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Introduction and aim. A small number of critically ill patients with coronavirus disease (COVID-19) develop thromboembolism (arterial or venous), both micro- and macrovascular complications such as deep vein thrombosis, pulmonary embolism, and pulmonary arterial thrombosis. The objective of the study is to describe the pathophysiology of venous thromboembolism in patients with COVID-19. Material and methods. In this article a narrative review regarding pathophysiology of thromboembolism in patients with COVID-19. Analysis of the literature. The development of coagulopathy is a consequence of the intense inflammatory response associated with hypercoagulability, platelet activation, and endothelial dysfunction. The pathophysiology that relates pulmonary thromboembolism (PTE) with COVID-19 is associated with a hypercoagulable state. PTE is suspected in hospitalized patients presenting dyspnea, decreased oxygen requirement, hemodynamic instability, and dissociation between hemodynamic and respiratory changes. In COVID-19-associated coagulopathy, initially, patients present with elevated levels of fibrinogen and D-dimer, with minimal changes in prothrombin time and platelet count. The main risk factor for the development of pulmonary embolism is the increase in D-dimer that is associated with the development of PTE. The administration of iodine-based contrast agent to patients with COVID-19 would affect P-creatinine and renal function, where Ultrasound is viewed as cost-effective and highly portable, can be performed at the bedside. Conclusion. Acute respiratory distress syndrome severity in patients with COVID-19 can explain PTE as a consequence of an exaggerated immune response. © 2022 Publishing Office of the University of Rzeszow. All Rights Reserved.
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Introduction: The impact of the coronavirus disease (COVID-19) outbreak on many parts of our lives cannot be overstated. This study aimed to identify the psychological, physical activity, and educational effects of COVID-19 on radiological sciences students and interns at the three campuses of King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Jeddah, and Alahsa. Methods: A cross-sectional study was conducted between November and December 2021 among Saudi-108 radiological sciences students and interns using non-probability convenient sampling at King Saud bin Abdul-Aziz University for Health Science (KSAU-HS), Riyadh, Jeddah, and Alahsa using a validated questionnaire. Statistical analyses were conducted using Excel and JMP statistical software. Results: 102 out of 108 completed the questionnaire resulting in a 94.44% response rate. The percentage of the overall negative psychological impact was 62%. For the physical activity effects of COVID-19 among students and interns, 96% reported a decline in their physical activities. 77% of participants reported a fair impression that the students were able to achieve some of their academic goals and acquired new skills during the pandemic, and 20% reported a good impression. They achieved all their goals and developed new skills, whereas 3% reported bad impressions and needed to achieve their goals or improve their skills. Conclusion: COVID-19 had a negative psychological and physical activity impact on RADs students and interns at the three KSAU-HS campuses in the Kingdom of Saudi Arabia. Despite technical difficulties, students and interns reported positive academic outcomes from COVID-19.
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COVID-19 is a severe respiratory tract infections which can range from mild to lethal. COVID-19 caused by SARS-CoV-2 can readily spread through direct or indirect contact with an infected person. This high spread rate pressure on the health care systems and requires non time-consuming methods for diagnosing. Convolutional Neural Networks (CNN) show a great success for various computer vision tasks. However, CNN like many computer vision models is a scale-variant model and requires expensive computation. In this paper, a novel micro architecture is proposed for multiscale feature extraction and classification. Proposed CNN learns multiscale features using a pyramid of shared convolution kernels with different dilation, atrous, rates. Proposed CNN is an attention based mechanism that is used to guide and select correct scale for each input. Proposed CNN is an end-to-end trainable Network. It achieved a 0.9929 for F1-score tested on QaTa-Cov19 benchmark dataset with a total of 5,040,571 trainable parameters.
