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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]

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support. [ABSTRACT FROM AUTHOR]

During the waves of the coronavirus disease (COVID-19) pandemic, emergency departments were overflowing with patients suffering with suspected medical or surgical issues. In these settings, healthcare staff should be able to deal with different medical and surgical scenarios while protecting themselves against the risk of contamination. Various strategies were used to overcome the most critical issues and guarantee quick and efficient diagnostic and therapeutic charts. The use of saliva and nasopharyngeal swab Nucleic Acid Amplification Tests (NAAT) in the diagnosis of COVID-19 was one of the most adopted worldwide. However, NAAT results were slow to report and could sometimes create significant delays in patient management, especially during pandemic peaks. On these bases, radiology has played and continues to play an essential role in detecting COVID-19 patients and solving differential diagnosis between different medical conditions. This systematic review aims to summarize the role of radiology in the management of COVID-19 patients admitted to emergency departments by using chest X-rays (CXR), computed tomography (CT), lung ultrasounds (LUS), and artificial intelligence (AI). [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has led to a significant increase in the number of deaths. This has resulted in forensic autopsies focusing on additional diagnostic possibilities. The following article is a summary of 23 autopsies of sudden and unexplained deaths. Particularly noteworthy are the described cases of children whose deaths were originally classified as SIDS (sudden infant death syndrome). All tests were performed at the Department of Forensic Medicine and Forensic Genetics, Pomeranian Medical University in Szczecin. Autopsy analyses were extended to include diagnostics of the SARS-CoV-2 virus using molecular methods and a detailed histopathological analysis of lung tissue. The material for molecular tests consisted of a nasopharyngeal swab taken postmortem and a lung tissue homogenate. In both cases, the RT-PCR method with CT cut-off point analysis was used for diagnosis. In all analyzed cases, the lungs showed massive congestion and increased fragility and cohesion. The tested material showed the presence of the SARS-CoV-2 virus, which indicated various stages of infection. It was observed that the higher the virus expression in the lungs, the lower or undetectable it was in the nasopharyngeal swab. This may explain false negative results during life in swabs. An interesting finding is that child deaths classified as SIDS also showed the presence of the virus. This may constitute a new direction of research. [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]

Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. [ABSTRACT FROM AUTHOR]

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights. [ABSTRACT FROM AUTHOR]

Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease. [ABSTRACT FROM AUTHOR]

