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Subclinical Myocardial Dysfunction in Patients with Persistent Dyspnea One Year after COVID-19 - Why Should Screening for Cardiovascular Diseases Be Performed? The presence of persistent dyspnea was observed in one-third of the recovered COVID-19 patients, in line with the current data on the long-term evolution of COVID-19 [[3]]. A total of 66 COVID-19 patients without a previous history of cardiorespiratory diseases were evaluated at 12 months for potential subclinical cardiac dysfunction following the acute infection [[2]]. [Extracted from the article]

However, the GCW and GWI values of the two groups, both with and without dyspnea, do not deviate from the normal reference values, which are based on the NORRE study [[2]] and that were underpinned by the authors. The study reports the following echocardiographic differences: Compared to asymptomatic patients, patients with dyspnea presented lower LV constructive work (GCW): 2183.7 ± 487.9 vs. 2483.1 ± 422.4, I p i = 0.024; and a lower global work index (GWI): 1960.0 ± 396.2 vs. 2221.1 ± 407.9, I p i = 0.030. This study compares 23 patients with self-reported dyspnea and 43 without 1 year after hospital admission for COVID-19. [Extracted from the article]

Severe acute respiratory syndrome (SARS-CoV-2), the causative agent of the global pandemic, which has resulted in more than one million deaths with tens of millions reported cases, requires a fast, accurate, and portable testing mechanism operable in the field environment. Electrochemical sensors, based on paper substrates with portable electrochemical devices, can prove an excellent alternative in mitigating the economic and public health effects of the disease. Herein, we present an impedance biosensor for the detection of the SARS-CoV-2 spike protein utilizing the IgG anti-SARS-CoV-2 spike antibody. This label-free platform utilizing screen-printed electrodes works on the principle of redox reaction impedance of a probe and can detect antigen spikes directly in nasopharyngeal fluid as well as virus samples collected in the universal transport medium (UTM). High conductivity graphene/carbon ink is used for this purpose so as to have a small background impedance that leads to a wider dynamic range of detection. Antibody immobilization onto the electrode surface was conducted through a chemical entity or a biological entity to see their effect; where a biological immobilization can enhance the antibody loading and thereby the sensitivity. In both cases, we were able to have a very low limit of quantification (i.e., 0.25 fg/mL), however, the linear range was 3 orders of magnitude wider for the biological entity-based immobilization. The specificity of the sensor was also tested against high concentrations of H1N1 flu antigens with no appreciable response. The most optimized sensors are used to identify negative and positive COVID-19 samples with great accuracy and precision. [ABSTRACT FROM AUTHOR]

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

Given the pronounced impact COVID-19 continues to have on society—infecting 700 million reported individuals and causing 6.96 million deaths—many deep learning works have recently focused on the virus's diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices. [ABSTRACT FROM AUTHOR]

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

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset's unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system's improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings. [ABSTRACT FROM AUTHOR]

X-ray diagnostics are widely used to detect various diseases, such as bone fracture, pneumonia, or intracranial hemorrhage. This method is simple and accessible in most hospitals, but requires an expert who is sometimes unavailable. Today, some diagnoses are made with the help of deep learning algorithms based on Convolutional Neural Networks (CNN), but these algorithms show limitations. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used to detect whether a chest X-ray image has disease (COVID or pneumonia) or is healthy. An improved model called DRCaps is proposed, which combines the advantage of CapsNet and the dilation rate (dr) parameter to manage images with 226 × 226 resolution. We performed experiments with 16,669 chest images, in which our model achieved an accuracy of 90%. Furthermore, the model size is 11M with a reconstruction stage, which helps to avoid overfitting. Experiments show how the reconstruction stage works and how we can avoid the max-pooling operation for networks with a stride and dilation rate to downsampling the convolution layers. In this paper, DRCaps is superior to other comparable models in terms of accuracy, parameters, and image size handling. The main idea is to keep the model as simple as possible without using data augmentation or a complex preprocessing stage. [ABSTRACT FROM AUTHOR]

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach's alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks. [ABSTRACT FROM AUTHOR]

