Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19

SARS-CoV-2 subgenomic RNA: formation process and rapid molecular diagnostic methods

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus disease-2019 (COVID-19) is spreading worldwide and posing enormous losses to human health and socio-economic. Due to the limitations of medical and health conditions, it is still a huge challenge to develop appropriate discharge standards for patients with COVID-19 and to use medical resources in a timely and effective manner. Similar to other coronaviruses, SARS-CoV-2 has a very complex discontinuous transcription process to generate subgenomic RNA (sgRNA). Some studies support that sgRNA of SARS-CoV-2 can only exist when the virus is active and is an indicator of virus replication. The results of sgRNA detection in patients can be used to evaluate the condition of hospitalized patients, which is expected to save medical resources, especially personal protective equipment. There have been numerous investigations using different methods, especially molecular methods to detect sgRNA. Here, we introduce the process of SARS-CoV-2 sgRNA formation and the commonly used molecular diagnostic methods to bring a new idea for clinical detection in the future.

Electrochemical biosensor for the evaluation of monoclonal antibodies targeting the N protein of SARS-CoV-2 virus

The emergence of COVID-19 caused by the coronavirus SARS-CoV-2 has prompted a global pandemic that requires continuous research and monitoring. This study presents a design of an electrochemical biosensing platform suitable for the evaluation of monoclonal antibodies targeting the SARS-CoV-2 nucleocapsid (N) protein. Screen-printed carbon electrodes (SPCE) modified with gold nanostructures (AuNS) were applied to design a versatile and sensitive sensing platform. Electrochemical techniques, including electrochemical impedance spectroscopy (EIS) and square wave voltammetry (SWV), were used to investigate the interactions between immobilised recombinant N (rN) protein and several monoclonal antibodies (mAbs). The electrochemical characterisation of SPCE/AuNS/rN demonstrated a successful immobilisation of rN, enhancing the electron transfer kinetics. Affinity interactions between immobilised rN and four mAbs (mAb-4B3, mAb-4G6, mAb-12B2, and mAb-1G5) were explored. Although mAb-4B3 showed some non-linearity, the other monoclonal antibodies exhibited specific and well-defined interactions followed by the formation of an immune complex. The biosensing platform demonstrated high sensitivity in the linear range (LR) from 0.2 nM to 1 nM with limits of detection (LOD) ranging from 0.012 nM to 0.016 nM for mAb-4G6, mAb-12B2, and mAb-1G5 and limits of quantification (LOQ) values ranging from 0.035 nM to 0.139 nM, as determined by both EIS and SWV methods. These results highlight the system's potential for precise and selective detection of monoclonal antibodies specific to the rN. This electrochemical biosensing platform provides a promising route for the sensitive and accurate detection of monoclonal antibodies specific to the rN protein.

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images

BACKGROUND

COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies' diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid radiologists in detecting and quantifying COVID-19-related lung infections.

OBJECTIVE

To develop deep learning-based models to classify and quantify COVID-19-related lung infections.

METHODS

Initially, Dual Decoder Attention-based Semantic Segmentation Networks (DDA-SSNets) such as Dual Decoder Attention-UNet (DDA-UNet) and Dual Decoder Attention-SegNet (DDA-SegNet) are proposed to facilitate the dual segmentation tasks such as lung lobes and infection segmentation in chest X-ray (CXR) images. The lung lobe and infection segmentations are mapped to grade the severity of COVID-19 infection in both the lungs of CXRs. Later, a Genetic algorithm-based Deep Convolutional Neural Network classifier with the optimum number of layers, namely GADCNet, is proposed to classify the extracted regions of interest (ROI) from the CXR lung lobes into COVID-19 and non-COVID-19.

RESULTS

The DDA-SegNet shows better segmentation with an average BCSSDC of 99.53% and 99.97% for lung lobes and infection segmentations, respectively, compared with DDA-UNet with an average BCSSDC of 99.14% and 99.92%. The proposed DDA-SegNet with GADCNet classifier offered excellent classification results with an average BCCAC of 99.98%, followed by the GADCNet with DDA-UNet with an average BCCAC of 99.92% after extensive testing and analysis.

CONCLUSIONS

The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.

Usefulness of receptor binding domain protein-based serodiagnosis of COVID-19

Objectives

This study evaluated the performance of recombinant receptor binding domain (RBD) protein-based enzyme-linked immunosorbent assays (RBD-ELISAs) for detecting anti-SARS-CoV-2 immunoglobulin (Ig) G and IgM antibodies.

Methods

In this study, 705 sera from SARS-CoV-2-infected individuals and 315 sera from healthy individuals were analyzed.

Results

The RBD-ELISA IgG exhibited high specificity (99.1%) and moderate sensitivity (48.0%), with an overall diagnostic accuracy of 73.5%. RBD-ELISA IgM demonstrated specificity at 94.6% and sensitivity at 51.1%, with an accuracy of 72.8%. Both assays displayed improved performance when analyzing samples collected 15-21 days post-symptom onset, achieving sensitivity and accuracy exceeding 88% and 90%, respectively. Combining RBD-ELISA IgG and IgM in parallel analysis enhanced sensitivity to 98.6% and accuracy to 96.2%. Comparing these RBD-ELISAs with commercially available tests, the study found overlapping sensitivity and similar specificity values. Notably, the combined RBD-ELISA IgG and IgM showed superior performance. Cross-reactivity analysis revealed low false-positive rates (4.4% for IgG, 3.7% for IgM), primarily with viral infections.

Conclusion

This research underscores the potential of RBD-based ELISAs for COVID-19 diagnosis, especially when assessing samples collected 15-21 days post-symptom onset and utilizing a parallel testing approach. The RBD protein's immunogenicity and specificity make it a valuable tool for serodiagnosis, offering an alternative to polymerase chain reaction-based methods, particularly in resource-limited settings.

Nanotechnology-based theranostic and prophylactic approaches against SARS-CoV-2

SARS-CoV-2 (COVID-19) pandemic has been an unpredicted burden on global healthcare system by infecting over 700 million individuals, with approximately 6 million deaths worldwide. COVID-19 significantly impacted all sectors, but it very adversely affected the healthcare system. These effects were much more evident in the resource limited part of the world. Individuals with acute conditions were also severely impacted. Although classical COVID-19 diagnostics such as RT-PCR and rapid antibody testing have played a crucial role in reducing the spread of infection, these diagnostic techniques are associated with certain limitations. For instance, drawback of RT-PCR diagnostics is that due to degradation of viral RNA during shipping, it can give false negative results. Also, rapid antibody testing majorly depends on the phase of infection and cannot be performed on immune compromised individuals. These limitations in current diagnostic tools require the development of nanodiagnostic tools for early detection of COVID-19 infection. Therefore, the SARS-CoV-2 outbreak has necessitated the development of specific, responsive, accurate, rapid, low-cost, and simple-to-use diagnostic tools at point of care. In recent years, early detection has been a challenge for several health diseases that require prompt attention and treatment. Disease identification at an early stage, increased imaging of inner health issues, and ease of diagnostic processes have all been established using a new discipline of laboratory medicine called nanodiagnostics, even before symptoms have appeared. Nanodiagnostics refers to the application of nanoparticles (material with size equal to or less than 100 nm) for medical diagnostic purposes. The special property of nanomaterials compared to their macroscopic counterparts is a lesser signal loss and an enhanced electromagnetic field. Nanosize of the detection material also enhances its sensitivity and increases the signal to noise ratio. Microchips, nanorobots, biosensors, nanoidentification of single-celled structures, and microelectromechanical systems are some of the most modern nanodiagnostics technologies now in development. Here, we have highlighted the important roles of nanotechnology in healthcare sector, with a detailed focus on the management of the COVID-19 pandemic. We outline the different types of nanotechnology-based diagnostic devices for SARS-CoV-2 and the possible applications of nanomaterials in COVID-19 treatment. We also discuss the utility of nanomaterials in formulating preventive strategies against SARS-CoV-2 including their use in manufacture of protective equipment, formulation of vaccines, and strategies for directly hindering viral infection. We further discuss the factors hindering the large-scale accessibility of nanotechnology-based healthcare applications and suggestions for overcoming them.

Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images

In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a ray of hope amidst the crisis. However, as with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic models, although proficient, are not immune to adversarial attacks. These attacks, characterized by carefully crafted perturbations to input data, can potentially disrupt the models' decision-making processes. In the medical context, such vulnerabilities could have dire consequences, leading to misdiagnoses and compromised patient care. To address this, we propose a two-phase defense framework that combines advanced adversarial learning and adversarial image filtering techniques. We use a modified adversarial learning algorithm to enhance the model's resilience against adversarial examples during the training phase. During the inference phase, we apply JPEG compression to mitigate perturbations that cause misclassification. We evaluate our approach on three models based on ResNet-50, VGG-16, and Inception-V3. These models perform exceptionally in classifying radiology images (X-ray and CT) of lung regions into normal, pneumonia, and COVID-19 pneumonia categories. We then assess the vulnerability of these models to three targeted adversarial attacks: fast gradient sign method (FGSM), projected gradient descent (PGD), and basic iterative method (BIM). The results show a significant drop in model performance after the attacks. However, our defense framework greatly improves the models' resistance to adversarial attacks, maintaining high accuracy on adversarial examples. Importantly, our framework ensures the reliability of the models in diagnosing COVID-19 from clean images.

Label-Free and Colorimetric Detection of Influenza A Virus via Receptor-Mediated Viral Fusion with Plasmonic Vesicles

The rapid transmission and numerous re-emerging human influenza virus variants that spread via the respiratory system have led to severe global damage, emphasizing the need for detection tools that can recognize active and intact virions with infectivity. Here, this work presents a plasmonic vesicle-mediated fusogenic immunoassay (PVFIA) comprising gold nanoparticle (GNP) encapsulating fusogenic polymeric vesicles (plasmonic vesicles; PVs) for the label-free and colorimetric detection of influenza A virus (IAV). The PVFIA combines two sequential assays: a biochip-based immunoassay for target-specific capture and a PV-induced fusion assay for color change upon the IAV-PV fusion complex formation. The PVFIA demonstrates excellent specificity in capturing the target IAV, while the fusion conditions and GNP induce a significant color change, enabling visual detection. The integration of two consecutive assays results in a low detection limit (100.7919 EID50 mL-1 ) and good reliability (0.9901), indicating sensitivity that is 104.208 times higher than conventional immunoassay. Leveraging the PV viral membrane fusion activity renders the PVFIA promising for point-of-care diagnostics through colorimetric detection. The innovative approach addresses the critical need for detecting active and intact virions with infectivity, providing a valuable tool with which to combat the spread of the virus.

C-MEMS-derived glassy carbon electrochemical biosensors for rapid detection of SARS-CoV-2 spike protein

According to a World Health Organization (WHO) report, the world has experienced more than 766 million cases of positive SARS-CoV-2 infection and more than 6.9 million deaths due to COVID through May 2023. The WHO declared a pandemic due to the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and the fight against this pandemic is not over yet. Important reasons for virus spread include the lack of detection kits, appropriate detection techniques, delay in detection, asymptomatic cases and failure in mass screening. In the last 3 years, several researchers and medical companies have introduced successful test kits to detect the infection of symptomatic patients in real time, which was necessary to monitor the spread. However, it is also important to have information on asymptomatic cases, which can be obtained by antibody testing for the SARS-CoV-2 virus. In this work, we developed a simple, advantageous immobilization procedure for rapidly detecting the SARS-CoV-2 spike protein. Carbon-MEMS-derived glassy carbon (GC) is used as the sensor electrode, and the detection is based on covalently linking the SARS-CoV-2 antibody to the GC surface. Glutaraldehyde was used as a cross-linker between the antibody and glassy carbon electrode (GCE). The binding was investigated using Fourier transform infrared spectroscopy (FTIR) characterization and cyclic voltammetric (CV) analysis. Electrochemical impedance spectroscopy (EIS) was utilized to measure the change in total impedance before and after incubation of the SARS-CoV-2 antibody with various concentrations of SARS-CoV-2 spike protein. The developed sensor can sense 1 fg/ml to 1 µg/ml SARS-CoV-2 spike protein. This detection is label-free, and the chances of false positives are minimal. The calculated LOD was ~31 copies of viral RNA/mL. The coefficient of variation (CV) number is calculated from EIS data at 100 Hz, which is found to be 0.398%. The developed sensor may be used for mass screening because it is cost-effective. A schematic representation of the SARS-CoV-2 spike protein sensing using surface functionalized glassy carbon electrode.

Efficient Selective Adsorption of SARS-CoV-2 via the Recognition of Spike Proteins Using an Affinity Spongy Monolith

Since the outbreak of COVID-19, SARS-CoV-2, the infection has been spreading to date. The rate of false-negative result on a polymerase chain reaction (PCR) test considered the gold standard is roughly 20%. Therefore, its accuracy poses a question as well as needs improvement in the test. This study reports fabrication of a substrate of an anti-spike protein (AS)-immobilized porous material having selective adsorption toward a spike protein protruding from the surface of SARS-CoV-2. We have employed an organic polymer substrate called spongy monolith (SPM). The SPM has through-pores of about 10 μm and is adequate for flowing liquid containing virus particles. It also involves an epoxy group on the surface, enabling arbitrary proteins such as antibodies to immobilize. When antibodies of the spike protein toward receptor binding domain were immobilized, selective adsorption of the spike protein was observed. At the same time, when mixed analytes of spike proteins, lysozymes and amylases, were flowed into an AS-SPM, selective adsorption toward the spike proteins was observed. Then, SARS-CoV-2 was flowed into the BSA-SPM or AS-SPM, amounts of SARS-CoV-2 adsorption toward the AS-SPM were much larger compared to the ones toward the BSA-SPM. Furthermore, rotavirus was not adsorbed to the AS-SPM at all. These results show that the AS-SPM recognizes selectively the spike proteins of SARS-CoV-2 and may be possible applications for the purification and concentration of SARS-CoV-2.

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.

CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification

Since the COVID-19 pandemic outbreak, over 760 million confirmed cases and over 6.8 million deaths have been reported globally, according to the World Health Organization. While the SARS-CoV-2 virus carried by COVID-19 patients can be identified though the reverse transcription-polymerase chain reaction (RT-PCR) test with high accuracy, clinical misdiagnosis between COVID-19 and pneumonia patients remains a challenge. Therefore, we developed a novel CovC-ReDRNet model to distinguish COVID-19 patients from pneumonia patients as well as normal cases. ResNet-18 was introduced as the backbone model and tailored for the feature representation afterward. In our feature-based randomized neural network (RNN) framework, the feature representation automatically pairs with the deep random vector function link network (dRVFL) as the optimal classifier, producing a CovC-ReDRNet model for the classification task. Results based on five-fold cross-validation reveal that our method achieved 94.94%, 97.01%, 97.56%, 96.81%, and 95.84% MA sensitivity, MA specificity, MA accuracy, MA precision, and MA F1-score, respectively. Ablation studies evidence the superiority of ResNet-18 over different backbone networks, RNNs over traditional classifiers, and deep RNNs over shallow RNNs. Moreover, our proposed model achieved a better MA accuracy than the state-of-the-art (SOTA) methods, the highest score of which was 95.57%. To conclude, our CovC-ReDRNet model could be perceived as an advanced computer-aided diagnostic model with high speed and high accuracy for classifying and predicting COVID-19 diseases.