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COVID-19 , Respiratory Tract Infections , Vision DisordersABSTRACT
The occurrence of sudden viral outbreaks, including (Covid-19, H1N1 flu, H5N1 flu) has globally challenged the existing medical facilities and raised critical concerns about saving affected lives, especially during pandemics. The detection of viral infections at an early stage using biosensors has been proven to be the most effective, economical, and rapid way to combat their outbreak and severity. However, state-of-the-art biosensors possess bottlenecks of long detection time, delayed stage detection, and sophisticated requirements increasing the cost and complexities of biosensing strategies. Recently, using two-dimensional MXenes as a sensing material for architecting biosensors has been touted as game-changing technology in diagnosing viral diseases. The unique surface chemistries with abundant functional terminals, excellent conductivity, tunable electric and optical attributes and high specific surface area have made MXenes an ideal material for architecting virus-diagnosing biosensors. There are numerous detecting modules in MXene-based virus-detecting biosensors based on the principle of detecting various biomolecules like viruses, enzymes, antibodies, proteins, and nucleic acid. This comprehensive review critically summarizes the state-of-the-art MXene-based virus-detecting biosensors, their limitations, potential solutions, and advanced intelligent prospects with the integration of internet-of-things, artificial intelligence, 5G communications, and cloud computing technologies. It will provide a fundamental structure for future research dedicated to intelligent and point-of-care virus detection biosensors.
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Vision Transformers (ViTs), with the magnificent potential to unravel the information contained within images, have evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by plenty of researchers to perform new as well as former experiments. Here, in this article, we investigate the intersection of vision transformers and medical images. We proffered an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision. We surveyed the applications of Vision Transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion detection, captioning, report generation, and reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process. Along with this, we also demystify several imaging modalities used in medical computer vision. Moreover, to get more insight and deeper understanding, the self-attention mechanism of transformers is also explained briefly. Conclusively, the ViT based solutions for each image analytics task are critically analyzed, open challenges are discussed and the pointers to possible solutions for future direction are deliberated. We hope this review article will open future research directions for medical computer vision researchers.
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The lack of preparedness for detecting and responding to the SARS-CoV-2 pathogen (i.e. COVID-19) has caused enormous harm to public health and the economy. Testing strategies deployed on a population scale at ‘Day Zero', i.e., the time of the first reported case, would be of significant value. Next Generation Sequencing (NGS) has such capabilities;however, it has limited detection sensitivity for low copy number pathogens. Here we leverage the CRISPR-Cas9 system to effectively remove abundant sequences not contributing to pathogen detection and show that NGS detection sensitivity of SARS-CoV-2 approaches that of RT-qPCR. The resulting sequence data can also be used for variant strain typing, co-infection detection, and individual human host response assessment, all in a single molecular and analysis workflow. This NGS workflow is pathogen agnostic and, therefore, has the potential to transform how large-scale pandemic response and focused clinical infectious disease testing are pursued in the future. Graphical Next generation sequencing could provide ‘Day Zero' testing for pandemic preparedness however, abundant uninformative sequences mask the signal from low level pathogens. Chan et al. establish a method using the CRISPR-Cas system to remove uninformative sequences in vitro to achieve sensitivity and specificity of pathogen detection comparable to RT-qPCR.
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Coronavirus disease 2019 (COVID-19) is an epidemic viral disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the excessive number of neurological articles that have investigated the effect of COVID-19 on the brain from the neurological point of view, very few studies have investigated the impact of COVID-19 on the cerebral microstructure and function of the brain. The aim of this study was to summarize the results of the existing studies on cerebral microstructural changes in COVID-19 patients, specifically the use of quantitative volumetric analysis, blood oxygen level dependent (BOLD), and diffusion tensor imaging (DTI). We searched PubMed/MEDLINE, ScienceDirect, Semantic Scholar, and Google Scholar from December 2020 to April 2022. A well-constructed search strategy was used to identify the articles for review. Seven research articles have met this study's inclusion and exclusion criteria, which have applied neuroimaging tools such as quantitative volumetric analysis, BOLD, and DTI to investigate cerebral microstructure changes in COVID-19 patients. A significant effect of COVID-19 was found in the brain such as hypoperfusion of cerebral blood flow, increased gray matter (GM) volume, and reduced cortical thickness. The insula and thalamic radiation were the most frequent GM region and white matter tract, respectively, that are involved in SARS-CoV-2. COVID-19 was found to be associated with changes in cerebral microstructures. These abnormalities in brain areas might lead to be associated with behaviors, mental and neurological alterations that need to be considered carefully in future studies.