It has been validated beyond doubt that High-Resolution Computed Tomography (HRCT) chest and to some extent chest radiographs have a role in corona virus disease-19 (COVID-19). Much less is known about the role of lung ultrasonography (LUS) in COVID-19. In this paper, our main purpose was to gauge the relationship between LUS and chest HRCT in reverse transcriptase polymerase chain reaction (RT–PCR) documented cases of COVID-19, as well as in those with high suspicion of COVID-19 with negative RT–PCR. It was a prospective study carried out at our tertiary care hospital, namely, SKIMS Soura. The total number of patients in this study were 152 (200 patients were selected out of which only 152 had undergone both LUS and chest HRCT). The patients were subjected to both LUS and chest HRCT. The radiologist who performed LUS was blinded to clinical findings and HRCT was evaluated by a radiologist with about a decade of experience. The LUS findings compatible with the disease were subpleural consolidations, B-lines and irregular pleural lines. Findings that were compatible with COVID-19 on chest HRCT were bibasilar, subpleural predominant ground glass opacities, crazy paving and consolidations. COVID-19-positive patients were taken up for chest HRCT for disease severity stratification and were also subjected to LUS. On HRCT chest, the imaging abnormalities compatible with COVID-19 were evident in 110 individuals (72.37%), and on Lung Ultrasound they were observed in 120 individuals (78.95%). Imaging of COVID-19 patients assessed by both LUS and HRCT chest,, showed a positive correlation (p < 0.0001). The study revealed a sensitivity of 88%, a specificity of 76.62%, a positive predictive value of 78.57% and a negative predictive value of 86.76%. None of the individuals with a diagnosis of COVID-19 on HRCT were missed on LUS. An excellent correlation was derived between the LUS score and CT total severity score (p < 0.0001 with a kappa of 0.431). Similar precision compared with chest HRCT in the detection of chest flaws in COVID-19 patients was obtained on LUS. [ABSTRACT FROM AUTHOR]

As the number of reports of post-acute COVID-19 musculoskeletal manifestations is rapidly rising, it is important to summarize the current available literature in order to shed light on this new and not fully understood phenomenon. Therefore, we conducted a systematic review to provide an updated picture of post-acute COVID-19 musculoskeletal manifestations of potential rheumatological interest, with a particular focus on joint pain, new onset of rheumatic musculoskeletal diseases and presence of autoantibodies related to inflammatory arthritis such as rheumatoid factor and anti-citrullinated protein antibodies. We included 54 original papers in our systematic review. The prevalence of arthralgia was found to range from 2% to 65% within a time frame varying from 4 weeks to 12 months after acute SARS-CoV-2 infection. Inflammatory arthritis was also reported with various clinical phenotypes such as symmetrical polyarthritis with RA-like pattern similar to other prototypical viral arthritis, polymyalgia-like symptoms, or acute monoarthritis and oligoarthritis of large joints resembling reactive arthritis. Moreover, high figures of post-COVID-19 patients fulfilling the classification criteria for fibromyalgia were found, ranging from 31% to 40%. Finally, the available literature about prevalence of rheumatoid factor and anti-citrullinated protein antibodies was largely inconsistent. In conclusion, manifestations of rheumatological interest such as joint pain, new-onset inflammatory arthritis and fibromyalgia are frequently reported after COVID-19, highlighting the potential role of SARS-CoV-2 as a trigger for the development of autoimmune conditions and rheumatic musculoskeletal diseases. [ABSTRACT FROM AUTHOR]

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

The disaster of the COVID-19 pandemic has claimed numerous lives and wreaked havoc on the entire world due to its transmissible nature. One of the complications of COVID-19 is pneumonia. Different radiography methods, particularly computed tomography (CT), have shown outstanding performance in effectively diagnosing pneumonia. In this paper, we propose a spatial attention and attention gate UNet model (SAA-UNet) inspired by spatial attention UNet (SA-UNet) and attention UNet (Att-UNet) to deal with the problem of infection segmentation in the lungs. The proposed method was applied to the MedSeg, Radiopaedia 9P, combination of MedSeg and Radiopaedia 9P, and Zenodo 20P datasets. The proposed method showed good infection segmentation results (two classes: infection and background) with an average Dice similarity coefficient of 0.85, 0.94, 0.91, and 0.93 and a mean intersection over union (IOU) of 0.78, 0.90, 0.86, and 0.87, respectively, on the four datasets mentioned above. Moreover, it also performed well in multi-class segmentation with average Dice similarity coefficients of 0.693, 0.89, 0.87, and 0.93 and IOU scores of 0.68, 0.87, 0.78, and 0.89 on the four datasets, respectively. Classification accuracies of more than 97% were achieved for all four datasets. The F1-scores for the MedSeg, Radiopaedia P9, combination of MedSeg and Radiopaedia P9, and Zenodo 20P datasets were 0.865, 0.943, 0.917, and 0.926, respectively, for the binary classification. For multi-class classification, accuracies of more than 96% were achieved on all four datasets. The experimental results showed that the framework proposed can effectively and efficiently segment COVID-19 infection on CT images with different contrast and utilize this to aid in diagnosing and treating pneumonia caused by COVID-19. [ABSTRACT FROM AUTHOR]

