Search Results (86)

Influenza and SARS-CoV-2 are often associated with viral pneumonia, resulting from direct exposure of the virus to lung tissue. 3,3′-Diindolylmethane (DIM) is a naturally occurring substance with multi-target activity, including anti-inflammatory and epigenetic modulation. In this study, we evaluated the therapeutic efficacy in vivo of a DIM formulation with fish oil (Cesarox Epi) against influenza A (H1N1) infection in mice and against SARS-CoV-2 infection in Syrian hamsters. In a model of lethal influenza pneumonia induced by A/California/04/2009 (H1N1)pdm09 virus, we showed that 5 days’ treatment with DIM Epi at 10, 20, and 60 mg/kg/day delayed the time to death, prevented body weight loss, and resulted in significant improvements in survival. DIM Epi tested in hamsters infected with SARS-CoV2 Dubrovka (Wuhan-like) strain at doses 50 and 100 mg/kg/day reduced clinical signs, weight loss, temperature elevation, and lung pathology. In both models of infections, treatment with DIM Epi did not significantly decrease viral titer in the animals’ lungs. DIM Epi and Oseltamivir were more effective against influenza infection when given in combination than given singly, while co-administration of DIM Epi with Molnupiravir did not yield an additive benefit against SARS-CoV-2 infection. These findings support DIM Epi as a promising host-directed adjunct therapy for viral pneumonia with potential to enhance outcomes in respiratory infections. Full article

Background and Objectives: Infection with SARS-CoV-2, the etiologic agent of Coronavirus 2019, spread rapidly globally after the first case was reported in Wuhan, China. Multiple respiratory complications, including pneumothorax and pneumomediastinum, have been observed. This study presents an analysis of 100 patients diagnosed with these conditions in the context of SARS-CoV-2 infection. Materials and Methods: This study was conducted between March 2020 and February 2021 and included patients from two hospital units designated for the management of patients with SARS-CoV-2 infection. Demographic data, laboratory investigation results, imaging assessments, medical-surgical management strategies, and survival data were recorded. Results: The study included 100 patients with confirmed SARS-CoV-2 infection (mechanically ventilated and non-ventilated). Of these, 57 patients presented with pneumothorax, 26 of whom also had associated pneumomediastinum and 43 of whom were diagnosed with pneumomediastinum alone. There was a higher incidence of pneumothorax among male patients. Also, 22 patients had concomitant subcutaneous emphysema. Regarding therapeutic management, 36 pleural drains were performed. Bilateral pneumothorax was identified in five patients. Conclusions: The presence of pneumothorax was correlated with a decreased survival rate among patients diagnosed with COVID-19. Also, performing pleural drainage in patients with pneumothorax and COVID-19 pneumonia did not significantly influence the prognosis of the underlying disease. Full article

