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Respect for the scientific process and a diversity of views; open discourse and debate based on principles of ethics, best available evidence, and scientific inquiry and integrity; and an understanding of evidence gaps and uncertainty and how to communicate about them are important values in the advancement of science and the practice of medicine. Physicians often must make decisions about their recommendations to patients in the face of scarce or conflicting data. Are these characteristics of medicine and science widely understood and effectively communicated among members of the profession and to patients and the public? Issues of scientific integrity are longstanding, but COVID-19 brought them to the forefront, in an environment that was sometimes characterized by communication missteps as guidance came and went-or changed-quickly. Today, is open debate flourishing? Have some debates shed more heat than light? Are people losing confidence in science and medicine? In health care institutions? The American College of Physicians explores these issues and offers guidance in this position paper., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M24-0648.

Background: A new coronavirus was first identified in Wuhan, China in December 2019. Since the times of the 1918 influenza pandemic, malnutrition has been known as a risk factor for severity and mortality from viral pneumonia. Similarly, the recently identified SARS-Cov2 infection (COVID-19) and related pneumonia may be closely linked to malnutrition. Therefore, this study will contribute to new knowledge and awareness of the recording and evaluation of each COVID-19 patient's nutritional status by assessing the effect of malnutrition on ICU admission and death of COVID-19 patients in developing countries., Method: We conducted a prospective cohort study in adult COVID-19 patients admitted to selected COVID-19 Isolation and Treatment Centers, Addis Ababa, Ethiopia. Baseline data of the patients were collected using interviewer-administered structured questionnaire and data on the adverse outcomes of follow up were extracted from follow up chart. The main clinical outcomes (ICU admission and death) were captured every week of follow up. We ran a multivariate Cox's regression analysis to determine the relationship between malnutrition at admission and its effect on ICU admission and death., Results: A total of 581 COVID-19 patients were enrolled. From the total of recruited patients, 346 (59.6%) were males and 235 (40.4%) were females. The mean age of the respondents was 55 years (16.45) years. The Cox proportional hazard model controlled for sex, age group, number of co-morbidities, and number of medications found that malnutrition at admission was associated with ICU admission and death. When compared to well-nourished patients, the rate of ICU admission was significantly associated and found to be higher among underweight [(adjusted hazard ratio (AHR) = 10.02, 95% CI: (8.64-12.10)] and overweight [(AHR = 7.7, 95% CI: (6.41-9.62)] patients. The rate of survival probability was significantly associated and was found to be better among well-nourished patients (AHR = 0.06, 95% CI : (0.01-0.44) when compared with malnourished COVID-19 patients., Conclusion: Malnutrition at the time of admission was shown to increase the risk of ICU admission and mortality among COVID-19 patients. Therefore, it is vital to evaluate patients' nutritional condition early in their admission and provide timely intervention to minimize the effects on patients and the healthcare system., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Jima et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In the SARS-CoV-2 Omicron period, the pattern of pneumonia changed from primarily viral pneumonia to pneumonia mixed with bacteria in the elderly. We investigated functional outcomes at 1 year after hospital discharge in patients with primary Omicron pneumonia and pneumonia mixed with bacteria, mainly aspiration pneumonia. Functional decline rates calculated using the Barthel Index at 1 year after hospital discharge were significantly higher in the pneumonia mixed with bacteria group than the primary viral pneumonia group (42.6% vs. 20.5%, p  < 0.0001). It is necessary to consider early rehabilitation and treatment in elderly patients even when the predominant strain is the Omicron variant., Competing Interests: The authors declare that they have no competing interests., (© 2024 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Background: Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase radiographic changes., Objective: The study aims to employ a deep learning model distinguishing CMV pneumonia from COVID-19 pneumonia, community-acquired pneumonia, and normal lungs post-HSCT., Methods: Initially, 6 neural network models were pre-trained with COVID-19 pneumonia, community-acquired pneumonia, and normal lung CT images from Kaggle's COVID multiclass dataset (Dataset A), then Dataset A was combined with the CMV pneumonia images from our center, forming Dataset B. We use a few-shot transfer learning strategy to fine-tune the pre-trained models and evaluate model performance in Dataset B., Results: 34 cases of CMV pneumonia were found between January 2018 and December 2022 post-HSCT. Dataset A contained 1681 images of each subgroup from Kaggle. Combined with Dataset A, Dataset B was initially formed by 98 images of CMV pneumonia and normal lung. The optimal model (Xception) achieved an accuracy of 0.9034. Precision, recall, and F1-score all reached 0.9091, with an AUC of 0.9668 in the test set of Dataset B., Conclusions: This framework demonstrates the deep learning model's ability to distinguish rare pneumonia types utilizing a small volume of CT images, facilitating early detection of CMV pneumonia post-HSCT.

Introduction: At the beginning of December 2019, humanity has faced a new problem caused by coronavirus. In Hubei province of central China, epidemic events associated with severe primary viral pneumonia in humans began to develop. The isolated etiological agent was identified as a representative of Coronaviridae family. The global pandemic associated with the new coronavirus infection, acute respiratory syndrome type 2 (Severe acute respiratory syndrome 2, SARS-CoV-2), has become a challenge for humanity., Objective: In our work, we assessed the replicative ability and pathogenesis of the SARS-CoV-2 virus in hamsters., Materials and Methods: Syrian hamsters ( n =16) randomly divided into two groups were used in experiment. The first group was infected intranasally with the SARS-CoV-2 virus, strain SARS-CoV-2/human/KAZ/KZ&#95;Almaty/2020 deposited in GenBank under number MZ379258.1. The second group remained as a control group. Clinical manifestations of the disease in hamsters were observed within 14 days. Samples were collected on days 3, 5, 7, 9, 12, and 14 postinfection. The obtained samples were tested for viral isolation in cell culture, histological examination and analysis of viral RNA by RT-PCR., Results: SARS-CoV-2 virus isolates showed efficient replication in the lungs of hamsters, causing pathological lung lesions in animals infected intranasally. Clinical manifestations of the disease in hamsters infected with this virus were characterized by a decrease in temperature and body weight, wetness and ruffled fur, and frequent stroking of the nasal planum. High virus titers were observed following the virus isolation in cell cultures from nasal, oral swabs and lungs of animals infected intranasally. Pathological autopsy demonstrated pathological changes in the lungs. Moreover, transmission by airborne droplets has been established when a healthy hamster was kept together with animals infected using the intranasal method., Conclusion: In conclusion, our study showed that the Syrian hamster model is a useful tool for studying the SARS-CoV-2 pathogenesis, as well as testing vaccine candidates against acute respiratory syndrome type 2.

Purpose: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray., Methods: This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions., Results: The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area., Conclusion: The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images., (© 2023. The Author(s).)