Intraocular foreign body injuries (IOFB) can lead to a number of intraocular pathologies; the visual results depend on the mechanism of the injury, the type of foreign body and the subsequent complications. The presence of intraocular cilia (eye lashes) following penetrating injury or surgical intervention is uncommon. In the present paper, we present a case of a 58-year-old woman with a history of eye trauma and a perforated corneal wound in the left eye that occurred 50 years ago. On the ophthalmological exam we noticed in the anterior chamber a straight linear extension, resembling cilia, extending behind the iris. The patient reports that it appeared during COVID-19 infection, after repeated episodes of coughing. After a follow-up period, we decided to remove the eyelash; 24 h after surgery, the patient complained of severe eye pain. Intraocular pressure (IOP) in LE was 54 mmHg. The slit-lamp examination showed perikeratic congestion, corneal edema and mydriasis. Eye hypotensive treatment was started immediately and the patient's general condition slightly improved. Intraocular cilia can be tolerated for many years without causing any ocular reaction. The decision for surgical intervention must be taken according to the individual needs of the patient and his ocular characteristics with careful pre- and post-operative follow up. [ABSTRACT FROM AUTHOR]

The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers. [ABSTRACT FROM AUTHOR]

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83 % accuracy and 93.39 % kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67 % accuracy and 86.67 % kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest. [ABSTRACT FROM AUTHOR]

This paper evaluates India's first officially approved self-administered rapid antigen test kit against COVID-19, a device called CoviSelf. The context is rural India. Rapid antigen tests (RATs) are currently popular in situations where vaccination rates are low, where sections of the community remain unvaccinated, where the COVID-19 pandemic continues to grow and where easy or timely access to RTPCR (reverse transcription-polymerase chain reaction) testing is not an option. Given that rural residents make up 66% of the Indian population, our evaluation focuses on the question of whether this self-administered RAT could help protect villagers and contain the Indian pandemic. CoviSelf has two components: the test and IT (information technology) parts. Using discourse analysis, a qualitative methodology, we evaluate the practicality of the kit on the basis of data in its instructional leaflet, reports about India's 'digital divide' and our published research on the constraints of daily life in Indian villages. This paper does not provide a scientific assessment of the effectiveness of CoviSelf in detecting infection. As social scientists, our contribution sits within the field of qualitative studies of medical and health problems. Self-administered RATs are cheap, quick and reasonably reliable. Hence, point-of-care testing at the doorsteps of villagers has much potential, but realising the benefits of innovative, diagnostic medical technologies requires a realistic understanding of the conditions in Indian villages and designing devices that work in rural situations. This paper forms part of a larger project regarding the COVID-19 pandemic in rural India. A follow-up study based on fieldwork is planned for 2022–2023. [ABSTRACT FROM AUTHOR]

Cellular immunity against SARS-CoV-2 is an important component of the immune response to the virus. At present, two such tests based on interferon-gamma release (interferon-γ release assays, IGRAs) are available—Quan-T-Cell SARS-CoV-2 by EUROIMMUN and T-SPOT.COVID by Oxford Immunotec. In this paper, we compared the results of these two tests in 90 subjects employed at the Public Health Institute Ostrava who had previously undergone COVID-19 infection or were vaccinated against that disease. To the best of our knowledge, this is the first head-to-head comparison of these two tests evaluating T-cell-mediated immunity against SARS-CoV-2. In addition, we also evaluated humoral immunity in the same individuals using the in-house virus neutralization test and IgG ELISA assay. The evaluation yielded similar results for both IGRAs, with Quan-T-Cell appearing to be insignificantly (p = 0.08) more sensitive (all 90 individuals were at least borderline positive) than T-SPOT.COVID (negative results found in five patients). The overall qualitative (presence/absence of immune response) agreement of both tests with virus neutralization test and anti-S IgG was also excellent (close or equal to 100% in all subgroups, with the exception of unvaccinated Omicron convalescents, a large proportion of whom, i.e., four out of six subjects, were IgG negative while at least borderline positive for T-cell-mediated immunity measured by Quan-T). This implies that the evaluation of T-cell-mediated immunity is a more sensitive indicator of immune response than the evaluation of IgG seropositivity. This is true at least for unvaccinated patients with a history of being infected only by the Omicron variant, but also likely for other groups of patients. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support. [ABSTRACT FROM AUTHOR]

In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual's health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets. [ABSTRACT FROM AUTHOR]

