR = 32.25), rs28442123 (p = 0.03, OR = 0.11) and rs2070788 (p = 0.02, OR = 6.33). Conclusions: These preliminary findings point to a possible role of TMPRSS2 SNPs in modulating the severity of COVID-19 in PCa pts. Validation in wider series is needed.

Characteristics and Risks of COVID-19 in Pregnant Women & Neonates: A Retrospective, Age-matched Comparison Study—Wuhan Experience in the Management of Peripartum Health

ABSTRACT Background: Coronavirus disease 2019 (COVID-19) has since spread globally causing the ongoing pandemic. However, there are few studies on the symptoms and outcomes of pregnant women with COVID-19 and their newborns. Limited data are available comparing pregnant women with and without COVID-19 pneumonia. Our study aimed to find symptoms unique in pregnancy to help the early detection of COVID-19 in pregnant women and to evaluate its impact on perinatal women and their newborns. Methods: Clinical data were reviewed and collected for 11 pregnant women with laboratory-confirmed COVID-19 (pregnant confirmed cohort) who were consecutively admitted to Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province, from January 26 to February 26, 2020. They were subsequently matched by age with 11 pregnant women suspected of COVID-19 (pregnant suspected group) who had typical changes in chest computed tomography (CT) scans but were repeatedly negative for reverse-transcriptase-polymerase-chain-reaction (RT-PCR), with 11 pregnant women who were not infected (pregnant non-infected group), and with 44 non-pregnant women with confirmed COVID-19 (Non-pregnant confirmed group). Results: The significant symptoms in pregnant confirmed women were mild fever and fatigue. Both the confirmed and suspected women had a fever (45.5% vs. 72.7%, P > 0.05) which were barely distinguishable. The chest CT scans of confirmed women showed unspecific manifestations, such as bilateral pleural effusions and slightly increased densities. Eight of eleven (72.7%) confirmed women did not feel anything unusual until abnormalities were found on chest CT scans on the admission screening test. All three groups had elevated white blood cell count, neutrophil count, lactate dehydrogenase, and reduced total protein. Compared to the normal group, women in the confirmed group had significantly reduced hemoglobin, lymphocyte, albumin, and increased lactate dehydrogenase (P < 0.05). The infection did not increase the risk of premature delivery, premature rupture of the membrane, or comorbidities in pregnancy. The neonates of the three groups of mothers had similar symptoms, except for those of the confirmed group had increased heart rate (130–143 bpm) and mean arterial pressure (93–112 mmHg) than those born to the normal group. The infection of the mother did lead to more complications in their neonates. And there was no evidence of vertical transmission during late pregnancy. Conclusions: Pregnant women were often asymptomatic and had incidental abnormal findings on a chest CT scan on admission. Screening of CT scans in disease prevalent areas is thus of crucial importance. Even after the laboratory confirmation, the manifestation of the CT scan could be atypical, which alerted the necessity of protection for healthcare workers. COVID-19 did not increase the risk of complications in pregnant women and their neonates.

Epidemiological studies of COVID-19 disease

The Chinese population having epidemiological link with wholesale market of seafood develop respiratory illness with pneumonia like clinical presentations in Hubei Province, Wuhan City, recently in December 2019. Laboratory results revealed a novel coronavirus that was named severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) and disease it caused given name of coronavirus disease 2019 (COVID-19). COVID-19 turn out to be third outbreak of human pathogenic coronaviruses and first pandemic of 21st century. The previous coronaviruses proved merely as a tip of iceberg after emergence of recently identified SARS-CoV-2 with potential of pandemic status that significantly revealed the concealed capabilities of virulence and contagiousness of betacoronaviruses group. The aim of this review is to discuss the origin of COVID-19, transmission patterns, susceptible hosts, factors that increase disease severity, nucleic acid base diagnosis, mortality and morbidity rates and therapeutic option against COVID-19. The diagnostic methods like microarray and RNA-targeting CRISPR were adopted recently for COVID-19 diagnosis after advancing them further for rapid and accurate detection of COVID-19 and high throughput.

Detecting COVID-19 in chest X-ray images

<span lang="EN-US">One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Self-attention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are non-COVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where non-COVID-19 cases could cover more than just a normal condition.</span>

A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator.

In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Moreover, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for providing tailored treatment to patients and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on the subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen-antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip featuring an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results indicate that the limit of detection of our device reaches 100 fg/ml (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Furthermore, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform presents a promising approach for attaining highly sensitive, selective, multiplexed, and quantitative point-of-care diagnosis and distinction between COVID-19 and influenza.

Targeted self-supervised attention network for coronavirus disease 2019 detection

The worldwide spread of the coronavirus disease 2019 (COVID-19) has had a significant impact on healthcare systems globally. Timely and accurate diagnosis is crucial for controlling and preventing outbreaks. Computed tomography (CT) has been demonstrated as an effective diagnostic tool for COVID-19. Deep learning techniques have been applied in related fields to solve relevant problems. Normally, they achieve good performance on large datasets directly. However, due to the need to protect patient privacy and the sudden outbreak of the epidemic, the amount of publicly available CT data is insufficient for utilizing these methods. In addition, some natural images, which are used for model pre-training, are quite different from chest CT images, causing some inevitable errors. To address these issues, a targeted self-supervised attention network based on an improved inception ResNet called SIRANet is proposed. SIRANet incorporates a self-supervised learning method during the pre-training process and introduces a attention module to improve feature selection and focus on key informative features. Moreover, a transfer learning-based strategy for COVID-19 detection is presented and experiments are conducted on three widely used datasets, including ImageNet, an unlabeled chest CT dataset, and a COVID-19 CT dataset. Experimental results show that SIRANet outperforms other state-of-the-art techniques in terms of accuracy, precision, recall, and f1-score, demonstrating its effectiveness in capturing key informative features from unlabeled and labeled images. The implementation of SIRANet is available at https://github.com/Xiiin6/COVID-19.