High-throughput isolation of SARS-CoV-2 nucleocapsid antibodies for improved antigen detection

SARS-CoV-2 nucleocapsid protein (NP) is the main target for COVID-19-diagnostic PCR and antigen rapid diagnostic tests (Ag-RDTs). Ag-RDTs are more convenient than PCR tests for point-of-care testing or self-testing to identify the SARS-CoV-2 antigen. The sensitivity and specificity of this method depends mainly on the affinity and specificity of NP-binding antibodies; therefore, antigen-antibody binding is key elements for the Ag-RDTs. Here, we applied the high-throughput antibody isolation platform that has been utilized to isolate therapeutic antibodies against rare epitopes. Two NP antibodies were identified to recognize non-overlapping epitopes with high affinity. One antibody specifically binds to SARS-CoV-2 NP, and the other rapidly and tightly binds to SARS-CoV-2 NP with cross-reactivity to SARS-CoV NP. Furthermore, these antibodies were compatible with a sandwich enzyme-linked immunosorbent assay that exhibited enhanced sensitivity for NP detection compared to the previously isolated NP antibodies. Thus, the NP antibody pair is applicable to more sensitive and specific Ag-RDTs, highlighting the utility of a high-throughput antibody isolation platform for diagnostics development.

Reverse transcription-loop-mediated isothermal amplification (RT-LAMP) assay as a rapid molecular diagnostic tool for COVID-19 in healthcare workers

In December 2019, the Chinese Center for Disease Control (CDC of China) reported an outbreak of pneumonia in the city of Wuhan (Hubei province, China) that haunted the world, resulting in a global pandemic. This outbreak was caused by a betacoronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several of these cases have been observed in healthcare professionals working in hospitals and providing care on the pandemic's frontline. In the present study, nasopharyngeal swab samples of healthcare workers were used to assess the performance of the reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay and subsequently compared with the real-time reverse-transcription quantitative PCR (RT-qPCR) method. Thus, in this study, we validated a method for detecting SARS-CoV-2 based on RT-LAMP that can be used to diagnose these workers. The methodology used was based on analyzing the sensitivity, specificity, evaluation of the detection limit, and cross-reaction with other respiratory viruses. The agreement was estimated using a dispersion diagram designed using the Bland-Altman method. A total of 100 clinical specimens of nasopharyngeal swabs were collected from symptomatic and asymptomatic healthcare workers in Pelotas, Brazil, during the SARS-CoV-2 outbreak. RT-LAMP assay, it was possible to detect SARS-CoV-2 in 96.7% of the healthcare professionals tested using the E gene and N gene primers approximately and 100% for the gene of human β-actin. The observed agreement was considered excellent for the primer set of the E and N genes (k = 0.957 and k = 0.896), respectively. The sensitivity of the RT-LAMP assay was positive for the primer set of the E gene, detected to approximately 2 copies per reaction. For the primer set of the N gene, the assay was possible to verify an LoD of approximately 253 copies per reaction. After executing the RT-LAMP assay, no positive reactions were observed for any of the virus respiratory tested. Therefore, we conclude that RT-LAMP is effective for rapid molecular diagnosis during the COVID-19 outbreak period in healthcare professionals.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Semi-Supervised COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using Probabilistic Active Contour and CNN Segmentation

Purpose

A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images.

Methods

First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.

Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images.

Results

A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1×10⁻⁴ in low-resolution and 5.1×10⁻⁵ for high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10% on average.

Conclusion

The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered as an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust and It may provide valuable information to differentiate between survived and deceased patients.

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Background and Objective

In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures.

Methods

Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes.

Results

PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases.

Conclusion

The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

Performance of three screening tools to predict COVID-19 positivity in emergency department patients

BACKGROUND

COVID-19 symptoms vary widely. This retrospective study assessed which of three clinical screening tools-a nursing triage screen (NTS), an ED review of systems (ROS) performed by physicians and physician assistants and a standardised ED attending (ie, consultant) physician COVID-19 probability assessment (PA)-best identified patients with COVID-19 on a subsequent reverse transcription PCR (RT-PCR) confirmation.

METHODS

All patients admitted to Boston Medical Center from the ED between 27 April 2020 and 17 May 2020 were included. Sensitivity, specificity and positive predictive value (PPV) and negative predictive value (NPV) were calculated for each method. Logistic regression assessed each tool's performance.

RESULTS

The attending physician PA had higher sensitivity (0.62, 95% CI 0.53 to 0.71) than the NTS (0.46, 95% CI 0.37 to 0.56) and higher specificity (0.76, 95% CI 0.72 to 0.80) than the NTS (0.71, 95% CI 0.66 to 0.75) and ED ROS (0.62, 95% CI 0.58 to 0.67). Categorisation as moderate or high probability on the ED physician PA was associated with the highest odds of having COVID-19 in regression analyses (adjusted OR=4.61, 95% CI 3.01 to 7.06). All methods had a low PPV (ranging from 0.26 for the ED ROS to 0.40 for the attending physician PA) and a similar NPV (0.84 for both the NTS and the ED ROS, and 0.89 for the attending physician PA).

CONCLUSION

The ED attending PA had higher sensitivity and specificity than the other two methods, but none was accurate enough to replace a COVID-19 RT-PCR test in a clinical setting where transmission control is crucial. Therefore, we recommend universal COVID-19 testing prior to all admissions.

CeO2@NH2 functionalized electrodes for the rapid detection of SARS-CoV-2 spike receptor binding domain

A detection method based on an electrochemical aptasensor has been developed as an alternative fast, portable, simple, inexpensive, and high-accuracy detection method for detecting the SARS-CoV-2 Spike Receptor Binding Domain (spike RBD). The CeO₂@NH₂ functionalized Screen Printed Carbon Electrode (SPCE) was used to immobilize an aminated aptamer of spike RBD protein via glutaraldehyde as a linker. The aptamer's interaction with the SARS-CoV-2 Spike RBD was measured via the [Fe(CN)₆]^4-/3- redox system signal. Experimental conditions were optimized using a Box-Behnken experimental design and showed that the optimal conditions of the SARS-CoV-2 aptasensor were 1.5 ng mL^-1 of aptamer, immobilization of aptamer for 60 minutes, and Spike RBD incubation for 10 minutes. The developed aptasensor was able to detect the standard SARS-CoV-2 Spike RBD with a detection limit of 0.017 ng mL^-1 in the range of 0.001-100 ng mL^-1. This aptasensor was used to detect salivary and oropharyngeal swab samples of normal individuals with the addition of Spike RBD, and the recoveries were 92.96% and 96.52%, respectively. The testing on nasopharyngeal swab samples of COVID-19 patients showed that the aptasensor results were comparable with the qRT-PCR results. Thus, the developed aptasensor has the potential to be applied as a SARS-CoV-2 rapid test method for clinical samples.