This is a review of full-length articles strictly concerning subacute thyroiditis (SAT) in relation to the SARS-CoV-2 virus infection (SVI) and COVID-19 vaccine (COV) that were published between the 1st of March 2020 and the 21st of March 2022 in PubMed-indexed journals. A total of 161 cases were reported as follows: 81 cases of SAT–SVI (2 retrospective studies, 5 case series, and 29 case reports), 80 respective cases of SAT–COV (1 longitudinal study, 14 case series, 17 case reports; also, 1 prospective study included 12 patients, with 6 patients in each category). To our knowledge, this represents the largest cohort of reported cases until the present time. SAT–SVI was detected in adults aged between 18 and 85 years, mostly in middle-aged females. SAT–COVID-19 timing classifies SAT as viral (synchronous with infection, which is an original feature of SATs that usually follow a viral infection) and post-viral (during the recovery period or after infection, usually within 6 to 8 weeks, up to a maximum 24 weeks). The clinical spectrum has two patterns: either that accompanying a severe COVID-19 infection with multi-organ spreading (most frequent with lung involvement) or as an asymptomatic infection, with SAT being the single manifestation or the first presentation. Either way, SAT may remain unrecognized. Some data suggest that more intense neck pain, more frequent fever, and more frequent hypothyroidism at 3 months are identified when compared with non-SAT–SVI, but other authors have identified similar presentations and outcomes. Post-COVID-19 fatigue may be due to residual post-SAT hypothyroidism. The practical importance of SAT–SVI derives from the fact that thyroid hormone anomalies aggravate the general status of severe infections (particular concerns being tachycardia/arrhythmias, cardiac insufficiency, and ischemic events). If misdiagnosed, SAT results in unnecessary treatment with anti-thyroid drugs or even antibiotics for fever of unknown cause. Once recognized, SAT does not seem to require a particular approach when compared with non-COVID-19 cases, including the need for glucocorticoid therapy and the rate of permanent hypothyroidism. A complete resolution of thyroid hormone anomalies and inflammation is expected, except for cases with persistent hypothyroidism. SAT–COV follows within a few hours to a few weeks, with an average of 2 weeks (no particular pattern is related to the first or second vaccine dose). Pathogenesis includes molecular mimicry and immunoinflammatory anomalies, and some have suggested that this is part of ASIA syndrome (autoimmune/inflammatory syndrome induced by adjuvants). An alternative hypothesis to vaccine-related increased autoimmunity is vaccine-induced hyperviscosity; however, this is supported by incomplete evidence. From what we know so far concerning the risk factors, a prior episode of non-SVI–SAT is not associated with a higher risk of SAT–COV, nor is a previous history of coronavirus infection by itself. Post-vaccine SAT usually has a less severe presentation and a good outcome. Generally, the female sex is prone to developing any type of SAT. HLA susceptibility is probably related to both new types of SATs. The current low level of statistical evidence is expected to change in the future. Practitioners should be aware of SAT–COV, which does not restrict immunization protocols in any case. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) pandemic control measures rely on the accurate and timely diagnosis of infected individuals. Real-time polymerase chain reaction (qPCR) remains the gold-standard method for laboratory diagnosis of the disease. Delayed diagnosis due to challenges that face laboratories performing COVID-19 testing can hinder public health control measures. Such challenges may be related to shortages in staff, equipment or materials, improper inventory management, flawed workflow, or long turnaround time (TAT). The aim of the current study was to assess the overall COVID-19 molecular testing capacity in Jordan as of April 2021. In addition, the study's objectives included the identification of potential defects that could comprise the utility of the COVID-19 molecular testing capacity in the country. All laboratories certified by the Ministry of Health (MoH) in Jordan to conduct molecular testing for SARS-CoV-2 were invited to participate in this study. Data were obtained from the participating laboratories (those which agreed to participate) by either telephone interviews or a self-reported written questionnaire with items assessing the key aspects of COVID-19 molecular testing. The full molecular testing capacity in each laboratory was self-reported considering 24 working hours. The total number of participating laboratories was 51 out of 77 (66.2%), with the majority being affiliated with MoH (n = 17) and private laboratories (n = 20). The total molecular COVID-19 testing capacity among the participating laboratories was estimated at 574,441 tests per week, while the actual highest number of tests performed over a single week was 310,047 (54.0%, reported in March 2021). Laboratories affiliated with the MoH were operating at a level closer to their maximum capacity (87.2% of their estimated full capacity for COVID-19 testing) compared to private hospital laboratories (41.3%, p = 0.004), private laboratories (20.8%, p < 0.001), and academic/research laboratories (14.7%, p < 0.001, ANOVA). The national average daily COVID-19 molecular testing was 349.2 tests per 100,000 people in April 2021. The average TAT over the first week of April 2021 for COVID-19 testing was 932 min among the participating laboratories, with the longest TAT among MoH laboratories (mean: 1959 min) compared to private laboratories (mean: 333 min, p < 0.001). Molecular COVID-19 testing potential in Jordan has not been fully utilized, particularly for private laboratories and those belonging to academic/research centers. Supply-chain challenges and shortages in staff were identified as potential obstacles hindering the exploitation of full molecular testing capacity for COVID-19 in the country. [ABSTRACT FROM AUTHOR]

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses' infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application's primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19)-associated coagulopathy (CAC), characterized by hypercoagulability and an increased risk of thrombotic complications, is an important consideration in the management of patients with COVID-19. As COVID-19 is a new disease, no standard of care for the diagnosis or management of its associated coagulopathy is yet established. Whole blood viscoelastic tests, such as thromboelastography (TEG® hemostasis analyzer), analyze whole blood to provide a complete overview of the coagulation status. We conducted a systematic review of thromboelastography for management of patients with COVID-19, using MEDLINE (PubMed) and Cochrane databases. TEG® parameter measurements and clinical outcomes data were extracted for analysis. Our review found 15 publications, with overall results showing thromboelastography can identify and assess a hypercoagulable state in patients with COVID-19. Furthermore, utilization of thromboelastography in this patient population was shown to predict thrombotic complications. The benefits of thromboelastography presented here, in addition to advantages compared with laboratory coagulation tests, position thromboelastography as an important opportunity for optimizing diagnosis of CAC and improving patient management in COVID-19. Given that the benefits of thromboelastography have already been demonstrated in several other clinical applications, we anticipate that clinical data from future studies in patients with COVID-19 will further elucidate the optimal use of thromboelastography in this patient population. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients. [ABSTRACT FROM AUTHOR]