Depthwise convolution based pyramid ResNet model for accurate detection of COVID-19 from chest X-Ray images

ABSTRACT The global pandemic of coronavirus disease 2019 (COVID-19) causes severe respiratory problems in humans. The Chest X-ray (CXR) imaging technique majorly assists in detecting abnormalities in the chest and lung areas caused by COVID-19. Hence, developing an automatic system for CXR-based COVID-19 detection is vital for disease diagnosis. To accomplish this requirement, an enhanced Residual Network (ResNet) model is proposed in this paper for accurate COVID-19 detection. The proposed model combines the Depthwise Separable Convolutional ResNet and Pyramid dilated module(DSC-ResNet-PDM) for deep feature extraction. Employing the DSC layer minimizes the number of parameters to mitigate the overfitting issue. Further, the pyramid dilated module is used for extracting multi-scale features. The extracted features are finally fed into the optimized Medium Gaussian kernel Support Vector Machine classifier (MGKSVM) for COVID-19 detection. The proposed model attained an accuracy of 99.5%, which is comparatively higher than the standard ResNet50 and ResNet101 models.

Performance Evaluation of SARS-CoV-2 Viral Transport Medium Produced by Bangladesh Reference Institute for Chemical Measurements.

A viral transport medium (VTM) was developed following the Centers for Disease Control and Prevention, USA (US-CDC) standard operating procedure (SOP) DSR-052-05 with necessary improvisation and was used for storing coronavirus disease 2019 (COVID-19) swab specimens. Considering Bangladesh's supply chain and storage conditions, improvisation was essential for extending sample storage time while retaining efficiency. In-house VTM was produced using Hank's balanced salt solution (HBSS) supplemented with 1% bovine serum albumin V (BSA), 0.5 µg /mL of gentamicin sulfate, and 100 µg/mL of fluconazole. The produced VTM composition, quality, sterility, specificity, and efficiency were verified in-house and through an independent contract research organization (CRO). An accelerated stability study projected that under the recommended temperature (4 °C), it would remain stable for four months and preserve samples for over a month. The real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) test detected the targeted N gene and ORF1ab gene from the VTM stored samples. Our VTM is equally as effective as the Sansure Biotech VTM in keeping SARS-CoV-2 RNA specimens detectable in rRT-PCR (100% sensitivity and specificity in random and blinded samples). In conclusion, the BRiCM VTM will make the battle against pandemics easier by effectively collecting and storing nasopharyngeal and oropharyngeal swabs for COVID-19 detection.

Protective effect of ursodeoxycholic acid on COVID-19 in patients with chronic liver disease.

Ursodeoxycholic acid (UDCA) may reduce susceptibility to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection by downregulating angiotensin-converting enzyme 2 (ACE2), based on recent experimental investigation. This study aimed to determine the potential protective effect of UDCA against SARS-CoV-2 infection in patients with chronic liver disease. Patients with chronic liver disease receiving UDCA (taking UDCA ≥1 month) at Beijing Ditan Hospital between January 2022 and December 2022 were consecutively enrolled. These patients were matched in a 1:1 ratio to those with liver disease not receiving UDCA during the same period by using a propensity score matching analysis with nearest neighbor matching algorithm. We conducted a phone survey of coronavirus disease 2019 (COVID-19) infection during the early phase of the pandemic liberation (from 15 December 2022 to 15 January 2023). The risk of COVID-19 was compared in two matched cohorts of 225 UDCA users and 225 non-UDCA users based on patient self-report. In the adjusted analysis, the control group was superior to the UDCA group in COVID-19 vaccination rates and liver function indicators, including γ-glutamyl transpeptidase and alkaline phosphatase (p < 0.05). UDCA was associated with a lower incidence of SARS-CoV-2 infection (UDCA 85.3% vs. control 94.2%, p = 0.002), more mild cases (80.0% vs. 72.0%, p = 0.047), and shorter median time from infection to recovery (5 vs. 7 days, p < 0.001). Logistic regression analysis showed that UDCA was a significant protective factor against COVID-19 infection (OR: 0.32, 95%CI: 0.16-0.64, p = 0.001). Furthermore, diabetes mellitus (OR: 2.48, 95%CI: 1.11-5.54, p = 0.027) and moderate/severe infection (OR: 8.94, 95%CI: 1.07-74.61, p = 0.043) were more likely to prolong the time from infection to recovery. UDCA therapy may be beneficial in reducing COVID-19 infection risk, alleviating symptoms, and shortening the recovery time in patients with chronic liver disease. However, it should be emphasized that the conclusions were based on patient self-report rather than classical COVID-19 detection by experimental investigations. Further large clinical and experimental studies are needed to validate these findings.