Analyzing the diffusion and duration of antibodies to SARS-CoV-2 during the natural infection and comparison with vaccination

To monitor SARS-CoV-2 diffusion, we measured anti-Spike S1/S2 IgGs in the serum of nearly 4000 workers employed in several healthcare facilities for one year. We found that the antibody response persists at least over 8 months in symptomatic subjects, particularly in individuals with anosmia/dysgeusia. Moreover, analyzing a smaller cohort (124 healthcare employees of which 57 had a previous history of SARS-CoV-2 exposure) vaccinated with two doses of Comirnaty vaccine, we observed that in symptomatic subjects previously exposed to SARS-CoV-2 one dose vaccine was sufficient to stimulate very high levels of antibodies, suggesting that these subjects should take only one dose of vaccine.

In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images

The virus responsible for COVID-19 is mutating day by day with more infectious characteristics. With the limited healthcare resources and overburdened medical practitioners, it is almost impossible to contain this virus. The automatic identification of this viral infection from chest X-ray (CXR) images is now more demanding as it is a cheaper and less time-consuming diagnosis option. To that cause, we have applied deep learning (DL) approaches for four-class classification of CXR images comprising COVID-19, normal, lung opacity, and viral pneumonia. At first, we extracted features of CXR images by applying a local binary pattern (LBP) and pre-trained convolutional neural network (CNN). Afterwards, we utilized a pattern recognition network (PRN), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (KNN) classifiers on the extracted features to classify aforementioned four-class CXR images. The performances of the proposed methods have been analyzed rigorously in terms of classification performance and classification speed. Among different methods applied to the four-class test images, the best method achieved classification performances with 97.41% accuracy, 94.94% precision, 94.81% recall, 98.27% specificity, and 94.86% F1 score. The results indicate that the proposed method can offer an efficient and reliable framework for COVID-19 detection from CXR images, which could be immensely conducive to the effective diagnosis of COVID-19-infected patients.

Densely attention mechanism based network for COVID-19 detection in chest X-rays

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

Socio-demographic Heterogeneity in Prevalence of SARS-COV-2 Infection and Death Rate: Relevance to Black College Student Knowledge of COVID-19 and SARS-COV-2

Black and Brown communities are affected disproportionately by COVID-19. In an attempt to learn if young Black college students unknowingly contribute to the spread of the COVID-19 in their communities, using surveys, this pilot study gauges the general safety knowledge and basic scientific knowledge of Black college students about SARS-COV-2 virus and COVID-19 at an HBCU. We also investigated whether students enrolled in chemistry courses designed for STEM (Science, Technology, and Engineering Majors) majors displayed increased knowledge of SARS-COV-2 and COVID-19 in comparison to their non-STEM major peers. Two sets of surveys with multiple choice questions, one with 25 and the other with 34 questions, were designed to assess general safety knowledge and basic scientific knowledge of the students about COVID-19 and the SARS-COV-2 virus. Survey questions were administered through Blackboard learning management system to one hundred eighty-seven (187) students in the summer of 2020 to two freshman non-science majors and in the fall of 2020 to one freshman non-science-major class, two freshmen STEM-major classes, and one senior STEM-major class. All students self-registered in the 6 chemistry classes at North Carolina A&T State University at random with no predetermined criteria. Results of the study show that regardless of their year of study, majority (> 90%) of the students possess basic scientific knowledge and are aware of the safety precautions concerning SARS-COV-2 virus and COVID-19. Majority of non-science major freshmen answered the basic safety questions correctly but were not able to choose the correct answers for the more specific scientific questions concerning SARS-COV-2 and COVID-19. Surprisingly, there was no significant difference in basic scientific knowledge regarding SARS-COV-2 and COVID-19 between STEM and non-STEM student populations, and first year STEM students were just as knowledgeable as senior STEM students. Based on these data, we speculate that students surveyed here have an acceptable basic understanding of how SARS-CoV-2 is transmitted, and therefore, they may not be a source of COVID-19 transmission to Black and Brown communities as this study confirms they are receiving accurate information about SARS-COV-2 and COVID-19. Possession of crucial timely and accurate knowledge about the health and safety is important in fighting racism and to gain equity within the society at large. By sharing the acquired knowledge, students can serve as positive role models for others in the community thus encouraging them to pursue science. Education brings equity, sharing the acquired knowledge encourages others to continue their education and succeed in obtaining higher degrees and better jobs as remedies for social inequality. Spread of accurate knowledge on various aspects of COVID-19 will also help remove fears of vaccination and hesitation towards visits to health clinics to resolve health issues. Relying on the results of this pilot study, we plan to explore these important factors further in our next study.

CRISPR-Based Diagnostics and Microfluidics for COVID-19 Point-of-Care Testing: A Review of Main Applications

An ongoing pandemic of coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). So far, there have been various approaches for SARS-CoV-2 detection, each having its pros and cons. The current gold-standard method for SARS-CoV-2 detection, which offers acceptable specificity and sensitivity, is the quantitative reverse transcription-PCR (qRT-PCR). However, this method requires considerable cost and time to transport samples to specialized laboratories and extract, amplify, and detect the viral genome. On the other hand, antigen and antibody testing approaches that bring rapidity and affordability into play have lower sensitivity and specificity during the early stages of COVID-19. Moreover, the immune response is variable depending on the individual. Methods based on clustered regularly interspaced short palindromic repeats (CRISPR) can be used as an alternative approach to controlling the spread of disease by a high-sensitive, specific, and low-cost molecular diagnostic system. CRISPR-based detection systems (CRISPR-Dx) target the desired sequences by specific CRISPR-RNA (crRNA)-pairing on a pre-amplified sample and a subsequent collateral cleavage. In the present article, we have reviewed different CRISPR-Dx methods and presented their benefits and drawbacks for point-of-care testing (POCT) of suspected SARS-CoV-2 infections at home or in small clinics.

Clinical Application of Antibody Immunity Against SARS-CoV-2: Comprehensive Review on Immunoassay and Immunotherapy

The current COVID-19 global pandemic poses immense challenges to global health, largely due to the difficulty to detect infection in the early stages of the disease, as well as the current lack of effective antiviral therapy. Research and understanding of the human immune system can provide important theoretical and technical support for the clinical diagnosis and treatment of COVID-19, the clinical implementations of which include immunoassays and immunotherapy, which play a crucial role in the fight against the pandemic. This review consolidates the current scientific evidence for immunoassay, which includes multiple methods of detecting antigen and antibody against SARS-CoV-2. We compared the characteristics, advantages and disadvantages, and clinical applications of these three detection techniques. In addition to detecting viral infections, knowledge on the body's immunity against the virus is desirable; thus, the immunotherapy-based neutralizing antibody (nAb) detection methods were discussed. We also gave a brief introduction to the new immunoassay technology such as biosensing. This was followed by an in-depth and extensive review on a variety of immunotherapy methods. It includes convalescent plasma therapy, neutralizing antibody-based treatments targeting different regions of SARS-CoV-2, immunotherapy targeted on the host cell including inhibiting the host cell receptor and cytokine storm, as well as cocktail antibodies, cross-neutralizing antibodies, and immunotherapy based on cross-reactivity between viral epitopes and autoepitopes and autoantibody. Despite the development of various immunological testing methods and antibody therapies, the current global situation of COVID-19 is still tense. We need more efficient detection methods and more reliable antibody therapies. The up-to-date knowledge on therapeutic strategies will likely help clinicians worldwide to protect patients from life-threatening viral infections.

Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation

The COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improve the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. The achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset. Source code is available at https://github.com/YujiaKCL/COVID-CT-Similarity-Regularization.

Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images

Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.