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

Chest pain and dyspnea are common symptoms in patients presenting to the emergency room (ER); oftentimes it is not possible to clearly identify the underlying cause, which may cause the patient to have to return to the ER. In other cases, while it is possible to identify the underlying cause, it is necessary to perform a large number of tests before being able to make a diagnosis. Over the last twenty years, emergency medicine physicians have had the possibility of using ultrasound to help them make and rule out diagnoses. Specific ultrasound tests have been designed to evaluate patients presenting with specific symptoms to ensure a fast, yet complete, evaluation. In this paper, we examine the role of ultrasound in helping physicians understand the etiology behind chest pain and dyspnea. We analyze the different diseases and disorders which may cause chest pain and dyspnea as symptoms and discuss the corresponding ultrasound findings. [ABSTRACT FROM AUTHOR]

The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku. [ABSTRACT FROM AUTHOR]

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico. [ABSTRACT FROM AUTHOR]

COVID-19 (COronaVIrus Disease 19) is an infectious disease also known as an acute respiratory syndrome caused by the SARS-CoV-2. Although in children and adolescents SARS-CoV-2 infection produces mostly mild or moderate symptoms, in a certain percentage of recovered young people a condition of malaise, defined as long-COVID-19, remains. To date, the risk factors for the development of long-COVID-19 are not completely elucidated. Neurotrophins such as NGF (Nerve Growth Factor) and BDNF (Brain-Derived Neurotrophic Factor) are known to regulate not only neuronal growth, survival and plasticity, but also to influence cardiovascular, immune, and endocrine systems in physiological and/or pathological conditions; to date only a few papers have discussed their potential role in COVID-19. In the present pilot study, we aimed to identify NGF and BDNF changes in the serum of a small cohort of male and female adolescents that contracted the infection during the second wave of the pandemic (between September and October 2020), notably in the absence of available vaccines. Blood withdrawal was carried out when the recruited adolescents tested negative for the SARS-CoV-2 ("post-infected COVID-19"), 30 to 35 days after the last molecular test. According to their COVID-19 related outcomes, the recruited individuals were divided into three groups: asymptomatics, acute symptomatics and symptomatics that over time developed long-COVID-19 symptoms ("future long-COVID-19"). As a control group, we analyzed the serum of age-matched healthy controls that did not contract the infection. Inflammatory biomarkers (TNF-α, TGF-β), MCP-1, IL-1α, IL-2, IL-6, IL-10, IL-12) were also analyzed with the free oxygen radicals' presence as an oxidative stress index. We showed that NGF serum content was lower in post-infected-COVID-19 individuals when compared to healthy controls; BDNF levels were found to be higher compared to healthy individuals only in post-infected-COVID-19 symptomatic and future long-COVID-19 girls, leaving the BDNF levels unchanged in asymptomatic individuals if compared to controls. Oxidative stress and inflammatory biomarkers were unchanged in male and female adolescents, except for TGF-β that, similarly to BDNF, was higher in post-infected-COVID-19 symptomatic and future long-COVID-19 girls. We predicted that NGF and/or BDNF could be used as early biomarkers of COVID-19 morbidity in adolescents. [ABSTRACT FROM AUTHOR]

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub. [ABSTRACT FROM AUTHOR]

Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user's home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease. [ABSTRACT FROM AUTHOR]

(1) Background: The current outbreak of COVID-19 infection is an ongoing challenge and a major threat to public health that requires surveillance, prompt diagnosis, as well as research efforts to understand the viral pathogenesis. Despite this, to date, very few studies have been performed concerning autoptic specimens. Therefore, this study aimed: (i) to reiterate the importance of the autoptic examination, the only method able to precisely define the cause of death; (ii) to provide a complete post-mortem histological and immunohistochemical investigation pattern capable of diagnosing death from COVID-19 infection. (2) Methods: In this paper, the lung examination of two subjects who died from COVID-19 are discussed, comparing the obtained data with those of the control, a newborn who died from pneumonia in the same pandemic period. (3) Results: The results of the present study suggest that COVID-19 infection can cause different forms of acute respiratory distress syndrome (ARDS), due to diffuse alveolar damage and diffuse endothelial damage. Nevertheless, different patterns of cellular and cytokine expression are associated with anti-COVID-19 antibody positivity, compared to the control case. Moreover, in both case studies, it is interesting to note that COVID-19, ACE2 and FVIII positivity was detected in the same fields. (4) Conclusions: COVID-19 infection has been initially classified as exclusively interstitial pneumonia with varying degrees of severity. Subsequently, vascular biomarkers showed that it can also be considered a vascular disease. The data on Factor VIII discussed in this paper, although preliminary and limited in number, seem to suggest that the thrombogenicity of Sars-CoV2 infection might be linked to widespread endothelial damage. In this way, it would be very important to investigate the pro-coagulative substrate both in all subjects who died and in COVID-19 survivors. This is because it may be hypothesized that the different patterns with which the pathology is expressed could depend on different individual susceptibility to infection or a different personal genetic-clinical background. In light of these findings, it would be important to perform more post-mortem investigations in order to clarify all aspects of the vascular hypothesis in the COVID-19 infection. [ABSTRACT FROM AUTHOR]