A cluster of cases caused by the virus SARS-CoV-2 was detected in Wuhan, China, in December 2019. The disease derived from that virus was named Coronavirus (COVID-19), which was officially recognized as a pandemic by the World Health Organization in March 2020. Since COVID-19 can cause serious pneumonia, early diagnosis is crucial for adequate treatment and for reducing health system overload. Therefore, deep learning algorithms to detect pneumonia have been developed using computed tomography (CT) scans, as they provide more detailed information about the disease because of their three-dimensionality and good visibility. This information analyzed by specialists could support the confirmation of pneumonia. To find out the accuracy levels of various classifiers, we evaluated the baseline models utilized by researchers. The findings we drew were that the majority of CT classification algorithms have strong accuracy values in comparison to other algorithms performed using CT, but have not reached above 98%. According to the systematic literature survey, low accuracy levels resulting from the performance of the models were attributed to the incongruous dealing of medical images. These images instead of having common formats such as png or jpg, use more complex formats such as DICOM and NIFTI, in order to save more information about the disease and the patient. Moreover, some studies found that the influence of environmental conditions and lung movement could affect the quality of the image. This unclear pneumonia area may also result in a decrease in the efficiency of deep-learning algorithms for detecting pneumonia. Therefore, the objective of this survey is to identify, gather data and build a catalog of deep-learning techniques for detecting pneumonia abnormalities and annotating CT images from the literature review, reflecting a better understanding of the classification of pneumonia using CT images. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been identified as the etiologic agent for the pneumonia outbreak that started in early December 2019 in Wuhan City, Hubei Province, China. To date, coronavirus disease (COVID-19) has caused almost 6 million deaths worldwide. The ability to propagate the virus into a customizable volume will enable better research on COVID-19 therapy, vaccine development, and many others. In the search for the most efficient replication host, we inoculated three (3) local SARS-CoV-2 isolates of different lineages (Clade L/Lineage B Wuhan, Clade GR/Lineage B.1.1.354, and Clade O/Lineage B.6.2) into various clinically important mammalian cell lines. The replication profile of these isolates was evaluated based on the formation of cytopathic effects (CPE), viral load (Ct value and plaque-forming unit (pfu)), as well as observation by electron microscopy (EM). Next-generation sequencing (NGS) was performed to examine the genomic stability of the propagated SARS-CoV-2 in these cell lines. Our study found that Vero E6 and Vero CCL-81 cell lines posed similar capacities in propagating the local isolates, with Vero CCL-81 demonstrating exceptional potency in conserving the genomic stability of the Lineage B Wuhan isolate. In addition, our study demonstrated the utility of Calu-3 cells as a replication host for SARS-CoV-2 without causing substantial cellular senescence. In conclusion, this study provides crucial information on the growth profile of Malaysian SARS-CoV-2 in various mammalian cell lines and thus will be a great source of reference for better isolation and propagation of the SARS-CoV-2 virus isolated in Malaysia. Full article

Since COVID-19 first emerged in Wuhan, China, and was declared a global pandemic by the WHO, researchers have been meticulously studying the disease and its complications. Studies of severe COVID-19 disease among pediatric populations are scarce, leading to difficulty in establishing a comprehensive management approach. Case presentation: This report outlines a case of a long-standing combined iron and vitamin B12 deficiency anemia in a three-year-old treated at the Children’s Clinical University Hospital due to severe COVID-19 disease. The patient’s clinical condition coincided with the derangement of biomarkers described in the literature, including lymphopenia, increased neutrophil/lymphocyte ratio (NLR), decreased lymphocyte/C-reactive protein ratio (LCR), as well as elevated inflammatory markers such as CRP and D-dimers. The patient developed severe bilateral pneumonia requiring invasive ventilation, high-flow oxygen, immunosuppressive therapy with dexamethasone and tocilizumab, and supplementation of anemia deficits with blood transfusion and vitamin B12 administration. Conclusions: Our findings are consistent with the most important biomarkers reported in the literature indicative of severe disease progression. Additionally, poorly controlled anemia may be suggested as a potentially important risk factor for severe COVID-19 disease among children. However, additional quantitative research is required to establish the nature and severity of the risk. Full article

An outbreak of pneumonia occurred on December 2019 in Wuhan, China, which caused a serious public health emergency by spreading around the globe. Globally, natural products are being focused on more than synthetic ones. So, keeping that in view, the current study was conducted to discover potential antiviral compounds from Allium sativum. Twenty-five phytocompounds of this plant were selected from the literature and databases including 3-(Allylsulphinyl)-L-alanine, Allicin, Diallyl sulfide, Diallyl disulfide, Diallyl trisulfide, Glutathione, L-Cysteine, S-allyl-mercapto-glutathione, Quercetin, Myricetin, Thiocysteine, Gamma-glutamyl-Lcysteine, Gamma-glutamylallyl-cysteine, Fructan, Lauricacid, Linoleicacid, Allixin, Ajoene, Diazinon Kaempferol, Levamisole, Caffeicacid, Ethyl linoleate, Scutellarein, and S-allylcysteine methyl-ester. Virtual screening of these selected ligands was carried out against drug target 3CL protease by CB-dock. Pharmacokinetic and pharmacodynamic properties defined the final destiny of compounds as drug or non-drug molecules. The best five compounds screened were Allicin, Diallyl Sulfide, Diallyl Disulfide, Diallyl Trisulfide, Ajoene, and Levamisole, which showed themselves as hit compounds. Further refining by screening filters represented Levamisole as a lead compound. All the interaction visualization analysis studies were performed using the PyMol molecular visualization tool and LigPlot+. Conclusively, Levamisole was screened as a likely antiviral compound which might be a drug candidate to treat SARS-CoV-2 in the future. Nevertheless, further research needs to be carried out to study their potential medicinal use. Full article

Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a “black swan”, is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak. Full article

An outbreak of a novel form of pneumonia due to a coronavirus occurred in Wuhan City, Hubei Province, China, in December 2019, and the disease was officially named coronavirus 2019 (COVID-19). COVID-19 is an infectious disease caused by the SARS-CoV-2 virus which attacks the human respiratory system. In critical cases of COVID-19, special treatment is administered in the ICU using a mechanical ventilator. However, since the results are less than optimal, additional interventions such as orienting the patient in the prone position can be employed, as this can reduce the pressure on the dorsal lung area so that alveolar recruitment can be improved. This study examines the effect of the prone position on increasing pulmonary ventilation. This study is a literature review study employing a narrative method and was conducted by tracing and analyzing articles relevant to the topic. This literature review searched three databases, namely, PubMed, Google Scholar, and ProQuest using the keywords “COVID-19 OR intubated COVID-19” AND “prone position OR mechanical ventilator” AND “Lung ventilation”. From these databases, 1062 journals were found and filtered based on inclusion and exclusion criteria, resulting in the inclusion of 10 journals. Then, they were filtered again using a critical appraisal instrument called “JBI Critical Appraisal”, which was conducted by two people, N.P. and A.S.; consequently, the results of 5 journals were obtained for further analysis; cumulatively, these papers represented a total population of 1789 patients. It was concluded that the prone position can improve pulmonary ventilation by up to 50% and reduce the total length of stay by up to 5 days in the hospital, as long as the patients do not possess any comorbid diseases. Full article

The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%. Full article

COVID-19 has attracted worldwide attention ever since the first case was identified in Wuhan (China) in December 2019 and was classified, at a later time, as a public health emergency of international concern in January 2020 and as a pandemic in March 2020. The interstitial pneumonia caused by COVID-19 often requires mechanical ventilation, which can lead to pulmonary barotrauma. We assessed the relationship between pneumonia severity and the development of barotrauma in COVID-19-positive patients mechanically ventilated in an intensive care unit; we therefore analyzed the prevalence of iatrogenic barotrauma and its trends over time during the pandemic in COVID-19-positive patients undergoing mechanical ventilation compared to COVID-19-negative patients, making a distinction between different types of ventilation (invasive mechanical ventilation vs. noninvasive mechanical ventilation). We compared CT findings of pneumomediastinum and pneumothorax in 104 COVID-19-positive patients hospitalized in an intensive care unit and 101 COVID-19-negative patients undergoing mechanical ventilation in the period between October 2020 and December 2021. The severity of pneumonia was not directly correlated with the development of barotrauma. Furthermore, a higher prevalence of complications due to barotrauma was observed in the group of mechanically ventilated COVID-19-postive patients vs. COVID-19-negative patients. A higher rate of barotrauma was observed in subgroups of COVID-19-positive patients undergoing mechanical ventilation compared to those treated with invasive mechanical ventilation. The prevalence of barotrauma in COVID 19-positive patients showed a decreasing trend over the period under review. CT remains an essential tool in the early detection, diagnosis, and monitoring of the clinical course of SARS-CoV2 pneumonia; in evaluating the disease severity; and in the assessment of iatrogenic complications such as barotrauma pathology. Full article