Comparison of the RSNA chest CT classification system and CO-RADS system in reporting COVID-19 pneumonia in symptomatic and asymptomatic patients

Background

Coronavirus disease (COVID-19) is a new infection with three pandemic waves up till now. CT plays an important role in diagnosis with multiple reporting systems that can be used during CT analysis. We aimed to compare reporting using the recommendations of the radiological society of North America (RSNA) versus the coronavirus disease reporting and data system (CO-RADS) and to assess the performance of CT if used in asymptomatic patients as a screening. Two hundred and fifty-one patients who underwent chest CT scanning either due to clinical suspicion or as screening before hospital admission were included in this retrospective observational cross-sectional study. This was followed by RT-PCR for confirmation. Three radiologists with different years of experience interpreted the CT findings using the RSNA recommendations and the CO-RADS reporting. The data were collected and compared.

Results

There was no statistically significant difference noted in the diagnostic accuracy obtained while using the RSNA recommendations and the CO-RADS reporting system. Also, a good inter-rater agreement was noticed while using the two reporting systems. The CT showed a highly significant value while used in the assessment of symptomatic patients in controversy to the screening of asymptomatic patients.

Conclusion

Both reporting systems show similar diagnostic accuracy with a good almost similar inter-rater agreement. Both can be used while interpreting the CT images of cases with suspected COVID-19 infection. CT can be used effectively in the detection of COVID-19 infection between symptomatic patients while it is of a lower value in the screening of asymptomatic patients.

Comparative Analysis of In-House RT-qPCR Detection of SARS-CoV-2 for Resource-Constrained Settings

We developed and standardized an efficient and cost-effective in-house RT-PCR method to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated sensitivity, specificity, and other statistical parameters by different RT-qPCR methods including triplex, duplex, and simplex assays adapted from the initial World Health Organization- (WHO) recommended protocol. This protocol included the identification of the E envelope gene (E gene; specific to the Sarvecovirus genus), RdRp gene of the RNA-dependent RNA polymerase (specific for SARS-CoV-2), and RNase P gene as endogenous control. The detection limit of the E and the RdRp genes were 3.8 copies and 33.8 copies per 1 µL of RNA, respectively, in both triplex and duplex reactions. The sensitivity for the RdRp gene in the triplex and duplex RT-qPCR tests were 98.3% and 83.1%, respectively. We showed a decrease in sensitivity for the RdRp gene by 60% when the E gene acquired Ct values > 31 in the diagnostic tests. This is associated with the specific detection limit of each gene and possible interferences in the protocol. Hence, developing efficient and cost-effective methodologies that can be adapted to various health emergency scenarios is important, especially in developing countries or settings where resources are limited.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection

Background

SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility.

Methods

This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy.

Results

Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials.

Conclusion

Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Identification of mutations in SARS-CoV-2 PCR primer regions

Due to the constantly increasing number of mutations in the SARS-CoV-2 genome, concerns have emerged over the possibility of decreased diagnostic accuracy of reverse transcription-polymerase chain reaction (RT-PCR), the gold standard diagnostic test for SARS-CoV-2. We propose an analysis pipeline to discover genomic variations overlapping the target regions of commonly used PCR primer sets. We provide the list of these mutations in a publicly available format based on a dataset of more than 1.2 million SARS-CoV-2 samples. Our approach distinguishes among mutations possibly having a damaging impact on PCR efficiency and ones anticipated to be neutral in this sense. Samples are categorized as "prone to misclassification" vs. "likely to be correctly detected" by a given PCR primer set based on the estimated effect of mutations present. Samples susceptible to misclassification are generally present at a daily rate of 2% or lower, although particular primer sets seem to have compromised performance when detecting Omicron samples. As different variant strains may temporarily gain dominance in the worldwide SARS-CoV-2 viral population, the efficiency of a particular PCR primer set may change over time, therefore constant monitoring of variations in primer target regions is highly recommended.

Urine Metabolites Enable Fast Detection of COVID-19 Using Mass Spectrometry

The COVID-19 pandemic boosted the development of diagnostic tests to meet patient needs and provide accurate, sensitive, and fast disease detection. Despite rapid advancements, limitations related to turnaround time, varying performance metrics due to different sampling sites, illness duration, co-infections, and the need for particular reagents still exist. As an alternative diagnostic test, we present urine analysis through flow-injection-tandem mass spectrometry (FIA-MS/MS) as a powerful approach for COVID-19 diagnosis, targeting the detection of amino acids and acylcarnitines. We adapted a method that is widely used for newborn screening tests on dried blood for urine samples in order to detect metabolites related to COVID-19 infection. We analyzed samples from 246 volunteers with diagnostic confirmation via PCR. Urine samples were self-collected, diluted, and analyzed with a run time of 4 min. A Lasso statistical classifier was built using 75/25% data for training/validation sets and achieved high diagnostic performances: 97/90% sensitivity, 95/100% specificity, and 95/97.2% accuracy. Additionally, we predicted on two withheld sets composed of suspected hospitalized/symptomatic COVID-19-PCR negative patients and patients out of the optimal time-frame collection for PCR diagnosis, with promising results. Altogether, we show that the benchmarked FIA-MS/MS method is promising for COVID-19 screening and diagnosis, and is also potentially useful after the peak viral load has passed.

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images

According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and effective deep learning frameworks to tackle this disease. However, poor feature extraction from the Chest X-ray images and the high computational cost of the available models impose difficulties to an accurate and fast Covid-19 detection framework. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier research. To achieve the specified goal, we explored the Inception V3 deep artificial neural network. This study proposed LCSB-Inception; a two-path (L and AB channel) Inception V3 network along the first three convolutional layers. The RGB input image is first transformed to CIE LAB coordinates (L channel which is aimed at learning the textural and edge features of the Chest X-Ray and AB channel which is aimed at learning the color variations of the Chest X-ray images). The L achromatic channel and the AB channels filters are set to 50%L-50%AB. This method saves between one-third and one-half of the parameters in the divided branches. We further introduced a global second-order pooling at the last two convolutional blocks for more robust image feature extraction against the conventional max-pooling. The detection accuracy of the LCSB-Inception is further improved by employing the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement technique on the input image before feeding them to the network. The proposed LCSB-Inception network is experimented on using two loss functions (Categorically smooth loss and categorically Cross-entropy) and two learning rates whereas Accuracy, Precision, Sensitivity, Specificity F1-Score, and AUC Score were used for evaluation via the chestX-ray-15k (Data_1) and COVID-19 Radiography dataset (Data_2). The proposed models produced an acceptable outcome with an accuracy of 0.97867 (Data_1) and 0.98199 (Data_2) according to the experimental findings. In terms of COVID-19 identification, the suggested models outperform conventional deep learning models and other state-of-the-art techniques presented in the literature based on the results.

RT-LAMP in SARS-CoV-2 detection: point to improve primer designing and decrease molecular diagnosis pitfalls

BACKGROUND

Due to the high transmission rate of SARS-CoV-2, diagnostic tests have become tools for identifying patients. The key points were the virus genomes survey to design RT-LAMP primers; comparing the sensitivity and specificity of RT-LAMP and RT-qPCR; and determining the relationship among clinical symptoms, CT scan, RT-qPCR, and RT-LAMP results.

METHODS

This cohort study included 444 symptomatic patients. The specificity and sensitivity of RT-LAMP were assayed. The five statistical models, simultaneously, by RapidMiner to find the best method for detecting the virus were done through the correlation between the clinical symptoms, RT-LAMP, RT-qPCR, and CT scan results. The chi-square test by SPSS 26.0 was used to calculate kappa agreement.