The COVID-19 disease, caused by infection with SARS-CoV-2, rapidly transformed into a pandemic following its emergence, and it continues to affect the population at a global level. This disease is associated with high mortality rates and mainly affects the pulmonary spectrum, with signs of interstitial pneumonia or other pathological modifications. Signs indicative of SARS-CoV-2 infection can be observed using pulmonary radiography or computed tomography scans and are closely linked to acute respiratory distress; however, there is accumulating evidence that the virus affects the central nervous system. Several symptoms, such as headaches, cough, fatigue, myalgia, ageusia, and anosmia, have also been reported along with neurological syndromes such as stroke, encephalopathy, Guillain–Barre syndrome, convulsions, and coma; the most frequent associated complication is ischemic stroke. Diagnosis of infection with SARS-CoV-2 virus is based on a positive RT-PCR test. Imaging investigations, such as thoracic computed tomography scans, are not used to diagnose COVID-19, monitor for pulmonary disease, or follow dynamic disease evolution, but they may be used in the case of a negative RT-PCR test. This paper presents the research performed on a group of 150 cases of patients affected by neurological disorders and that were subsequently confirmed to be infected with SARS-CoV-2, which was carried out over a period of 10 months within the Neurology Department and Laboratory of Magnetic Resonance Imaging of "Sf. Andrei" Emergency Hospital in Constanta. The collected data are observational and provide perspectives on the neurological pathology associated with the SARS-CoV-2 virus, and on the frequently associated risk factors, associated comorbidities, and the ages of patients who were affected by the virus, as well as the clinical and paraclinical manifestations of the patients admitted to the hospital's neurology department. [ABSTRACT FROM AUTHOR]

Pneumonia is a prevalent severe respiratory infection that affects the distal and alveoli airways. Across the globe, it is a serious public health issue that has caused high mortality rate of children below five years old and the aged citizens who must have had previous chronic-related ailment. Pneumonia can be caused by a wide range of microorganisms, including virus, fungus, bacteria, which varies greatly across the globe. The spread of the ailment has gained computer-aided diagnosis (CAD) attention. This paper presents a multi-channel-based image processing scheme to automatically extract features and identify pneumonia from chest X-ray images. The proposed approach intends to address the problem of low quality and identify pneumonia in CXR images. Three channels of CXR images, namely, the Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) CXR images are processed by deep neural networks. CXR-related features of LBP images are extracted using shallow CNN, features of the CLAHE CXR images are extracted by pre-trained inception-V3, whereas the features of CECED CXR images are extracted using pre-trained MobileNet-V3. The final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final identification result. The proposed network can accurately classify pneumonia according to the experimental result. The proposed method tested on publicly available dataset reports accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%. Compared with the single models and the state-of-the-art models, our proposed network achieves comparable performance. [ABSTRACT FROM AUTHOR]

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients. [ABSTRACT FROM AUTHOR]

Background: The SARS-CoV-2 infection has occurred in neonates, but it is a fact that radiation exposure is not recommended given their age. The aim of this review is to assess the evidence on the utility of lung ultrasound (LUS) in neonates diagnosed with COVID-19. Methods: A systematic literature review was performed so as to find a number of published studies assessing the benefits of lung ultrasound for newborns diagnosed with COVID and, in the end, to make a comparison between LUS and the other two more conventional procedures of chest X-rays or CT exam. The key terms used in the search of several databases were: "lung ultrasound", "sonography", "newborn", "neonate", and "COVID-19′. Results: In total, 447 studies were eligible for this review, and after removing the duplicates, 123 studies referring to LU were further examined, but only 7 included cases of neonates. These studies were considered for the present research paper. Conclusions: As a non-invasive, easy-to-use, and reliable method for lung lesion detection in neonates with COVID-19, lung ultrasound can be used as a useful diagnosis tool for the evaluation of COVID-19-associated lung lesions. The benefits of this method in this pandemic period are likely to arouse interest in opening new research horizons, with immediate practical applicability. [ABSTRACT FROM AUTHOR]

The new 'normal' defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses. [ABSTRACT FROM AUTHOR]