This paper reports the proceedings of a meeting convened by the Research Group on Thoracic Ultrasound in Older People of the Italian Society of Gerontology and Geriatrics, to discuss the current state-of-the-art of clinical research in the field of geriatric thoracic ultrasound and identify unmet research needs and potential areas of development. In the last decade, point-of-care thoracic ultrasound has entered clinical practice for diagnosis and management of several respiratory illnesses, such as bacterial and viral pneumonia, pleural effusion, acute heart failure, and pneumothorax, especially in the emergency-urgency setting. Very few studies, however, have been specifically focused on older patients with frailty and multi-morbidity, who frequently exhibit complex clinical pictures needing multidimensional evaluation. At the present state of knowledge, there is still uncertainty on the best requirements of ultrasound equipment, methodology of examination, and reporting needed to optimize the advantages of thoracic ultrasound implementation in the care of geriatric patients. Other issues regard differential diagnosis between bacterial and aspiration pneumonia, objective grading of interstitial syndrome severity, quantification and monitoring of pleural effusions and solid pleural lesions, significance of ultrasonographic assessment of post-COVID-19 sequelae, and prognostic value of assessment of diaphragmatic thickness and motility. Finally, application of remote ultrasound diagnostics in the community and nursing home setting is still poorly investigated by the current literature. Overall, the presence of several open questions on geriatric applications of thoracic ultrasound represents a strong call to implement clinical research in this field., (© 2023. The Author(s).)

Rationale: Several authors have compared COVID-19 infection with influenza in the ICU., Objective: This study aimed to compare the baseline clinical profiles, care procedures, and mortality outcomes of patients admitted to the intensive care unit, categorized by infection status (Influenza vs. COVID-19)., Methods: Retrospective observational study. Data were extracted from the Toulouse University Hospital from March 2014 to March 2021. To compare survival curves, we plotted the survival at Day-90 using the Kaplan-Meier curve and conducted a log-rank test. Additionally, we performed propensity score matching to adjust for confounding factors between the COVID-19 and influenza groups. Furthermore, we use the CART model for multivariate analysis., Results: The study included 363 patients admitted to the ICU due to severe viral pneumonia: 152 patients (41.9 %) with influenza and 211 patients (58.1 %) with COVID-19. COVID-19 patients exhibited a higher prevalence of cardiovascular risk factors, whereas influenza patients had significantly higher severity scores (SOFA: 10 [6-12] vs. 6 [3-9], p<0.01 and SAPS II: 51 [35-67] vs. 37 [29-50], p<0.001). Overall mortality rates were comparable between the two groups (27.6 % (n = 42) in the influenza group vs. 21.8 % (n = 46) in the COVID-19 group, p=NS). Mechanical ventilation was more commonly employed in the influenza group (76.3 % (n = 116) vs. 59.7 % (n = 126), p<0.001); however, COVID-19 patients required longer durations of mechanical ventilation (18 [9-29] days vs. 13 [5-24] days, p<0.006) and longer hospital stays (23 [13-34] days vs. 18.5 [9-34.5] days, p = 0.009). The CART analysis revealed that the use of extra renal replacement therapy was the most influential prognostic factor in the influenza group, while the PaO2/FiO2-PEEP ratio played a significant role in the COVID-19 group., Conclusions: Despite differences in clinical presentation and prognostic factors, the mortality rates at 90 days, after adjusting for confounding factors, were similar between COVID-19 and influenza patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes. Everyone's lives have been significantly changed due to the COVID-19 outbreak. Identifying patients infected with COVID-19 in a timely manner is critical to overcoming this challenge. The Real-Time Polymerase Chain Reaction (RT-PCR) diagnostic assay is currently the gold standard for COVID-19 detection. However, RT-PCR is a time-consuming and costly technique requiring a lab kit that is difficult to get in crises and epidemics. This work suggests the CIDICXR-Net50 model, a ResNet-50-based Transfer Learning (TL) method for COVID-19 detection via Chest X-ray (CXR) image classification. The presented model is developed by substituting the final ResNet-50 classifier layer with a new classification head. The model is trained on 3,923 chest X-ray images comprising a substantial dataset of 1,360 viral pneumonia, 1,363 normal, and 1,200 COVID-19 CXR images. The proposed model's performance is evaluated in contrast to the results of six other innovative pre-trained models. The proposed CIDICXR-Net50 model attained 99.11% accuracy on the provided dataset while maintaining 99.15% precision and recall. This study also explores potential relationships between COVID-19 and Diabetes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Ahmad, Merla, Ali, Shah, AlZubi and AlZubi.)

Composition of pulmonary microbiome of patients with severe pneumonia is poorly known. The aim of this work was to analyse the lung microbiome of patients admitted to the intensive care unit  (ICU) with severe community acquired pneumonia (CAP) between 2019 and 2021 in comparison with a control group of 6 patients undergoing digestive surgery. As a second objective, the diagnostic capabilities of metagenomics was also studied in a small group of selected patients. The lung microbiome of patients with viral (5 with Influenza A and 8 with SARS-CoV-2) pneumonia at admission showed a similar diversity as the control group (p = 0.140 and p = 0.213 respectively). Contrarily, the group of 12 patients with pneumococcal pneumonia showed a significant lower Simpson´s index (p = 0.002). In the control group (n = 6) Proteobacteria (36.6%), Firmicutes (24.2%) and Actinobacteria (23.0%) were the predominant phyla. In SARS-CoV-2 patients (n = 8), there was a predominance of Proteobacteria (mean 41.6%) (Moraxella and Pelomonas at the genus level), Actinobacteria (24.6%) (Microbacterium) and Firmicutes (22.8%) mainly Streptococcus, Staphylococcus and Veillonella. In patients with Influenza A pneumonia (n = 5) there was a predominance of Firmicutes (35.1%) mainly Streptococcus followed by Proteobacteria (29.2%) (Moraxella, Acinetobacter and Pelomonas). In the group of pneumococcal pneumonia (n = 12) two phyla predominated: Firmicutes (53.1%) (Streptococcus) and Proteobacteria (36.5%) (Haemophilus). In the 7 patients with non-pneumococcal bacterial pneumonia Haemophilus influenzae (n = 2), Legionella pneumophila (n = 2), Klebsiella pneumoniae, Streptococcus pyogenes and Leptospira were detected by metagenomics, confirming the diagnosis done using conventional microbiological techniques. The diversity of the respiratory microbiome in patients with severe viral pneumonia at ICU admission was similar to that of the control group. Contrarily, patients with pneumococcal pneumonia showed a lower grade of diversity. At initial stages of SARS-CoV-2 infection, no important alterations in the pulmonary microbiome were observed. The analysis of bacterial microbiome showed promising results as a diagnostic tool., (© 2023. Springer Nature Limited.)