Background: A previous study demonstrated the performance of the Salivette® (SARSTEDT, Numbrecht, Germany) as a homogeneous saliva collection system to diagnose COVID-19 by RT-qPCR, notably for symptomatic and asymptomatic patients. However, for convalescent patients, the corroboration of molecular detection of SARS-CoV-2 in paired nasopharyngeal swabs (NPS) and saliva samples was unsatisfactory. Objectives: The aim of the present work was to assess the concordance level of SARS-CoV-2 detection between paired sampling of NPSs and saliva collected with Salivette® at two time points, with ten days of interval. Results: A total of 319 paired samples from 145 outpatients (OP) and 51 healthcare workers (HW) were collected. Unfortunately, at day ten, 73 individuals were lost to follow-up, explaining some kinetic missing data. Due to significant waiting rates at hospitals, most of the patients ate and/or drank while waiting for their turn. Consequently, mouth washing was systematically proposed prior to saliva collection. None of the HW were diagnosed as SARS-CoV-2 positive using NPS or saliva specimens at both time points (n = 95) by RT-qPCR. The virus was detected in 56.3% (n = 126/224) of the NPS samples from OP, but solely 26.8% (n = 60/224) of the paired saliva specimens. The detection of the internal cellular control, the human RNase P, in more than 98% of the saliva samples, underlined that the low sensitivity of saliva specimens (45.2%) for SARS-CoV-2 detection was not attributed to an improper saliva sample storing or RNA extraction. Conclusions: This work revealed that mouth washing decreased viral load of buccal cavity conducting to impairment of SARS-CoV-2 detection. Viral loads in saliva neo-produced appeared insufficient for molecular detection of SARS-CoV-2. At the time when saliva tests could be a rapid, simple and non-invasive strategy to assess large scale schoolchildren in France, the determination of the performance of saliva collection becomes imperative to standardize procedures. [ABSTRACT FROM AUTHOR]

The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers' effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment. [ABSTRACT FROM AUTHOR]

The unusual cases of pneumonia outbreak were reported from Wuhan city in late December 2019. Serological testing provides a powerful tool for the identification of prior infection and for epidemiological studies. Pseudotype virus neutralization assays are widely used for many viruses and applications in the fields of serology. The accuracy of pseudotype neutralizing assay allows for its use in low biosafety lab and provides a safe and effective alternative to the use of wild-type viruses. In this study, we evaluated the performance of this assay compared to the standard microneutralization assay as a reference. The lentiviral pseudotype particles were generated harboring the Spike gene of SARS-CoV-2. The generated pseudotype particles assay was used to evaluate the activity of neutralizing antibodies in 300 human serum samples from a COVID-19 sero-epidemiological study. Testing of these samples resulted in 55 positive samples and 245 negative samples by pseudotype viral particles assay while microneutralization assay resulted in 64 positive and 236 negative by MN assay. Compared to the MN, the pseudotyped viral particles assay showed a sensitivity of 85.94% and a specificity of 100%. Based on the data generated from this study, the pseudotype-based neutralization assay showed a reliable performance for the detection of neutralizing antibodies against SARS-CoV-2 and can be used safely and efficiently as a diagnostic tool in a biosafety level 2 laboratory. [ABSTRACT FROM AUTHOR]

Providing guidelines to health care workers during a period of rapidly evolving viral pandemic infections is not an easy task, but it is extremely necessary in order to coordinate appropriate action so that all patients will get the best possible care given the circumstances they are in. With these International Society of Infectious Disease in Obstetrics and Gynecology (ISIDOG) guidelines we aim to provide detailed information on how to diagnose and manage pregnant women living in a pandemic of COVID-19. Pregnant women need to be considered as a high-risk population for COVID-19 infection, and if suspected or proven to be infected with the virus, they require special care in order to improve their survival rate and the well-being of their babies. Both protection of healthcare workers in such specific care situations and maximal protection of mother and child are envisioned. [ABSTRACT FROM AUTHOR]