The novel coronavirus (nCoV-2019) is responsible for the acute respiratory disease in humans known as COVID-19. This infection was found in the Wuhan and Hubei provinces of China in the month of December 2019, after which it spread all over the world. By March, 2020, this epidemic had spread to about 117 countries and its different variants continue to disturb human life all over the world, causing great damage to the economy. Through this paper, we have attempted to identify and predict the novel coronavirus from influenza-A viral cases and healthy patients without infection through applying deep learning technology over patient pulmonary computed tomography (CT) images, as well as by the model that has been evaluated. The CT image data used under this method has been collected from various radiopedia data from online sources with a total of 548 CT images, of which 232 are from 12 patients infected with COVID-19, 186 from 17 patients with influenza A virus, and 130 are from 15 healthy candidates without infection. From the results of examination of the reference data determined from the point of view of CT imaging cases in general, the accuracy of the proposed model is 79.39%. Thus, this deep learning model will help in establishing early screening of COVID-19 patients and thus prove to be an analytically robust method for clinical experts. [ABSTRACT FROM AUTHOR]

Testing programs for COVID-19 depend on the voluntary actions of members of the public for their success. Understanding people's knowledge, attitudes, and behavior related to COVID-19 testing is, therefore, key to the design of effective testing programs worldwide. This paper reports on the findings of a rapid scoping review to map the extent, characteristics, and scope of social science research on COVID-19 testing and identifies key themes from the literature. Main findings include the discoveries that people are largely accepting of testing technologies and guidelines and that a sense of social solidarity is a key motivator of testing uptake. The main barriers to accessing and undertaking testing include uncertainty about eligibility and how to access tests, difficulty interpreting symptoms, logistical issues including transport to and from test sites and the discomfort of sample extraction, and concerns about the consequences of a positive result. The review found that existing research was limited in depth and scope. More research employing longitudinal and qualitative methods based in under-resourced settings and examining intersections between testing and experiences of social, political, and economic vulnerability is needed. Last, the findings of this review suggest that testing should be understood as a social process that is inseparable from processes of contact tracing and isolation and is embedded in people's everyday routines, livelihoods and relationships. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) can potentially affect all organs owing to the ubiquitous diffusion of the angiotensin-converting enzyme II (ACE2) receptor-binding protein. Indeed, the SARS-CoV-2 virus is capable of causing heart disease. This systematic review can offer a new perspective on the potential consequences of COVID-19 through an analysis of the current literature on cardiac involvement. This systematic review, conducted from March 2020 to July 2021, searched the current literature for postmortem findings in patients who were positive for SARS-CoV-2 by combining and meshing the terms "COVID-19", "postmortem", "autopsy", and "heart" in titles, abstracts, and keywords. The PubMed database was searched following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen papers met the inclusion criteria (case reports and series, original research, only English-written). A total of 209 patients were found (mean age (interquartile range (IQR)), 60.17 years (IQR, 54.75–70.75 years); 122 men (58.37%, ratio of men to women of 1:0.7%)). Each patient tested positive for SARS-CoV-2. Death was mainly the result of respiratory failure. The second most common cause of death was acute heart failure. Few patients specifically died of myocarditis. Variables such as pathological findings, immunohistochemical data, and previous clinical assessments were analyzed. Main cardiac pathological findings were cardiac dilatation, necrosis, lymphocytic infiltration of the myocardium, and small coronary vessel microthrombosis. Immunohistochemical analyses revealed an inflammatory state dominated by the constant presence of CD3+ and CD8+ cytotoxic lymphocytes and CD68+ macrophages. COVID-19 leads to a systemic inflammatory response and a constant prothrombotic state. The results of our systematic review suggest that SARS-CoV-2 was able to cause irreversible changes in several organs, including the heart; this is reflected by the increased cardiac risk in patients who survive COVID-19. Postmortem analysis (including autopsy, histologic, and immunohistochemical examination) is an indispensable tool to better understand pathological changes caused by emerging diseases such as COVID-19. Our results may provide more information on the involvement of the heart in COVID-19 patients. [ABSTRACT FROM AUTHOR]

Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls. The literature was studied and analyzed from diverse assets which include Scopus, Nature medicine, IEEE, Google scholar, Wiley Library, and PubMed. The search terms used were 'COVID-19', 'AI', 'diagnosis', and 'prognosis'. To strengthen the overall performance of AI in COVID-19 diagnosis and prognosis, we segregated several components to perceive threats and opportunities, as well as their inter-dependencies that affect the healthcare sector. This paper seeks to pick out the crucial fulfillment of factors for AI with inside the healthcare sector in the Indian context. Using critical literature review and experts' opinion, a total of 11 factors affecting COVID-19 diagnosis and prognosis were detected, and we eventually used an interpretive structural model (ISM) to build a framework of interrelationships among the identified factors. Finally, the matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis resulted the driving and dependence powers of these identified factors. Our analysis will help healthcare stakeholders to realize the requirements for successful implementation of AI. [ABSTRACT FROM AUTHOR]