Background: Despite the life-threatening presentation of multisystem inflammatory syndrome in children (MIS-C), the overall prognosis is favourable in centres with access to appropriate supportive care. In this study, we investigate the short-term outcomes in children with MIS-C in Cape Town, South Africa., Methods: This prospective observational cohort study included children <13 years who fulfilled the WHO case definition of MIS-C and were admitted to Tygerberg Hospital in Cape Town, South Africa between 1 June 2020 and 31 October 2021. Clinical features were recorded at baseline and at follow-up at the 6-week cardiology and 3-month rheumatology-immunology clinics, respectively., Findings: Fifty-three children with a median age of 7.4 years (IQR 4.2-9.9) were included. There was a slight male predominance (30/53; 56.6%) and the majority was of mixed ancestry (28/53; 52.83%) or black African ancestry (24/53; 45.3%). Fourteen children (14/53; 26.4%) had comorbid disease. The median length of hospital stay was 8 days (IQR 6-10). All children had an echocardiogram performed at baseline of which 39 were abnormal (39/53; 73.6%). All children were discharged alive. The median days from discharge to cardiology follow-up was 39 days (IQR 33.5-41.5) and for rheumatology-immunology clinic was 70.5 days (IQR 59.5-85.0). Eleven children (11/41; 26.8%) had a persistently abnormal echocardiogram at cardiology follow-up. Systemic inflammation and organ dysfunction resolved in most., Interpretation: Although the short-term outcomes of MIS-C in our cohort were generally good, the cardiac morbidity needs further characterisation and follow-up., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The COVID-19 pandemic represented a challenge in medical care. A tool would be very useful to establish the prognosis of in-hospital death that is reliable and can be applied to the Mexican population entitled to the IMSS., Objective: To propose a prognostic scale to stratify patients with viral pneumonia COVID-19 in the emergency services., Material and Methods: A nested case-control study was conducted in a cohort of patients who were consecutively admitted to the emergency department with viral pneumonia COVID-19. The cases were those patients who died, and the controls were those who were discharged due to health improvement. An association analysis was performed between the variables with significant differences between groups. Subsequently, the association was adjusted using a multivariate logistic regression model, from which the prognostic scale was developed., Results: A total of 70 subjects with COVID-19 were included, 34 cases and 36 controls. Chronic diseases, smoking, severe pulmonary involvement diagnosed by tomography, leukocytosis, and pulse oximetry less than 80% with were associated with in-hospital mortality; Odds Ratio (OR) of >1.1. Vaccination was a protective factor (OR = 0.04, CI95%: 0.01-0.16). A score greater than 3 points on the prognostic scale predicts in-hospital mortality with a specificity of 0.86 and a sensitivity of 0.73., Conclusions: The proposed prognostic scale can be a useful tool in the classification of patients with COVID-19 viral pneumonia in the emergency room services of secondary care level Hospitals., (Licencia CC 4.0 (BY-NC-ND) © 2023 Revista Médica del Instituto Mexicano del Seguro Social.)

The airway epithelium, which lines the conducting airways, is central to the defense of the lungs against inhaled particulate matter and pathogens such as SARS-CoV-2, the virus that causes COVID-19. Recognition of pathogens results in the activation of an innate and intermediate immune response which involves the release of cytokines and chemokines by the airway epithelium. This response can inhibit further viral invasion and influence adaptive immunity. However, severe COVID-19 is characterized by a hyper-inflammatory response which can give rise to clinical presentations including lung injury and lead to acute respiratory distress syndrome, viral pneumonia, coagulopathy, and multi-system organ failure. In response to SARS-CoV-2 infection, the airway epithelium can mount a maladaptive immune response which can delay viral clearance, perpetuate excessive inflammation, and contribute to the pathogenesis of severe COVID-19. In this article, we will review the barrier and immune functions of the airway epithelium, how SARS-CoV-2 can interact with the epithelium, and epithelial-derived cytokines and chemokines and their roles in COVID-19 and as biomarkers. Finally, we will discuss these immune mediators and their potential as therapeutic targets in COVID-19.

Objective: Belgium has been hit harder by COVID-19 than other countries in Europe. While clinical risk factors are well studied, socioeconomic risk factors remained underexplored. This study's objective was to analyse the social and clinical profile of patients hospitalised for COVID-19 during the two waves of 2020, compared with a control population in 2019 in two hospitals located in Brussels' most deprived area., Design and Methods: We did a case-control study by using the minimal clinical data set in two Brussels hospitals. All patients hospitalised for COVID-19 in 2020, divided into two waves (n=3220), were compared with all patients hospitalised for viral pneumonia and respiratory diseases in 2019 (control population n=2950). Multinomial regression models were used to estimate the relative risk ratios of the association between the COVID-19 hospitalised populations (waves 1 and 2) and risk factors (social and clinical) stratified by age., Results: Patients under 65 years of age and hospitalised for COVID-19 presented significantly higher rates (relative rate ratio (95% CI)), especially for the first wave, of obesity 1.6 (1.2-2.2), African nationalities 1.4 (1.0-1.8), lack of health insurance 1.6 (1.3-2.1), living in high-density population areas 1.6 (1.3-2.1) and low incomes 1.7 (1.4-2.1), compared with the control population For patients over 65 years of age, we did not observe significant excess of COVID-19 hospitalisations for any risk factors, except diabetes during for the second wave but we have a significant excess mortality rate than the control population for both waves (p<0.002)., Conclusions: The social and clinical profile of patients hospitalised for COVID-19 compared with a population hospitalised for viral respiratory diseases differed between age groups and waves. For younger patients, risk factors were linked to patients' precarious situations. This study underlines the role of selected social health determinants and the importance of routinely collecting social data, along with clinical data, particularly among vulnerable populations., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Low-dose radiation therapy (LDRT) can suppress intractable inflammation, such as that in rheumatoid arthritis, and is used for treating more than 10,000 rheumatoid arthritis patients annually in Europe. Several recent clinical trials have reported that LDRT can effectively reduce the severity of coronavirus disease (COVID-19) and other cases of viral pneumonia. However, the therapeutic mechanism of LDRT remains unelucidated. Therefore, in the current study, we aimed to investigate the molecular mechanism underlying immunological alterations in influenza pneumonia after LDRT. Mice were irradiated to the whole lung 1 day post-infection. The changes in levels of inflammatory mediators (cytokines and chemokines) and immune cell populations in the bronchoalveolar lavage (BALF), lungs, and serum were examined. LDRT-treated mice displayed markedly increased survival rates and reduced lung edema and airway and vascular inflammation in the lung; however, the viral titers in the lungs were unaffected. Levels of primary inflammatory cytokines were reduced after LDRT, and transforming growth factor-β (TGF-β) levels increased significantly on day 1 following LDRT. Levels of chemokines increased from day 3 following LDRT. Additionally, M2 macrophage polarization or recruitment was increased following LDRT. We found that LDRT-induced TGF-β reduced the levels of cytokines and polarized M2 cells and blocked immune cell infiltration, including neutrophils, in BALF. LDRT-induced early TGF-β production was shown to be a key regulator involved in broad-spectrum anti-inflammatory activity in virus-infected lungs. Therefore, LDRT or TGF-β may be an alternative therapy for viral pneumonia., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Song, Chen, Park, Han, Ji, Byun, Kwon, Kim, Ahn and Seo.)

Objective: COVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet., Methods: The proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal., Result: The test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively., Conclusion: The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset., (© 2023 The Authors. The Clinical Respiratory Journal published by John Wiley & Sons Ltd.)