RESULTS

The virus genome was detected in all the positive samples (198) by RT-qPCR and RT-LAMP. In addition, 246 samples were negative by RT-qPCR, while 88 were positive by RT-LAMP. Data mining analysis indicated that there were most associations between the RT-LAMP and CT scan data compared to RT-qPCR and CT scan data.

CONCLUSIONS

RT-LAMP could detect SARS-CoV-2 with great simplicity, speed, and cheapness. Therefore, it is logical to screen, a large number of patients by RT-LAMP, and then RT-qPCR can be used on the limited samples.

Indirect Virus Transmission via Fomites Can Counteract Lock-Down Effectiveness

The spread of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has raised major health policy questions. Direct transmission via respiratory droplets seems to be the dominant route of its transmission. However, indirect transmission via shared contact of contaminated objects may also occur. The contribution of each transmission route to epidemic spread might change during lock-down scenarios. Here, we simulate viral spread of an abstract epidemic considering both routes of transmission by use of a stochastic, agent-based SEIR model. We show that efficient contact tracing (CT) at a high level of incidence can stabilize daily cases independently of the transmission route long before effects of herd immunity become relevant. CT efficacy depends on the fraction of cases that do not show symptoms. Combining CT with lock-down scenarios that reduce agent mobility lowers the incidence for exclusive direct transmission scenarios and can even eradicate the epidemic. However, even for small fractions of indirect transmission, such lockdowns can impede CT efficacy and increase case numbers. These counterproductive effects can be reduced by applying measures that favor distancing over reduced mobility. In summary, we show that the efficacy of lock-downs depends on the transmission route. Our results point to the particular importance of hygiene measures during mobility lock-downs.

Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis

Background

Chest computerized tomography (CT) plays an important role in detecting patients with suspected coronavirus disease 2019 (COVID-19), however, there are no systematic summaries on whether the chest CT findings of patients within mainland China are applicable to those found in patients outside.

Methods

Relevant studies were retrieved comprehensively by searching PubMed, Embase, and Cochrane Library databases before 15 April 2022. Quality assessment of diagnostic accuracy studies (QUADAS) was used to evaluate the quality of the included studies, which were divided into two groups according to whether they were in mainland China or outside. Data on diagnostic performance, unilateral or bilateral lung involvement, and typical chest CT imaging appearances were extracted, and then, meta-analyses were performed with R software to compare the CT features of COVID-19 pneumonia between patients from within and outside mainland China.

Results

Of the 8,258 studies screened, 19 studies with 3,400 patients in mainland China and 14 studies with 554 outside mainland China were included. Overall, the risk of quality assessment and publication bias was low. The diagnostic value of chest CT is similar between patients from within and outside mainland China (93, 91%). The pooled incidence of unilateral lung involvement (15, 7%), the crazy-paving sign (31, 21%), mixed ground-glass opacities (GGO) and consolidations (51, 35%), air bronchogram (44, 25%), vascular engorgement (59, 33%), bronchial wall thickening (19, 12%), and septal thickening (39, 26%) in patients from mainland China were significantly higher than those from outside; however, the incidence rates of bilateral lung involvement (75, 84%), GGO (78, 87%), consolidations (45, 58%), nodules (12, 17%), and pleural effusion (9, 15%) were significantly lower.

Conclusion

Considering that the chest CT features of patients in mainland China may not reflect those of the patients abroad, radiologists and clinicians should be familiar with various CT presentations suggestive of COVID-19 in different regions.

An Exploration of Nanoparticle-Based Diagnostic Approaches for Coronaviruses: SARS-CoV-2, SARS-CoV and MERS-CoV

The wildfire-like spread of COVID-19, caused by severe acute respiratory syndrome-associated coronavirus-2, has resulted in a pandemic that has put unprecedented stress on the world's healthcare systems and caused varying severities of socio-economic damage. As there are no specific treatments to combat the virus, current approaches to overcome the crisis have mainly revolved around vaccination efforts, preventing human-to-human transmission through enforcement of lockdowns and repurposing of drugs. To efficiently facilitate the measures implemented by governments, rapid and accurate diagnosis of the disease is vital. Reverse-transcription polymerase chain reaction and computed tomography have been the standard procedures to diagnose and evaluate COVID-19. However, disadvantages, including the necessity of specialized equipment and trained personnel, the high financial cost of operation and the emergence of false negatives, have hindered their application in high-demand and resource-limited sites. Nanoparticle-based methods of diagnosis have been previously reported to provide precise results within short periods of time. Such methods have been studied in previous outbreaks of coronaviruses, including severe acute respiratory syndrome-associated coronavirus and middle east respiratory syndrome coronavirus. Given the need for rapid diagnostic techniques, this review discusses nanoparticle use in detecting the aforementioned coronaviruses and the recent severe acute respiratory syndrome-associated coronavirus-2 to highlight approaches that could potentially be used during the COVID-19 pandemic.

Development and characterisation of cysteine-based gold electrodes for the electrochemical biosensing of the SARS-CoV-2 spike antigen

This article describes three novel electrochemical biosensing platforms developed to determine the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) spike antigen protein: glutaraldehyde, SARS-CoV-2 spike antibody and bovine serum albumin; N,N-dicyclohexyl carbodiimide/4-(dimethylamino)pyridine functionalised SARS-CoV-2 spike antibody and bovine serum albumin; and 1-ethyl-3-[3-dimethylaminopropyl]-carbodiimide hydrochloride/N-hydroxysuccinimide functionalised SARS-CoV-2 spike antibody and bovine serum albumin modified cysteine-based gold-flower modified glassy carbon electrodes. Two of the produced biosensors having better signals were used to determine the SARS-CoV-2 spike antigen in spiked-saliva and clinical samples containing gargle and mouthwash liquids and characterised using cyclic voltammetry, scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. The study provides highly significant information in terms of how coupling reagents ought to be used with linkers consisting of both amine and carboxylic acid terminals (i.e. cysteine). The electrochemical cathodic signals based on antibody-antigen protein interactions at approximately -270 mV were evaluated as a response using square wave voltammetry, and they increased in proportion to the SARS-CoV-2 spike antigen. The limit of detection values were 0.93 and 46.3 ag mL^-1 in a linear range from 1 ag mL^-1 to 100 pg mL^-1 and from 100 ag mL^-1 to 10 ng mL^-1 and the recovery and relative standard deviation values for spiked-saliva samples were 99.50% and 99.40%, and 3.87% and 0.13% for BSA/S-AB/GluAl/Cys/Au/GCE and BSA/S-AB/f-Cys/Au/GCE, respectively. The results showed that both biosensing platforms could be selectively and accurately used to diagnose COVID-19 in RT-PCR-approved clinical samples.

Correlation between chest CT and RT-PCR testing in India's second COVID-19 wave: a retrospective cohort study

OBJECTIVES

To assess the diagnostic accuracy of chest CT in clinically suspected patients with COVID-19 using reverse transcriptase PCR (RT-PCR) as the reference standard and establish the correlation between CT Severity Score (CTSS) and RT-PCR results.

DESIGN AND SETTING

Retrospective cohort study. Single-centre tertiary care hospital-based study.

PARTICIPANTS

We enrolled 112 clinically suspected patients with COVID-19 between 1 April 2021 and 31 May 2021. Chest CT and RT-PCR tests were performed for all patients at a time interval of no longer than 7 days between the two tests. Patients with prior chronic respiratory illnesses were excluded. The diagnostic performance of chest CT was evaluated using RT-PCR as the reference standard. The CTSS was calculated for all patients with positive chest CT findings, and it was correlated with results of the RT-PCR assay.