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Asymptomatic and pre-symptomatic staff and residents likely contribute to widespread transmission of COVID-19 in long-term care settings. Here, we describe the successful containment of a COVID-19 outbreak on one floor of a 163-bed Veterans Affairs (VA) Community Living Center (CLC). Testing using nasopharyngeal swabs with a rapid turn-around-time identified 3 of 28 (11%) residents and 2 of 41 (5%) healthcare personnel (HCP) with COVID-19. Both HCP likely worked on the floor while pre-symptomatic. When one HCP reported a cough to the secondary (employee) screening clinic, she was erroneously advised to work. Protocols to limit the risk for HCP to import COVID-19 were reinforced with Community Living Center staff as well as with personnel in secondary screening. Further, the CLC implemented an expanded screening tool that assessed residents for typical and atypical symptoms of COVID-19. No further cases of COVID-19 were detected on the CLC floor in the subsequent 6 weeks. Swift recognition and response helped contain the outbreak and prevent further COVID-19 infections among other residents and staff.
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Background: Honey has been used medicinally in folk medicine since the dawn of civili-zation. It is a necessary component of medicine and food in a wide variety of cultures. It has been used in Unani Medicine for centuries to treat a variety of ailments. Objective(s): This review article aims to explore the medicinal characteristics of honey in view of Unani and modern concepts, highlight its potential in the treatment of the ailments stated in Unani medical literature, and also explore the relevant evidence-based phytochemistry, pharmacological, and clinical data. Method(s): The authors searched classical texts exhaustively for information on the temperament (Mizaj), pharmacological activities, mechanism of action, and therapeutic benefits of honey. Addition-ally, a comprehensive search of internet databases was conducted to compile all available information on the physicochemical, phytochemical, and pharmacological properties of this compound. Result(s): Evidence suggests that honey contains about 180 different types of various compounds, including carbohydrates, proteins, enzymes, flavonoids, and other chemical substances. In Unani classical literature, it exerts important pharmacological actions besides its immense nutritional signifi-cance. Unani physicians advocated many tested/experimented prescriptions and formulations, which still have their relevance in the amelioration of various diseases. Conclusion(s): This analysis concludes that honey has been successfully utilized in Unani medicine for centuries to treat a variety of maladies and is a potential natural source of remedy for a variety of medical disorders. Future research on honey should include a combination of Unani and modern principles.Copyright © 2023 Bentham Science Publishers.
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Asymptomatic and pre-symptomatic staff and residents likely contribute to widespread transmission of COVID-19 in long-term care settings. Here, we describe the successful containment of a COVID-19 outbreak on one floor of a 163-bed Veterans Affairs (VA) Community Living Center (CLC). Testing using nasopharyngeal swabs with a rapid turn-around-time identified 3 of 28 (11%) residents and 2 of 41 (5%) healthcare personnel (HCP) with COVID-19. Both HCP likely worked on the floor while pre-symptomatic. When one HCP reported a cough to the secondary (employee) screening clinic, she was erroneously advised to work. Protocols to limit the risk for HCP to import COVID-19 were reinforced with Community Living Center staff as well as with personnel in secondary screening. Further, the CLC implemented an expanded screening tool that assessed residents for typical and atypical symptoms of COVID-19. No further cases of COVID-19 were detected on the CLC floor in the subsequent 6 weeks. Swift recognition and response helped contain the outbreak and prevent further COVID-19 infections among other residents and staff.