Background: Patients over 70 years old represent a substantial proportion of the COVID-19 ICU population and their mortality rates are high. The aim of this study is to describe the outcomes of patients ≥70 years old admitted to Dutch ICUs with COVID-19, compared to patients ≥70 years old admitted to the ICU for bacterial and other viral pneumonias, with adjustments for age, comorbidities, severity of illness, and ICU occupancy rate., Methods: Retrospective cohort study including patients ≥70 years old admitted to Dutch ICUs, comparing patients admitted with COVID-19 from March 1st 2020 to January 1st 2022 with patients ≥70 years old admitted because of a bacterial and other viral pneumonia, both divided in a historical (i.e., January 1st 2017 to January 1st 2020) and current cohort (i.e., March 1st 2020 to January 1st 2022). Primary outcome is hospital mortality., Results: 11,525 unique patients ≥70 years old admitted to Dutch ICUs were included; 5094 with COVID-19, 5334 with a bacterial pneumonia, and 1312 with another viral pneumonia. ICU-mortality and in-hospital mortality rates of the patients ≥70 years old admitted with COVID-19 were 39.7% and 47.6% respectively. ICU- and hospital mortality rates of the patients who were admitted in the same or in an historical time period with a bacterial pneumonia or other viral pneumonias were considerably lower (19.5% and 28.6% for patients with a bacterial pneumonia in the historical cohort and 19.1% and 28.8% in the same period, for the patients with other viral pneumonias 20.7% and 28.9%, and 22.7% and 31.8% respectively, all p < 0.001). Differences persisted after correction for several clinical characteristics and ICU occupancy rate., Conclusions: In ICU-patients ≥70 years old, COVID-19 is more severe compared to bacterial or viral pneumonia., (© 2023 The American Geriatrics Society.)

Chronic sequelae of COVID-19 remain undetermined. We report a case of postinfection sequelae in a patient presenting with subacute obstruction 2 months after COVID-19 infection. A 34-year-old man with a prior prolonged hospital stay due to COVID-19 complicated by upper gastrointestinal (GI) bleed presented with subacute obstruction and failure to thrive. Upper GI push enteroscopy revealed residual ulcers and multiple proximal jejuno-jejunal fistulae. Midline laparotomy revealed strictures with dense intra-abdominal adhesions, a large jejuno-jejunal fistula, and evidence of prior jejunal perforation following severe COVID-19 infection. The patient recovered after small bowel resection with anastomoses and was discharged home. Histopathological examination of resected specimen confirmed transmural infarction with evidence of prior hemorrhage, diffuse ulcers, and multifocal inflammation. This is the first report of a chronic GI sequelae resulting from COVID-19. As the pandemic evolves, medical professionals must be vigilant to consider alternative GI diagnoses in the COVID-19 survivors.

Background: In young children, rates of lower respiratory infections (LRI) and invasive pneumococcal disease (IPD) have been associated with respiratory syncytial virus (RSV), human metapneumovirus (hMPV), influenza (flu), and parainfluenza (PIV) (collectively termed here as pneumonia and pneumococcal disease-associated viruses [PDA-viruses]). However, their contribution to the pathogenesis of these disease endpoints has not yet been elucidated. The COVID-19 pandemic provided a unique opportunity to examine the question., Methods: This prospective study comprised all children <5 years, living in southern Israel, during 2016 through 2021. The data were previously collected in multiple ongoing prospective surveillance programs and include: hospital visits for community-acquired alveolar pneumonia (CAAP), non-CAAP LRI; nasopharyngeal pneumococcal carriage (<3 years of age); respiratory virus activity; and nationwide, all-ages COVID-19 episodes and IPD in children <5 years. A hierarchical statistical model was developed to estimate the proportion of the different clinical endpoints attributable to each virus from monthly time series data, stratified by age and ethnicity. A separate model was fit for each endpoint, with covariates that included a linear time trend, 12-month harmonic variables to capture unexplained seasonal variations, and the proportion of tests positive for each virus in that month., Findings: During 2016 through 2021, 3,204, 26,695, 257, and 619 episodes of CAAP, non-CAAP LRI, pneumococcal bacteremic pneumonia and non-pneumonia IPD, respectively, were reported. Compared to 2016-2019, broad declines in the disease endpoints were observed shortly after the pandemic surge, coincident with a complete disappearance of all PDA-viruses and continued circulation of rhinovirus (RhV) and adenovirus (AdV). From April 2021, off-season and abrupt surges of all disease endpoints occurred, associated with similar dynamics among the PDA-viruses, which re-emerged sequentially. Using our model fit to the entire 2016-2021 period, 82% (95% CI, 75-88%) of CAAP episodes in 2021 were attributable to the common respiratory viruses, as were 22%-31% of the other disease endpoints. Virus-specific contributions to CAAP were: RSV, 49% (95% CI, 43-55%); hMPV, 13% (10-17%); PIV, 11% (7-15%); flu, 7% (1-13%). RhV and AdV did not contribute. RSV was the main contributor in all endpoints, especially in infants. Pneumococcal carriage prevalence remained largely stable throughout the study., Interpretation: RSV and hMPV play a critical role in the burden of CAAP and pneumococcal disease in children. Interventions targeting these viruses could have a secondary effect on the burden of disease typically attributed to bacteria., Funding: There was no funding for this study., Competing Interests: Declaration of interests RD has received grants from Pfizer, MSD, MedImmune/AstraZenaca. He serves as a scientific consultant and on the advisory board of Pfizer and MSD. He also is part of the speakers' bureau of Pfizer, MSD, Sanofi Pasteur, and GSK. SB-S has received grants from Pfizer and serves as a scientific consultant and on the advisory board of Pfizer and MSD. He also is part of the speakers' bureau of Pfizer, MSD, and GSK. DG has received grants from MSD, and he serves as scientific consultant for Pfizer, MSD, and GSK where he is also part of the speakers' bureau. DMW has received consulting fees from Pfizer, Merck, Affinivax, Matrivax, and GSK and is principal investigator on grants from Pfizer and Merck to Yale University. DD has received grants from Pfizer., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Little is known about the risk of multisystem inflammatory syndrome in children (MIS-C) with different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. In southeast England, MIS-C rates per confirmed SARS-CoV-2 infections in children aged 0-16 years were 56% lower (rate ratio [RR], 0.34 [95% confidence interval {CI}, .23-.50]) during prevaccine Delta, 66% lower (RR, 0.44 [95% CI, .28-.69]) during postvaccine Delta, and 95% lower (RR, 0.05 [95% CI, .02-.10]) during the Omicron period., Competing Interests: Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Lung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks., Methods: This paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch. After the actual analysis of the imbalanced medical image data, the key target domain images are selected, the key migration branch is established, and a single generator is used to complete the medical image multi-domain translation. The conversion between domains ensures the attention performance of the medical image multi-domain translation model and the quality of the synthesized images. At the same time, a lung image classification model based on synthetic image data augmentation is proposed. The synthetic lung CT medical images and the original real medical images are used as the training set together to study the performance of the auxiliary diagnosis model in the classification of normal healthy subjects, and also of the mild and severe COVID-19 patients., Results: Based on the chest CT image dataset, MI-GAN has completed the mutual conversion and generation of normal lung images without disease, viral pneumonia and Mild COVID-19 images. The synthetic images GAN-test and GAN-train indicators reached, respectively 92.188% and 85.069%, compared with other generative models in terms of authenticity and diversity, there is a considerable improvement. The accuracy rate of pneumonia diagnosis of the lung image classification model is 93.85%, which is 3.1% higher than that of the diagnosis model trained only with real images; the sensitivity of disease diagnosis is 96.69%, a relative improvement of 7.1%. 1%, the specificity was 89.70%; the area under the ROC curve (AUC) increased from 94.00% to 96.17%., Conclusion: In this paper, a multi-domain translation model of medical images based on the key transfer branch is proposed, which enables the translation network to have key transfer and attention performance. It is verified on lung CT images and achieved good results. The required medical images are synthesized by the above medical image translation model, and the effectiveness of the synthesized images on the lung image classification network is verified experimentally., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest., (Copyright © 2022. Published by Elsevier B.V.)