MAIN OUTCOME MEASURES

The primary outcome measures were determination of the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of chest CT using RT-PCR as the standard of reference. The correlation between CTSS and RT-PCR was the secondary outcome.

RESULTS

85/112 (76%) patients tested positive on the RT-PCR whereas 91/112 (81%) had chest CT findings typical of SARS-CoV-2 infection. Chest CT had a sensitivity of 90.6% (95% CI 82.3% to 95.8%), a specificity of 48.1% (95% CI 28.7% to 68.0%), a PPV of 84.6% (95% CI 79.2% to 88.8%), an NPV of 61.9% (95% CI 43.0% to 77.8%) and an accuracy of 80.4% (95% CI 71.8% to 87.3%). There was a significant correlation between the CTSS and RT-PCR positivity (p value=0.003).

CONCLUSION

In our experience, chest CT has a good sensitivity and provides a reliable diagnostic tool for moderate-to-severe COVID-19 cases in resource limited settings.

A Comparative Study of Voltammetric vs Impedimetric Immunosensor for Rapid SARS-CoV-2 Detection at the Point-of-care

Here, a novel biosensing platform for the detection of SARS-CoV-2 usable both at voltammetric and impedimetric mode is reported. The platform was constructed on a multi-walled carbon nanotubes (MWCNTs) screen-printed electrode (SPE) functionalized by methylene blue (MB), antibodies against SARS-CoV-2 spike protein (SP), a bioactive layer of chitosan (CS) and protein A (PrA). The voltammetric sensor showed superior performances both in phosphate buffer solution (PBS) and spiked-saliva samples, with LOD values of 5.0±0.1 and 30±2.1 ng/mL, compared to 20±1.8 and 50±2.5 ng/mL for the impedimetric sensor. Moreover, the voltammetric immunosensor was tested in real saliva, showing promising results.

The Agreement Between Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and Rapid Antigen Test (RAT) in Diagnosing COVID-19

Background

False-negative results derived from RT-PCR tests for diagnosing coronavirus disease (COVID-19) have raised questions about whether to consider them the gold standard for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using an imperfect gold standard to assess other diagnostic tests would never let the other tests show better diagnostic performance. The best strategy in such cases is to do an agreement analysis, and this study aims to estimate the agreement between real-time reverse transcriptase-polymerase chain reaction (RT-PCR) and rapid antigen test (RAT) for COVID-19 detection.

Methods

A retrospective study was done using paired data of individuals tested for COVID-19, both by RT-PCR and RAT, obtained from the virology laboratory of Government Bundelkhand Medical College, Sagar, Madhya Pradesh, India. A sample size of 93 was calculated, and the data were abstracted in a data abstraction sheet. Variables included were results of RT-PCR and RAT, age, gender, presence of symptoms, test kit used, and the time duration between sampling for RT-PCR and RAT. Apart from descriptive statistics, keeping in mind the binary outcome of RT-PCR and RAT, Cohen’s kappa was calculated for agreement analysis. A p-value of <0.05 was considered significant.

Results

The data on 100 participants suspected to be infected with COVID-19 (58 male and 42 female) with a mean age of 39.8 (±19.0) years were analysed. The number of discordant pairs was eight. Cohen’s kappa showed substantial agreement between RT-PCR and RAT, κ=0.646, (95% CI 0.420 to 0.871), p<0.001.

Conclusion

Considering the ease of conducting RAT with quick results and substantial agreement with RT-PCR, RAT could be a better choice in detecting SARS-CoV-2 and, hence, COVID-19 disease on a large scale.

Discriminative electrochemical biosensing of wildtype and omicron variant of SARS-CoV-2 nucleocapsid protein with single platform

Electrochemical biosensors for determining wildtype and omicron variant of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) nucleocapsid antigen in nasopharyngeal swab samples were produced by using functionalised graphene oxide and the wildtype and omicron types of SARS-CoV-2 nucleocapsid antibody modified glassy carbon electrodes. The developed biosensors characterised by cyclic voltammetry, scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy were able to detect 0.76 and 0.24 ag/mL of the wildtype and omicron SARS-CoV-2 nucleocapsid antigen protein in linear ranges varied from 1 ag/mL to 100 fg/mL and from 1 ag/mL to 10 fg/mL, respectively. The performance of both biosensors produced was compared in nasopharyngeal swab samples containing the wildtype and omicron variant of the SARS-CoV-2, and it was evaluated whether they could be used interchangeably.

Predictors of depressive symptoms among hospitalized COVID-19 patients with respiratory problems

COVID-19 patients and survivors quite often experience depressive symptoms, which can increase risk for lower immune system response and poorer recovery. Vulnerability to depressive symptoms may be elevated in those patients who have the most severe COVID-19 course of illness, that is, patients who require supplementary oxygen therapy or even intubation. The current study involved a unique sample of patients who were hospitalized due to COVID-19 and who required respiratory support (N = 34, 10 women) in which we investigated depressive symptoms as well as psychopathological personality traits (PID5) as predictors. The majority of patients (76.5%) presented some degree of depressive symptoms. Although we expected severe levels of depressive symptoms to be most prevalent, more patients showed rather moderate levels. At the same time, Negative Affectivity was most predictive of depressive symptoms. We suggest that medical care for patients with greater emotional sensitivity and vulnerability to stress be supplemented with psychological support in order to address depressive symptoms and foster recovery.

CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images

The Coronavirus (COVID-19) pandemic has created havoc on humanity by causing millions of deaths and adverse physical and mental health effects. To prepare humankind for the fast and efficient detection of the virus and its variants shortly, COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. To detect COVID-19, there are numerous publicly accessible datasets of Chest X-rays that the researchers have combined to solve the problem of inadequate data. The cause for concern here is that in combining two or more datasets, some of the images might be duplicates, so a curated dataset has been used in this study, taken from an author’s paper. This dataset consists of 1281 COVID-19, 3270 Normal X-rays, and 1656 viral-pneumonia infected Chest X-ray images. Dataset has been pre-processed and divided carefully to ensure that there are no duplicate images. A comparative study on many traditional pre-trained models was performed, analyzing top-performing models. Fine-tuned InceptionV3, Modified EfficientNet B0&B1 produced an accuracy of 99.78% on binary classification, i.e., covid-19 infected and normal Chest X-ray image. ResNetV2 had a classification accuracy of 97.90% for 3-class classification i.e., covid-19 infected, normal, and pneumonia. Furthermore, a trailblazing custom CNN-based model, CoviXNet, has been proposed consisting of 15 layers that take efficiency into account. The proposed model CoviXNet exhibited a 10-fold accuracy of 99.47% on binary classification and 96.61% on 3-class. CoviXNet has shown phenomenal performance with exceptional accuracy and minimum computational cost. We anticipate that this comparative study, along with the proposed model CoviXNet, can assist medical centers with the efficient real-life detection of Coronavirus.

Update on the limited sensitivity of computed tomography relative to RT-PCR for COVID-19: a systematic review

Purpose

The global and ongoing COVID-19 outbreak has compelled the need for timely and reliable methods of detection for SARS-CoV-2 infection. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely accepted as a reference standard for COVID-19 diagnosis, several early studies have suggested the superior sensitivity of computed tomography (CT) in identifying SARS-CoV-2 infection. In a previous systematic review, we stratified studies based on risk for bias to evaluate the true sensitivity of CT for detecting SARS-CoV-2 infection. This study revisits our prior analysis, incorporating more current data to assess the sensitivity of CT for COVID-19.