Background: After its 2019 outbreak in Wuhan, scientists worldwide have been studying the epidemiology and clinical characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in children. Evidence indicates that children with SARS-CoV-2 infection are more likely to develop upper and lower respiratory tract infections in association with other infectious agents, such as Mycoplasma pneumoniae. Here, we conducted a systematic review of SARS-CoV-2 and Mycoplasma pneumoniae co-infection and their clinical course in children. Methods: We evaluated the published literature on SARS-CoV-2 by using the medical databases PubMed, Embase, Cochrane Library, Scopus, and Web of Science. In the searches, the Medical Subject Heading (MeSH) terms “SARS-CoV-2 and Mycoplasma pneumoniae” AND “co-infection SARS-CoV-2” were used. Studies describing co-infection with SARS-CoV-2 and Mycoplasma pneumoniae in children were included in the review. The study was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: According to the PRISMA guidelines, of the 38 identified studies, 14 were conducted in children (children/adolescents 0–18 years), 6 of which were included in this review. In total, 5867 children under the age of 17 years were diagnosed with SARS-CoV-2 infection through real-time polymerase chain reaction analysis of nasopharyngeal swabs to detect viral RNA. Elevated serum IgM levels specific to Mycoplasma pneumoniae were observed in 534 children and were associated with a Kawasaki-like illness in one child. To date, all of the children are alive. Conclusion: This study underlines the importance of considering, depending on the clinical context, a possible co-infection between SARS-CoV-2 and atypical bacteria, such as Mycoplasma pneumoniae. Co-infections with other respiratory pathogens during the pandemic and hospital stay can cause mistakes in clinical diagnostic and drug treatment. Physicians should perform early differential diagnosis of SARS-CoV-2 in association with other infectious agents. Further studies are needed to have a real incidence of these co-infections and their impact on symptoms, course, and outcome of patients with SARS-CoV-2. Full article

At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR test kits, one of the main tools for detecting the virus, chest X-ray images are a popular tool for diagnosing the virus in patients with respiratory symptoms. Models based on deep learning are showing promising results in combating this pandemic. A novel convolutional neural network, FirecovNet, is suggested in this study that detects COVID-19 infection automatically based on raw chest X-ray images. With an architecture inspired by the integration of DarkNet and SqueezeNet networks, the proposed model has fewer parameters than state-of-the-art models and is trained using COVID-19, bacterial pneumonia, normal, lung opacity, and viral pneumonia images, which were collected from two public datasets and also are symmetric in the distribution in class. FirecovNet performance has been verified using the stratified 5-fold cross-validation method. A total of five classification tasks are performed, including four 4-class classifications, and one 5-class classification, and the accuracy of all tasks was at least 95.9%. For all classification tasks, the proposed network has demonstrated promising results in precision, sensitivity, and F1-score. Moreover, a comparison was made between the proposed network and eight deep transfer learning networks and in terms of accuracy, precision, sensitivity, F1-score, speed, and size of the saved model; FirecovNet was very promising. Therefore, FirecovNet can be useful as a tool for more accurate diagnosis of the COVID-19 virus, along with diagnostic tests, in situations where the number of specialist radiologists may be limited. Full article

Background. Infection caused by SARS-CoV-2 mostly affects the upper and lower respiratory tracts and causes symptoms ranging from the common cold to pneumonia with acute respiratory distress syndrome. Chemokines are deeply involved in the chemoattraction, proliferation, and activation of immune cells within inflammation. It is crucial to consider that mutations within the virion can potentially affect the clinical course of SARS-CoV-2 infection because disease severity and manifestation vary depending on the genetic variant. Our objective was to measure and assess the different concentrations of chemokines involved in COVID-19 caused by different variants of the virus. Methods. We used the blood plasma of patients infected with different variants of SARS-CoV-2, i.e., the ancestral Wuhan strain and the Alpha, Delta, and Omicron variants. We measured the concentrations of 11 chemokines in the samples: CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL7/MCP-3, CCL11/Eotaxin, CCL22/MDC, CXCL1/GROα, CXCL8/IL-8, CXCL9/MIG, CXCL10/IP-10, and CX3CL1/Fractalkine. Results. We noted a statistically significant elevation in the concentrations of CCL2/MCP-1, CXCL8/IL-8, and CXCL1/IP-10 independently of the variant, and a drop in the CCL22/MDC concentrations. Conclusions. The chemokine concentrations varied significantly depending on the viral variant, leading us to infer that mutations in viral proteins play a role in the cellular and molecular mechanisms of immune responses. Full article