Background: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19)., Methods: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L)., Results: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4., Conclusions: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions., (© 2023. The Author(s).)

Abstract: Coronavirus 2 is an infectious agent primarily identified as the cause of a pandemic viral pneumonia. With the mass vaccination against this virus, one of the health issues is the safety of currently available vaccines considering their adverse reactions. This systematic review was conducted to assess and summarize all reported data on histopathologic findings associated with mucocutaneous reactions that developed after COVID-19 vaccination for a better pathophysiology interpretation and clinical management of these reactions. A systematic search was performed in PubMed, Web of Science, and Scopus databases as well as Google Scholar engine for relevant English articles published till July 1, 2022. This review includes 131 studies with a total number of 287 cases. Eruptions that underwent a biopsy were mostly described as erythematous maculopapular, papulosquamous, vasculitis-like, lichenoid, or urticarial lesions. Histopathology revealed spongiosis, interstitial, and perivascular lymphohistiocytic infiltration, erythrocyte extravasation, parakeratosis, endothelial inflammation, and the like. Findings were highly consistent with morbilliform erythema, psoriasiform dermatosis, leukocytoclastic vasculitis, and lichenoid or urticarial drug reactions. The majority of these reactions had a mild nature and were primarily observed in patients with underlying health conditions. Microscopic evaluation was also consistent with transient inflammatory changes, and features like neutrophilic infiltrates, subcorneal pustules, and vasculopathy were less frequently reported than what seen in COVID infection. Therefore, dermatologic reactions developing after vaccination in the general population should not hinder a complete vaccination., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

This study compared the immune response in mild versus fatal SARS-CoV2 infection. Forty nasopharyngeal swabs with either productive mild infection (n = 20) or negative for SARS-CoV2 (n = 20) were tested along with ten lung sections from people who died of COVID-19 which contained abundant SARS-CoV2 and ten controls. There was a 25-fold increase in the CD3+T cell numbers in the viral positive nasopharyngeal swabs compared to the controls (p < 0.001) and no change in the CD3+T cell count in the fatal COVID-19 lungs versus the controls. CD11b + and CD206+ macrophage counts were significantly higher in the mild versus fatal disease (p = 0.002). In situ analysis for SARS-CoV2 RNA found ten COVID-19 lung sections that had no/rare detectable virus and also lacked the microangiopathy typical of the viral positive sections. These viral negative lung tissues when compared to the viral positive lung samples showed a highly significant increase in CD3+ and CD8 T cells (p < 0.001), equivalent numbers of CD163+ cells, and significantly less PDL1, CD11b and CD206+ cells (p = 0.002). It is concluded that mild SARS-CoV2 infection is marked by a much stronger CD3/CD8 T cell, CD11b, and CD206 macrophage response than the fatal lung disease where viral RNA is abundant., Competing Interests: Declaration of competing interest The authors have no conflict of interests to report., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.

Currently, several convolutional neural network (CNN)-based methods have been proposed for computer-aided COVID-19 diagnosis based on lung computed tomography (CT) scans. However, the lesions of pneumonia in CT scans have wide variations in appearances, sizes, and locations in the lung regions, and the manifestations of COVID-19 in CT scans are also similar to other types of viral pneumonia, which hinders the further improvement of CNN-based methods. Delineating infection regions manually is a solution to this issue, while excessive workload of physicians during the epidemic makes it difficult for manual delineation. In this article, we propose a CNN called dense connectivity network with parallel attention module (PAM-DenseNet), which can perform well on coarse labels without manually delineated infection regions. The parallel attention module automatically learns to strengthen informative features from both channelwise and spatialwise simultaneously, which can make the network pay more attention to the infection regions without any manual delineation. The dense connectivity structure performs feature maps reuse by introducing direct connections from previous layers to all subsequent layers, which can extract representative features from fewer CT slices. The proposed network is first trained on 3530 lung CT slices selected from 382 COVID-19 lung CT scans, 372 lung CT scans infected by other pneumonia, and 200 normal lung CT scans to obtain a pretrained model for slicewise prediction. We then apply this pretrained model to a CT scans dataset containing 94 COVID-19 CT scans, 93 other pneumonia CT scans, and 93 normal lung scans, and achieve patientwise prediction through a voting mechanism. The experimental results show that the proposed network achieves promising results with an accuracy of 94.29%, a precision of 93.75%, a sensitivity of 95.74%, and a specificity of 96.77%, which is comparable to the methods that are based on manually delineated infection regions.

Background: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients., Methods: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers., Findings: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses., Interpretation: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated., Funding: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS)., Competing Interests: Declaration of interests PGL- Present work: efforts were supported by, BJC HealthCare Healthcare Innovation Lab “Big Ideas” award, Doris Duke Charitable Foundation, and Fund to Retain Clinical Scientists, # 2015215. Outside scope of present work- CDC MMC- Outside scope of present work, grants to institution: DOD PRMRP W81XWH-21-1-0009, NIH/ NIDDK R01-DK126933A -01, NIH/ NIGMS R35-13362546, NIH/NIGMS, R01-GM123193. Patent pending ARCD. P0535US.P2. APM- Outside scope of present work: Pfizer personal stock ownership. TK- Outside scope of present work: Grants: NIA, AHRQ, NLM, NCATS, NIMH; Licenses: Springer, Elsevier; Consultant: Pfizer, Inc; Presentations and Events: Department of Defense. CCS- Outside scope of present work: Grants: NIH, Department of Defense, Roche-Genentech, Quantum Leap Healthcare Collaborative; Consultant: Vasomune, Gen1e Life Sciences, Cellenkos, Janssen. SLW. AD, KD, TMP, SVB, AB, PS, EHG, BNR, MK, and AM have no conflicts of interest to report., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

While immunocompromised patients are at very high risk of developing severe COVID 19, few of them have been enrolled in studies aimed at evaluating treatments. In the early stages of research on this disease, glucocorticoid therapy became the standard of care for patients requiring oxygen supplementation. It has been demonstrated that the neutralizing monoclonal antibody combination of Casirivimab and Imdevimab reduced (by 28 days) mortality in COVID-19 patients admitted to hospital who were seronegative at baseline, but not in those who were seropositive. There is still a need to determine the place of available various antivirals (Molnupiravir or Nirmatrelvir plus Ritonavir) and passive immunotherapies (Sotrovimab…) as well as convalescent plasma therapy in immunocompromised settings., Competing Interests: Declaration of interest The authors declare no conflict of interest, (Copyright © 2022. Published by Elsevier Masson SAS.)

Coronavirus disease 2019 (COVID-19) affects millions of people worldwide. Clinical manifestations range from asymptomatic to severe viral pneumonia. CVID patients with COVID-19 infection are not adequately studied. In some studies, CVID patients had higher mortality rates, although other studies showed that CVID patients might have an uncomplicated COVID-19 infection. We describe 14 cases of COVID-19 infection in Iranian CVID patients in this study, including clinical manifestations, laboratory findings, and treatment strategies. There were 29% of patients with mild disease, 43% with moderate disease, and 29% with severe disease in this study. A critical case and a death occurred in none of our patients. There were six cases of infection more than two weeks after receiving the second dose of Sinopharm BIBP COVID-19 vaccine; all had mild to moderate disease. Among these patients, Remdesivir was the most frequently prescribed medication. According to this study, most of our patients presented with an uncomplicated disease course.