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management. [ABSTRACT FROM AUTHOR]

The implementation and validation of anti-SARS-CoV-2 IgG serological assays are reported in this paper. S1 and RBD proteins were used to coat ELISA plates, and several secondary antibodies served as reporters. The assays were initially validated with 50 RT-PCR positive COVID-19 sera, which showed high IgG titers of mainly IgG1 isotype, followed by IgG3. Low or no IgG2 and IgG4 titers were detected. Then, the RBD/IgG assay was further validated with 887 serum samples from RT-PCR positive COVID-19 individuals collected at different times, including 7, 14, 21, and 40 days after the onset of symptoms. Most of the sera were IgG positive at day 40, with seroconversion happening after 14–21 days. A third party conducted an additional performance test of the RBD/IgG assay with 406 sera, including 149 RT-PCR positive COVID-19 samples, 229 RT-PCR negative COVID-19 individuals, and 28 sera from individuals with other viral infections not related to SARS-CoV-2. The sensitivity of the assay was 99.33%, with a specificity of 97.82%. All the sera collected from individuals with infectious diseases other than COVID-19 were negative. Given the robustness of this RBD/IgG assay, it received approval from the sanitary authority in Mexico (COFEPRIS) for production and commercialization under the name UDISTEST-V2G®. [ABSTRACT FROM AUTHOR]

Although universal vaccination is one of the most important healthcare strategies for limiting SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) circulation and averting the huge number of hospitalizations and deaths due to coronavirus disease 2019 (COVID-19), significant inter-individual variability of COVID-19 vaccines' efficacies has been described, mostly due to heterogeneous immune response in recipients. This opinion paper hence aims to discuss aspects related to the opportunity of monitoring anti-SARS-CoV-2 antibodies before and after COVID-19 vaccination, highlighting the pros and cons of this strategy. In summary, the advantages of anti-SARS-CoV-2 antibodies' testing in recipients of COVID-19 vaccination encompass an assessment of baseline seroprevalence of SARS-CoV-2 infection in non-vaccinated individuals; early identification of low or non-responders to COVID-19 vaccination; and timely detection of faster decay of anti-SARS-CoV-2 antibody levels. In contrast, potential drawbacks to date include an unproven equivalence between anti-SARS-CoV-2 antibody titer, neutralizing activity, and vaccine efficiency; the lack of cost-effective analyses of different testing strategies; the enormous volume of blood drawings and increase of laboratory workload that would be needed to support universal anti-SARS-CoV-2 antibodies testing. A potential solution entails the identification of cohorts to be prioritized for testing, including those at higher risk of being infected by variants of concern, those at higher risk of unfavorable disease progression, and subjects in whom vaccine immunogenicity may be expectedly lower and/or shorter. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations. [ABSTRACT FROM AUTHOR]

Lung ultrasound has become increasingly used in both adult and pediatric populations, allowing the rapid evaluation of many lung and pleura diseases. This popularity is due to several advantages of the method such as the low cost, rapidity, lack of ionizing radiation, availability of bedside and repeatability of the method. These features are even more important after the outbreak of the SARS-CoV-2 pandemic, given the possibility of recognizing through ultrasound the signs of interstitial lung syndrome typical of pneumonia caused by the virus. The purpose of this paper is to review the available evidence of lung ultrasound (LUS) in children and its main applications in pediatric diseases. [ABSTRACT FROM AUTHOR]

The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively. [ABSTRACT FROM AUTHOR]

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. [ABSTRACT FROM AUTHOR]

Since December 2019, a new form of coronavirus, SARS-CoV-2, has spread from China to the whole word, raising concerns regarding Coronavirus Disease 2019 (COVID-19) endangering public health and life. Over 1.5 million deaths related with COVID-19 have been recorded worldwide, with wide variations among countries affected by the pandemic and continuously growing numbers. The aim of this paper was to provide an overview of the literature cases of deaths involving COVID-19 and to evaluate the application of the COVID-19 Significance Score (CSS) in the classification of SARS CoV-2-related fatalities, comparing it with the Hamburg rating scale. The results obtained allowed us to highlight that CSS used after a complete accurate post-mortem examination, coupled to the retrieval of in vivo data, post-mortem radiology, histology and toxicology, as well as to additional required analyses (e.g., electronic microscopy) is a useful and concise tool in the assessment of the cause of death and the role played by this virus. A shared use of this scale might hopefully lower the inhomogeneities in forensic evaluation of SARS CoV-2-related fatalities. [ABSTRACT FROM AUTHOR]