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Oral anticancer therapy mostly faces the challenges of low aqueous solubility, poor and irregular absorption from the gastrointestinal tract, food-influenced absorption, high first-pass metabolism, non-targeted delivery, and severe systemic and local adverse effects. Interest has been growing in bioactive self-nanoemulsifying drug delivery systems (bio-SNEDDSs) using lipid-based excipients within nanomedicine. This study aimed to develop novel bio-SNEDDS to deliver antiviral remdesivir and baricitinib for the treatment of breast and lung cancers. Pure natural oils used in bio-SNEDDS were analyzed using GC-MS to examine bioactive constituents. The initial evaluation of bio-SNEDDSs were performed based on self-emulsification assessment, particle size analysis, zeta potential, viscosity measurement, and transmission electron microscopy (TEM). The single and combined anticancer effects of remdesivir and baricitinib in different bio-SNEDDS formulations were investigated in MDA-MB-231 (breast cancer) and A549 (lung cancer) cell lines. The results from the GC-MS analysis of bioactive oils BSO and FSO showed pharmacologically active constituents, such as thymoquinone, isoborneol, paeonol and p-cymenene, and squalene, respectively. The representative F5 bio-SNEDDSs showed relatively uniform, nanosized (247 nm) droplet along with acceptable zeta potential values (+29 mV). The viscosity of the F5 bio-SNEDDS was recorded within 0.69 Cp. The TEM suggested uniform spherical droplets upon aqueous dispersions. Drug-free, remdesivir and baricitinib-loaded bio-SNEDDSs (combined) showed superior anticancer effects with IC50 value that ranged from 1.9-4.2 µg/mL (for breast cancer), 2.4-5.8 µg/mL (for lung cancer), and 3.05-5.44 µg/mL (human fibroblasts cell line). In conclusion, the representative F5 bio-SNEDDS could be a promising candidate for improving the anticancer effect of remdesivir and baricitinib along with their existing antiviral performance in combined dosage form.
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Breast Neoplasms , Lung Neoplasms , Nanoparticles , Humans , Female , Drug Repositioning , Administration, Oral , Emulsions , Drug Delivery Systems/methods , Solubility , Oils , Particle Size , Biological Availability , Surface-Active Agents , Drug LiberationABSTRACT
BACKGROUND: The Middle East respiratory syndrome coronavirus (MERS-CoV) is a pathogen associated with an acute respiratory infection that has a high mortality rate in humans. It was first identified in June of 2012 in the Arabian Peninsula. The success of the COVID-19 vaccines has shown that it is possible to take advantage of medical and scientific advances to produce safe and effective vaccines for coronaviruses. This study aimed to examine the safety and immunogenicity of MERS-CoV vaccines. METHODS: The research method Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used as the guideline for this study. RevMan 5.4 software was used to perform a meta-analysis of the included studies. The safety was assessed by recording adverse events following vaccination, and the immunogenicity was assessed by using seroconversion. RESULTS: The study included five randomized controlled trials that met the inclusion criteria after screening. The studies had 173 participants and were performed in four countries. The vaccines examined were the ChAdOx1 MERS vaccine, MVA-MERS-S vaccine, and GLS-5300 DNA MERS-CoV vaccine. The meta-analysis showed no significant differences in local adverse effects (all local adverse effects and pain) or systemic adverse effects (all systemic adverse effects, fatigue, and headache) among participants in groups receiving a high-dose vaccine or a low-dose vaccine. There were, however, higher levels of seroconversion in high-dose groups than in low-dose groups (OR 0.16 [CI 0.06, 0.42, p = 0.0002]). CONCLUSION: The findings showed that high doses of current MERS-CoV vaccine candidates conferred better immunogenicity than low doses and that there were no differences in the safety of the vaccines.
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COVID-19 , Middle East Respiratory Syndrome Coronavirus , Humans , Middle East Respiratory Syndrome Coronavirus/genetics , COVID-19 Vaccines , Antibodies, Viral , DNAABSTRACT
COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.
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COVID-19 , Humans , COVID-19/diagnostic imaging , Thorax , Benchmarking , Neural Networks, Computer , Tomography, X-Ray ComputedABSTRACT
The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients' images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently. The performance of the Swin transformer is compared with the other seven deep learning models, including ResNet50, DenseNet121, InceptionV3, Efficient-NetB2, VGG19, ViT, CaIT, Swim transformer provides 98% recall and 96% accuracy on corona affected images of the X-ray category. The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis, and proposed technique is found better in terms of accuracy. Our system could support clin-icians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contri-bute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients. © 2023, Tech Science Press. All rights reserved.