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.

Despite the advancement of vaccination and therapies currently available, deaths due to the coronavirus disease 2019 (COVID-19) are still heavily documented. Severely infected individuals experience a generalized inflammatory storm, caused by massive secretion of pro-inflammatory cytokines that can lead to endothelial dysfunction, cardiovascular disease, multi-organ failure, and even death. COVID-19 convalescent plasma (CCP) therapy, selected primarily based on anti-SARS-CoV-2 antibody levels, has not been as convincing as expected in the fight against COVID-19. Given the consequences of a dysfunctional endothelium on the progression of the disease, we propose that the selection of plasma for CCP therapy should be based on more specific parameters that take into consideration the effect on vascular inflammation. Thus, in the present study, we have characterized a subset of CCP that have been used for CCP therapy and measured their anti- or pro-inflammatory effect on human coronary artery endothelial cells (HCAECs). Our data revealed that the longer the time lapse between the onset of symptoms and the plasma donation, the more mitochondrial dysfunction can be evidenced. The concentration of blood endothelial cell extracellular vesicles (BEC-EVs) was increased in the plasma of young individuals with mild symptoms. This type of selected convalescent plasma promoted the activation of the blood vascular endothelium, as reflected by the overexpression of ICAM1 and NFκB1 and the downregulation of VE-Cadherin. We propose this mechanism is a warning signal sent by the injured endothelium to trigger self-defense of peripheral blood vessels against excessive inflammation. Therefore, these results are in line with our previous data. They suggest that a more specific selection of COVID-19 convalescent plasma should be based on the time of donation following the onset of the clinical symptoms of the donor, the severity of the symptoms, and the age of the donor. These characteristics are relatively easy to identify in any hospital and would reflect the concentration of plasma BEC-EVs and be optimal in CCP therapy.

Objective: The majority of children experience a mild course of acute Coronavirus Disease 2019 (COVID-19). Only few studies have looked at long-term recovery from COVID-19 infection in children. The purpose of this study was to identify the predictors of long-COVID by performing a thorough analysis of the clinical, laboratory, and demographic characteristics of children with COVID-19., Patients and Methods: Between August and October 2021, data were obtained retrospectively from the medical records of 251 children diagnosed with COVID-19 at a tertiary single-center hospital. The prognostic effects of admission-related factors were compared between patients who experienced long-lasting symptoms and those who did not., Results: Long-COVID symptoms were noted in 12.4% of patients. Joint pain (7.6%), lumbago (4.8%), and headache (3.2%) were the most common symptoms. The mean onset of long-COVID symptoms was 1.35±0.49 months. The onset of long-COVID symptoms was 4 weeks after initial diagnosis in 64.5% of patients and 4-8 weeks later in 35.5% of the patients. The mean duration of long-COVID symptoms was 5.32±2.51 months. Children with long-COVID had higher leukocytes, neutrophils, monocytes, basophils, platelets, and D-dimer when compared with patients without long-COVID (p < 0.001). Leukocytes, neutrophils, monocytes, platelets, and D-dimer had the highest AUC in the ROC analysis (0.694, 0.658, 0.681, 0.667, and 0.612, respectively) and were statistically significant., Conclusions: Despite the majority of children with COVID-19 having mild or asymptomatic acute disease, the majority of long-COVID symptoms were associated with functional impairment between 1 and 9 months after the start of the infection. Increased leukocytes, monocytes, neutrophils, platelets, and D-dimer appear to be the most powerful laboratory predictors for long-COVID and monitoring these predictors may assist clinicians to identify and follow-up patients with higher risk for long-COVID.

Purpose: To evaluate the management of children with severe gastrointestinal symptoms during the disease course of COVID-19 and multisystem inflammatory syndrome (MIS-C)., Methods: After ethical approval, we reviewed the medical records, retrospectively, of children with COVID-19 or MIS-C requiring surgical consultation for severe gastrointestinal symptoms., Results: The subjects comprised 15 children, 13 with MIS-C and 2 with COVID-19. Twelve children (80%) had been in known close contact with a person with SARS-CoV-19 and 13 were positive for Anti-SARS-CoV-2 IgG. All the children had experienced fever for at least 1 day and had signs of involvement of two or more systems. Three patients required surgical intervention: one underwent surgical exploration with a presumptive diagnosis of acute appendicitis in the referring center and was transported to our center following clinical deterioration, where a diagnosis of MIS-C was confirmed; and the remaining two developed appendicitis during hospitalization for COVID-19. All three patients had a longer duration of abdominal pain, a higher number of lymphocytes, and a lower level of inflammatory markers than the non-surgically managed patients. None of the patients presenting with MIS-C underwent surgical exploration., Conclusion: Gastrointestinal involvement may mimic acute abdomen in children with COVID-19. Thus, children presenting with acute abdomen in the pandemic era require careful evaluation and prompt diagnosis to avoid unnecessary surgical intervention., (© 2022. The Author(s) under exclusive licence to Springer Nature Singapore Pte Ltd.)

Background: Convalescent plasma (CP) has been widely used to treat COVID-19 and is under study. However, the variability in the current clinical trials has averted its wide use in the current pandemic. We aimed to evaluate the safety and efficacy of CP in severe coronavirus disease 2019 (COVID-19) in the early stages of the disease., Methods: A randomized controlled clinical study was conducted on 101 patients admitted to the hospital with confirmed severe COVID-19. Most participants had less than 14 days from symptoms onset and less than seven days from hospitalization. Fifty patients were assigned to receive CP plus standard therapy (ST), and 51 were assigned to receive ST alone. Participants in the CP arm received two doses of 250 mL each, transfused 24 h apart. All transfused plasma was obtained from "super donors" that fulfilled the following criteria: titers of anti-SARS-CoV-2 S1 IgG ≥ 1:3200 and IgA ≥ 1:800 antibodies. The effect of transfused anti-IFN antibodies and the SARS-CoV-2 variants at the entry of the study on the overall CP efficacy was evaluated. The primary outcomes were the reduction in viral load and the increase in IgG and IgA antibodies at 28 days of follow-up. The per-protocol analysis included 91 patients., Results: An early but transient increase in IgG anti-S1-SARS-CoV-2 antibody levels at day 4 post-transfusion was observed (Estimated difference [ED], - 1.36; 95% CI, - 2.33 to - 0.39; P = 0.04). However, CP was not associated with viral load reduction in any of the points evaluated. Analysis of secondary outcomes revealed that those patients in the CP arm disclosed a shorter time to discharge (ED adjusted for mortality, 3.1 days; 95% CI, 0.20 to 5.94; P = 0.0361) or a reduction of 2 points on the WHO scale when compared with the ST group (HR adjusted for mortality, 1.6; 95% CI, 1.03 to 2.5; P = 0.0376). There were no benefits from CP on the rates of intensive care unit admission (HR, 0.82; 95% CI, 0.35 to 1.9; P = 0.6399), mechanical ventilation (HR, 0.66; 95% CI, 0.25 to 1.7; P = 0.4039), or mortality (HR, 3.2; 95% CI, 0.64 to 16; P = 0.1584). Anti-IFN antibodies and SARS-CoV-2 variants did not influence these results., Conclusion: CP was not associated with viral load reduction, despite the early increase in IgG anti-SARS-CoV-2 antibodies. However, CP is safe and could be a therapeutic option to reduce the hospital length of stay. Trial registration NCT04332835., (© 2022. The Author(s).)