Material and methods

The PubMed and Google Scholar databases were searched for relevant articles published between 1 January 2020, and 25 April 2021. Exclusion criteria included lack of specification regarding whether the study cohort was adult or paediatric, whether patients were symptomatic or asymptomatic, and not identifying the source of RT-PCR specimens. Ultimately, 62 studies were included for systematic review and were subsequently stratified by risk for bias using the QUADAS-2 quality assessment tool. Sensitivity data were extracted for random effects meta-analyses.

Results

The average sensitivity for COVID-19 reported by the high-risk-of-bias studies was 68% [CI: 58, 80; range: 38-96%] for RT-PCR and 91% [CI: 87, 96; range: 47-100%] for CT. The average sensitivity reported by the low-risk-of-bias studies was 84% [CI: 0.75, 0.94; range: 70-97%] for RT-PCR and 78% [CI: 71, 0.86; range: 44-92%] for CT.

Conclusions

On average, the high-risk-of bias studies underestimated the sensitivity of RT-PCR and overestimated the sensitivity of CT for COVID-19. Given the incorporation of recently published low-risk-of-bias articles, the sensitivities according to low-risk-of-bias studies for both RT-PCR and CT were higher than previously reported.

Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images

COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. Deep Neural Networks with hundreds or even millions of parameters (weights) are referred to as ”black boxes” because their behavior is difficult to comprehend, even when the model’s structure and weights are visible. On the same dataset, different Deep Convolutional Neural Networks perform differently. So, we do not necessarily have to rely on just one model; instead, we can evaluate our final score by combining multiple models. While including multiple models in the voter pool, it is not always true that the accuracy will improve. So, In this regard, the authors proposed a novel approach to determine the voting ensemble score of individual classifiers based on Condorcet’s Jury Theorem (CJT). The authors demonstrated that the theorem holds while ensembling the N number of classifiers in Neural Networks. With the help of CJT, the authors proved that a model’s presence in the voter pool would improve the likelihood that the majority vote will be accurate if it is more accurate than the other models. Besides this, the authors also proposed a Domain Extended Transfer Learning (DETL) ensemble model as a soft voting ensemble method and compared it with CJT based ensemble method. Furthermore, as deep learning models typically fail in real-world testing, a novel dataset has been used with no duplicate images. Duplicates in the dataset are quite problematic since they might affect the training process. Therefore, having a dataset devoid of duplicate images is considered to prevent data leakage problems that might impede the thorough assessment of the trained models. The authors also employed an algorithm for faster training to save computational efforts. Our proposed method and experimental results outperformed the state-of-the-art with the DETL-based ensemble model showing an accuracy of 97.26%, COVID-19, sensitivity of 98.37%, and specificity of 100%. CJT-based ensemble model showed an accuracy of 98.22%, COVID-19, sensitivity of 98.37%, and specificity of 99.79%.

Diagnostic accuracy of CRISPR technology for detecting SARS-CoV-2: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of CRISPR-Cas technology for SARS-CoV-2.

METHODS

In our study, RT-qPCR is defined as the reference standard. Data was collected independently and assessed by Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. A bivariate model for pooling was employed to estimates of sensitivity and specificity and subgroups analysis was used to explore heterogeneity.

RESULTS

2264 samples and 6 countries from 28 articles were extracted for evaluating the accuracy of CRISPR technology for diagnosing SARS-CoV-2. The overall pooled sensitivity and specificity of CRISPR technology were 0.98 (95% CI: 0.95-0.99) and 1.0 (95% CI: 0.98-1.00), respectively. As for literature quality assessment, high risks in patient selection bias and unclear risk of index test bias may affect accuracy. Subgroup analysis draws significant conclusions. CRISPR-Cas12 is more applicable for molecular diagnostics for its active editing characteristics. RT-LAMP and RT-RPA are usually used for pre-amplification and combined with fluorescence detection to output results quantitatively. Nasopharyngeal swabs and dual-genes perform greatly in our study.

CONCLUSION

The results concluded from all studies showed that CRISPR technology is a promising and accurate molecular method for detecting SARS-CoV-2. Standard methods including comparable sample material, patient selection, operating procedure and operators should be established.

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Background

Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood.

Objective

This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code.

Methods

A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included.

Results

This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies.

Conclusions

Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.

Detection of SARS-CoV-2 Genome in Stool and Plasma Samples of Laboratory Confirmed Iranian COVID-19 Patients

Coronavirus disease 2019 (COVID19), caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV2), was first discovered in China in late 2019 and quickly spread worldwide. Although nasopharyngeal swab sampling is still the most popular approach identify SARS-CoV-2 carriers, other body samples may reveal the virus genome, indicating the potential for virus transmission via non-respiratory samples. In this study, researchers looked at the presence and degree of SARS-CoV-2 genome in stool and plasma samples from 191 Iranian COVID-19 patients, and looked for a link between these results and the severity of their disease. SARS-CoV-2 RNA shedding in feces and plasma of COVID-19 patients was assessed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Medical data were collected and evaluated, including Clinical features, demographics, radiological, and laboratory findings of the patients. Plasma samples from 117 confirmed laboratory patients were evaluated and 24 out of 117 patients (20.51%) tested positive for SARS-COV-2 RNA. Besides, 20 out of 74 patients (27.03%) tested positive for SARS-COV-2 RNA in stool samples. There seems to be no relationship between the presence of SARS-CoV-2 genome in fecal and plasma samples of Covid-19 patients and the severity of illness. We provide evidence of the SARS-CoV-2 genome presence in stool and plasma samples of Iranian COVID-19 patients.

Factors associated with mortality, length of hospital stay and diagnosis of COVID-19: Data from a field hospital

Background

During the pandemic of COVID-19, phylogenetic changes have been observed in the characteristics of the virus, in the diagnosis and treatment of the disease. The clinical course and the severe form of the disease depends on several factors. This study characterized the beginning setting for patient care of COVID-19 in a referral center in one of the main capital cities of Brazil. In addition, were evaluated the factors associated with mortality, length of stay, and diagnostic outcome.

Methods

A cross-sectional study was conducted during May 2020 (n = 1100). The association of the variables with outcome was evaluated by a multivariable logistic regression model, using odds ratios (OR) and 95 % confidence intervals (CI).

Results

Overall, 76 % of patients were COVID-19 positive, and 70 % were diagnosed by RT–qPCR. The majority were male (56 %), and over 52 years old (74 %), 68 % had hypertension, 44 % had diabetes mellitus, and 32 % were obese. The mean length of stay was 10 ± 8 days, which was higher in the 34 % who died (≥14; OR=2; 95 %CI=1.4–4) and who had hypertension (OR=2; 95 %CI=1.3–3) (P < 0.001). The mean length of stay was also higher (P = 0.008) for those patients with pulmonary impairment ≥ 50 % (10.72 ± 8.24), than those with< 50 % (8.98 ± 6.81). Age (>62 and 65 years) was associated with longer hospitalization (OR=2; 95 %CI=1.4–3) and death (OR=6; 95 %CI=3–11). The time of sample collection for RT–qPCR was different between positive and negative tests (P = 0.001), with the time of 4–10 days showing a greater chance for virus detection (OR=2.9; 95 %CI=1.6–5).

Conclusion

Death was associated with age and pulmonary impairment. The length of hospitalization was associated with age, hypertension, pulmonary impairment and death. The time of sample collection to perform RT–qPCR and the rapid test was associated with a positive result for COVID-19. These results highlight the ongoing challenge of diagnosing, treating, and mitigating the effects caused by the COVID-19 pandemic.