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34 % and an intersection-over-union (IoU) score of 68.77 % —higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10 % better than those of the popular biomedical segmentation method U-Net. [ABSTRACT FROM AUTHOR]

One of the most effective ways to prevent the spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is to develop accurate and rapid diagnostic tests. There are a number of molecular, serological, and imaging methods that are used to diagnose this infection in hospitals and clinical settings. The purpose of this review paper is to present the available approaches for detecting SARS-CoV-2 and address the advantages and limitations of each detection method. This work includes studies from recent literature publications along with information from the manufacturer's manuals of commercially available SARS-CoV-2 diagnostic products. Furthermore, supplementary information from the Food & Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) is cited. The viral components targeted for virus detection, the principles of each diagnostic technique, and the detection efficiency of each approach are discussed. The potential of using diagnostic tests that were originally developed for previous epidemic viruses is also presented. [ABSTRACT FROM AUTHOR]

Accurate diagnosis at an early stage of infection is essential for the successful management of any contagious disease. The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is a pandemic that has affected 214 countries affecting more than 37.4 million people causing 1.07 million deaths as of the second week of October 2020. The primary diagnosis of the infection is done either by the molecular technique of RT-qPCR by detecting portions of the RNA of the viral genome or through immunodiagnostic tests by detecting the viral proteins or the antibodies produced by the host. As the demand for the test increased rapidly many naive manufacturers entered the market with novel kits and more and more laboratories also entered the diagnostic arena making the test result more error-prone. There are serious debates globally and regionally on the sensitivity and specificity of these tests and about the overall accuracy and reliability of the tests for decision making on control strategies. The significance of the test is also complexed by the presence of asymptomatic carriers, re-occurrence of infection in cured patients as well as by the varied incubation periods of the infection and shifting of the viral location in the host tissues. In this paper, we review the techniques available for SARS-CoV-2 diagnosis and probable factors that can reduce the sensitivity and specificity of the different test methods currently in vogue. We also provide a checklist of factors to be considered to avoid fallacious practices to reduce false positive and false negative results by the clinical laboratories. [ABSTRACT FROM AUTHOR]

(1) Background: The paper aims to review the available evidence regarding the health risk of the aerosolization induced by laparoscopy induced and impact of the COVID-19 pandemic upon minimally invasive surgery. (2) Materials and methods: A systematic review of the literature was performed on PubMed, Medline and Scopus until 10 July. (3) Results: Chemicals, carcinogens and biologically active materials, such as bacteria and viruses, have been isolated in surgical smoke. However, the only evidence of viral transmission through surgical smoke to medical staff is post-laser ablation of HPV-positive genital warts. The reports of SARS-CoV-2 infected patients who underwent laparoscopic surgery revealed the presence of the virus, when tested, in digestive wall and stools in 50% of cases but not in bile or peritoneal fluid. All surgeries did not result in contamination of the personnel, when protective measures were applied, including personal protective equipment (PPE) and filtration of the pneumoperitoneum. There are no comparative studies between classical and laparoscopic surgery. (4) Conclusions: Previously published data showed there is a possible infectious and toxic risk related to surgical smoke but not particularly proven for SARS-CoV-2. Implementing standardized filtration systems for smoke evacuation during laparoscopy, although increases costs, is necessary to increase the safety and it will probably remain a routine also in the future. [ABSTRACT FROM AUTHOR]

At the time of writing, the COVID-19 infection is spreading rapidly. Currently, there is no vaccine or treatment, and researchers around the world are attempting to fight the infection. In this paper, we consider a diagnosis method for COVID-19, which is characterized by a very rapid rate of infection and is widespread. A possible method for avoiding severe infections is to stop the spread of the infection in advance by the prompt and accurate diagnosis of COVID-19. To this end, we exploit a group testing (GT) scheme, which is used to find a small set of confirmed cases out of a large population. For the accurate detection of false positives and negatives, we propose a robust algorithm (RA) based on the maximum a posteriori probability (MAP). The key idea of the proposed RA is to exploit iterative detection to propagate beliefs to neighbor nodes by exchanging marginal probabilities between input and output nodes. As a result, we show that our proposed RA provides the benefit of being robust against noise in the GT schemes. In addition, we demonstrate the performance of our proposal with a number of tests and successfully find a set of infected samples in both noiseless and noisy GT schemes with different COVID-19 incidence rates. [ABSTRACT FROM AUTHOR]