Background: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam., Method: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity., Result: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models' predictive capabilities., Conclusion: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes., (© 2022. The Author(s).)

New variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are continuing to spread globally, contributing to the persistence of the COVID-19 pandemic. Increasing resources have been focused on developing vaccines and therapeutics that target the Spike glycoprotein of SARS-CoV-2. Recent advances in microfluidics have the potential to recapitulate viral infection in the organ-specific platforms, known as organ-on-a-chip (OoC), in which binding of SARS-CoV-2 Spike protein to the angiotensin-converting enzyme 2 (ACE2) of the host cells occurs. As the COVID-19 pandemic lingers, there remains an unmet need to screen emerging mutations, to predict viral transmissibility and pathogenicity, and to assess the strength of neutralizing antibodies following vaccination or reinfection. Conventional detection of SARS-CoV-2 variants relies on two-dimensional (2-D) cell culture methods, whereas simulating the micro-environment requires three-dimensional (3-D) systems. To this end, analyzing SARS-CoV-2-mediated pathogenicity via microfluidic platforms minimizes the experimental cost, duration, and optimization needed for animal studies, and obviates the ethical concerns associated with the use of primates. In this context, this review highlights the state-of-the-art strategy to engineer the nano-liposomes that can be conjugated with SARS-CoV-2 Spike mutations or genomic sequences in the microfluidic platforms; thereby, allowing for screening the rising SARS-CoV-2 variants and predicting COVID-19-associated coagulation. Furthermore, introducing viral genomics to the patient-specific blood accelerates the discovery of therapeutic targets in the face of evolving viral variants, including B1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), c.37 (Lambda), and B.1.1.529 (Omicron). Thus, engineering nano-liposomes to encapsulate SARS-CoV-2 viral genomic sequences enables rapid detection of SARS-CoV-2 variants in the long COVID-19 era., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Objective: The aim is to characterise early and late respiratory and bloodstream co-infection in patients admitted to intensive care units (ICUs) with SARS-CoV-2-related acute hypoxemic respiratory failure (AHRF) needing respiratory support in seven ICUs within Wales, during the first wave of the COVID-19 pandemic. We compare the rate of positivity of different secondary pathogens and their antimicrobial sensitivity in three different patient groups: patients admitted to ICU with COVID-19 pneumonia, Influenza A or B pneumonia, and patients without viral pneumonia., Design: Multicentre, retrospective, observational cohort study with rapid microbiology data from Public Health Wales, sharing of clinical and demographic data from seven participating ICUs., Setting: Seven Welsh ICUs participated between 10 March and 31 July 2020. Clinical and demographic data for COVID-19 disease were shared by each participating centres, and microbiology data were extracted from a data repository within Public Health Wales. Comparative data were taken from a cohort of patients without viral pneumonia admitted to ICU during the same period as the COVID-19 cohort (referred to as no viral pneumonia or 'no viral' group), and to a retrospective non-matched cohort of consecutive patients with Influenza A or B admitted to ICUs from 20 November 2017. The comparative data for Influenza pneumonia and no viral pneumonia were taken from one of the seven participating ICUs., Participants: A total of 299 consecutive patients admitted to ICUs with COVID-19 pneumonia were compared with 173 and 48 patients admitted with no viral pneumonia or Influenza A or B pneumonia, respectively., Main Outcome Measures: Primary outcome was to calculate comparative incidence of early and late co-infection in patients admitted to ICU with COVID-19, Influenza A or B pneumonia and no viral pneumonia. Secondary outcome was to calculate the individual group of early and late co-infection rate on a per-patient and per-sample basis, with their antimicrobial susceptibility and thirdly to ascertain any statistical correlation between clinical and demographic variables with rate of acquiring co-infection following ICU admission., Results: A total of 299 adults (median age 57, M/F 2:1) were included in the COVID-19 ICU cohort. The incidence of respiratory and bloodstream co-infection was 40.5% and 15.1%, respectively. Staphylococcus aureus was the predominant bacterial pathogen within the first 48 h. Gram-negative organisms from Enterobacterales group were predominantly seen after 48 h in COVID-19 cohort. Comparative no viral pneumonia cohort had lower rates of respiratory tract infection and bloodstream infection. The influenza cohort had similar rates respiratory tract infection and bloodstream infection. Mortality in all three groups was similar, and no clinical or demographic variables were found to increase the rate of co-infection and ICU mortality., Conclusions: Higher incidence of bacterial co-infection was found in COVID-19 cohort as compared to the no viral pneumonia cohort admitted to ICUs for respiratory support., (© 2022. The Author(s).)