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OBJECTIVES: To evaluate the role of teleradiology during the COVID-19 pandemic from Saudi radiologists' perspectives to improve the radiology quality service. METHODS: A cross-sectional study was carried out in Saudi Arabia among radiologists working at local hospitals from October to November 2021. It contains 21 questions involved demographic information; general information on teleradiology services; and the impact of teleradiology during COVID-19. One-way ANOVA was used to compare demographic groups. Chi-square test was used to compare demographic groups regarding their distribution of responses. All tests were carried out <0.05 level of significance. RESULTS: A total of 102 radiologists participated in this study (56% males, 44% females), 58.8% of them were sub-specialized in chest radiology. Regarding the general status of teleradiology, 69.6% of participants believed that teleradiology is a helpful tool for imaging interpretation. However, 44% of them were uncertain on the impact of teleradiology on patients' confidentiality. Approximately 87% of participants agreed that there is a positive contribution of teleradiology during COVID-19, which enables decreasing risk of infection and workload. There was a significant difference between professional degrees and overall participant responses (p<0.05). Academicians agreed that it enhances radiology departments' work (mean=17.78, SD=1.86). CONCLUSION: Concerns raised on complicated cases that require physical presence of patients, cannot be performed by teleradiology. Additionally, it might provide insufficient communication with other professionals to discuss images.
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COVID-19 , Teleradiology , Male , Female , Humans , Cross-Sectional Studies , Saudi Arabia/epidemiology , Pandemics , RadiologistsABSTRACT
Image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.
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Graphical Research has shown that chloroquine (CQ) can effectively help control COVID-19 infection. B24N24 nanocage is a drug delivery system. Thus, through density functional theory, the present study analyzed pristine nanocage-CQ interaction and CQ interaction with Si- and Al -doped nanocage. The findings revealed that nanocage doping, particularly with Si and Al, yields more satisfactory drug delivery for CQ due to their greater electronic and energetic characteristics with CQ.
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Research has shown that chloroquine (CQ) can effectively help control COVID-19 infection. B24N24 nanocage is a drug delivery system. Thus, through density functional theory, the present study analyzed pristine nanocage-CQ interaction and CQ interaction with Si- and Al -doped nanocage. The findings revealed that nanocage doping, particularly with Si and Al, yields more satisfactory drug delivery for CQ due to their greater electronic and energetic characteristics with CQ.
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Enzyme inhibitors are frequently used to treat viral illnesses. Protease inhibitors are a promising class for combating novel and life-threatening viral infections. This research aimed to evaluate the efficacy and safety of lopinavir/ritonavir monotherapy or lopinavir/ritonavir plus interferon for the treatment of COVID-19. The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched for English articles with full texts available online. ReviewManager software was used to conduct a meta-analysis, subgroup analysis, and sensitivity analysis. Following the creation of the protocol, the collected sources were sorted into categories and evaluated for quality. Risk and hazard ratios and the random effects model were implemented, with statistical heterogeneity assigned using the Higgins I2 statistic. Lopinavir/ritonavir, with or without interferon, was associated with a nonsignificant higher mortality rate (odds ratio [OR] 1.29;95% confidence interval [CI] 0.95 to 1.761;p = 0.1), as was clinical improvement (OR 1.2;95% CI 0.8 to 1.84;p = 0.36). The difference in the length of hospital stay was in favor of the control group but statistically insignificant (standardized mean difference [SMD] 0.07;95% CI -0.44 to 0.57;p = 0.79). The pooled data showed that lopinavir/ritonavir, with or without interferon, was associated with a significantly higher number of adverse events than placebo (OR 1.2;95% CI 1.09 to 2.34;p = 0.02). Serious adverse events were insignificantly increased in the treated group over the control group (OR 1.2;95% CI 0.96 to 2.12;p = 0.08). In the subgroup analysis, it was found that interferon used with lopinavir/ritonavir did not have a statistically significant effect on mortality rates (OR 1.75;95% CI 0.87 to 3.55;p = 0.37), adverse effects (OR 1.20;95% CI 0.75 to 1.91;p = 0.27), or serious adverse effects (OR 1.86;95% CI 1.17 to 2.96;p = 0.33). Treatment with lopinavir/ritonavir alone or in combination with interferon for COVID-19 did not significantly outperform placebo in this study. Large randomized clinical trials are required to evaluate lopinavir/ritonavir in conjunction with interferon for the treatment of COVID-19. Such studies would benefit greatly from being conducted in a double-blind fashion at multiple locations.