Objective and Design: The existing biological models of diffuse alveolar damage (DAD) in mice have many shortcomings. To offset these shortcomings, we have proposed a simple, nonsurgical, and reproducible method of unilateral total damage of the left lung in ICR mice. This model is based on the intrabronchial administration of a mixture of bacterial lipopolysaccharide (LPS) from the cell wall of S. enterica and α-galactosylceramide (inducing substances) to the left lung., Methods: Using computer tomography of the lungs with endobronchial administration of contrast material, we have been able to perform an operative intravital verification of the targeted delivery of the inducer. The model presented is characterized by more serious and homogeneous damage of the affected lung compared to the existing models of focal pneumonia; at the same time, our model is characterized by longer animal survival since the right lung remains intact., Results: The model is also characterized by diffuse alveolar damage of the left lung, animal survival of 100%, abrupt increases in plasma levels of TNFa, INFg, and IL-6, and significant myocardial overload in the right heart. It can be used to assess the efficacy of innovative drugs for the treatment of DAD and ARDS as the clinical manifestations that are developed in patients infected with SARS-CoV-2. Morphological patterns of lungs in the noninfectious ("sterile") model of DAD induced by LPS simultaneously with α-galactosylceramide (presented here) and in the infectious model of DAD induced by SARS-CoV-2 have been compared., Conclusion: The DAD model we have proposed can be widely used for studying the efficacy of candidate molecules for the treatment of infectious respiratory diseases, such as viral pneumonias of different etiology, including SARS-CoV-2., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objectives: Different biomarkers such as C-reactive protein (CRP), serum ferritin and D-dimer are used in prognostic assessment of patients with COVID-19 pneumonia. Presepsin (PSP) is a soluble CD14 subtype that has recently been proposed as a novel biomarker in patients with sepsis. The aim of the current study was to detect the relation of PSP to the outcome of COVID-19 as well as its relation to other inflammatory biomarkers., Methods: This multicenter retrospective observational study was conducted in Saudi Arabia and Misr International Hospital, Egypt, from January 2021 to May 2021. Hospitalised patients who had positive throat swab of SARS-CoV-2 and radiological evidence of viral pneumonia (moderate and severe forms) were included in the study. Demographics and clinical features, as well as laboratory parameters, including serum ferritin, CRP, D-dimer and PSP, of enrolled patients were retrospectively collected. Pneumonia severity index (PSI) was used to evaluate the severity of pneumonia., Results: A total of 202 hospitalised patients who were diagnosed with COVID-19 pneumonia and tested positive for SARS-CoV-2 RNA were enrolled in our study. Of 202 hospitalised patients, 67 (33.17%) required intensive care unit (ICU) admission. A total of 176 (87.1%) patients survived and were discharged, whereas 26 (12.9%) patients did not survive. PSP level was found to be significantly elevated in nonsurvivor versus survivor group (median [IQR] 978.5 [755.8-1400] vs 516.5 [343.3-720], P<0.001) as well as in ICU versus non-ICU patients (median [IQR] 800 [631-1200] and 446 [320-626], respectively) (P<0.001). Elevated levels were also found to be associated with increased length of hospital stay. Levels above 775 pg/mL were found to be associated with in-hospital mortality (specificity 80%, sensitivity 73%)., Conclusion: Elevated PSP levels indicated poor outcomes in hospitalised patients with COVID-19 pneumonia and were associated with in-hospital mortality., Competing Interests: Conflict of interests The authors declare that there are no conflicts of interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs., Materials and Methods: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared., Results: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58., Conclusion: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff., Competing Interests: Declaration of Competing Interest All authors state that there were no conflict of interest. The NICE foundation pays the department of Medical Informatics to process and analyze data for the registry. This institution is not paid specifically for this study but it is part of the task agreement to perform analyses like this on data from the NICE registry., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background/aims: The aim of this study is to share autopsy findings of COVID-19-positive cases and autopsy algorithms for safely handling of suspicious bodies during this pandemic., Methods: COVID-19-positive cases of Istanbul Morgue Department were retrospectively analyzed. Sampling indications for PCR tests in suspicious deaths, macroscopic and microscopic findings obtained in cases with positive PCR tests were evaluated., Results: In the morgue department, 345(25.8%) of overall 1336 autopsy cases were tested for COVID-19. PCR test was found positive in 26 cases. Limited autopsy procedure was performed in 7 cases, while the cause of death was determined by external examination in the remaining 19 cases. Male-to-female ratio was found 3.3:1 and mean age was 60.0 ± 13.6 among all PCR-positive cases. Cause of death was determined as viral pneumonia in fully autopsied cases. Most common findings were sticky gelatinous fluid in cavities and firm and swollen lungs, varying degrees of consolidation. In microscopy, diffuse alveolar epithelial damage, type-II pneumocyte hyperplasia, hyaline membrane formation, fibrinous exudate, and fibrinous plaques in the alveoli were the most common findings., Conclusions: In COVID-19 autopsies, pulmonary findings were found to be prominent and the main pathology was pneumonia. Older age and findings of chronic diseases indicate that the cases were in the multirisk group in terms of COVID-19 mortality., (© 2021. Royal Academy of Medicine in Ireland.)

In 2019, the world faced a serious health challenge, the rapid spreading of a life-threatening viral pneumonia, coronavirus disease 2019 (COVID-19) caused by a betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 2022 WHO statistics shows more than 5.6 million death and about 350 million infection by SARS-CoV-2. One of the life threatening aspects of COVID-19 is secondary infections and reduced efficacy of antibiotics against them. Since the beginning of COVID-19 many researches have been done on identification, treatment, and vaccine development. Bacterial viruses (bacteriophages) could offer novel approaches to detect, treat and control COVID-19. Phage therapy and in particular using phage cocktails can be used to control or eliminate the bacterial pathogen as an alternative or complementary therapeutic agent. At the same time, phage interaction with the host immune system can regulate the inflammatory response. In addition, phage display and engineered synthetic phages can be utilized to develop new vaccines and antibodies, stimulate the immune system, and elicit a rapid and well-appropriate defense response. The emergence of SARS-CoV-2 new variants like delta and omicron has proved the urgent need for precise, efficient and novel approaches for vaccine development and virus detection techniques in which bacteriophages may be one of the plausible solutions. Therefore, phages with similar morphology and/or genetic content to that of coronaviruses can be used for ecological and epidemiological modeling of SARS-CoV-2 behavior and future generations of coronavirus, and in general new viral pathogens. This article is a comprehensive review/perspective of potential applications of bacteriophages in the fight against the present pandemic and the post-COVID era., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Competing Interests: None declared.

Background: A mismatch between a widespread use of broad-spectrum antibiotic agents and a low prevalence of reported bacterial co-infections in patients with SARS-CoV-2 infections has been observed. Herein, we sought to characterize and compare bacterial co-infections at admission in hospitalized patients with SARS-CoV-2, influenza or respiratory syncytial virus (RSV) positive community-acquired pneumonia (CAP)., Methods: A retrospective cohort study of bacterial co-infections at admission in SARS-CoV-2, influenza or RSV-positive adult patients with CAP admitted to Karolinska University Hospital in Stockholm, Sweden, from year 2011 to 2020. The prevalence of bacterial co-infections was investigated and compared between the three virus groups. In each virus group, length of stay, ICU-admission and 30-day mortality was compared in patients with and without bacterial co-infection, adjusting for age, sex and co-morbidities. In the SARS-CoV-2 group, risk factors for bacterial co-infection, were assessed using logistic regression models and creation of two scoring systems based on disease severity, age, co-morbidities and inflammatory markers with assessment of concordance statistics., Results: Compared to influenza and RSV, the bacterial co-infection testing frequency in SARS-CoV-2 was lower for all included test modalities. Four percent [46/1243 (95% CI 3-5)] of all SARS-CoV-2 patients had a bacterial co-infection at admission, whereas the proportion was 27% [209/775 (95% CI 24-30)] and 29% [69/242 (95% CI 23-35)] in influenza and RSV, respectively. S. pneumoniae and S. aureus constituted the most common bacterial findings for all three virus groups. Comparing SARS-CoV-2 positive patients with and without bacterial co-infection at admission, a relevant association could not be demonstrated nor excluded with regards to risk of ICU-admission (aHR 1.53, 95% CI 0.87-2.69) or 30-day mortality (aHR 1.28, 95% CI 0.66-2.46) in adjusted analyses. Bacterial co-infection was associated with increased inflammatory markers, but the diagnostic accuracy was not substantially different in a scoring system based on disease severity, age, co-morbidities and inflammatory parameters [C statistic 0.66 (95% CI 0.59-0.74)], compared to using disease severity, age and co-morbidities only [C statistic 0.63 (95% CI 0.56-0.70)]., Conclusions: The prevalence of bacterial co-infections was significantly lower in patients with community-acquired SARS-CoV-2 positive pneumonia as compared to influenza and RSV positive pneumonia., (© 2022. The Author(s).)

Spontaneous pneumomediastinum is a rare condition in viral pneumonia. However, it can arise spontaneously in COVID-19 patients with no other risk factors. Here we present four cases of spontaneous pneumomediastinum in patients with COVID-19 pneumonia with no other precipitating factors.

Background: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved., Objective: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients., Methods: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models., Results: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds., Conclusion: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.

Several studies reported acute symptomatic seizures as a possible neurological complication of COVID-19 pneumonia. Apart from metabolic imbalances, hypoxia, and fever, other ictogenic mechanisms are likely related to an immune-mediated damage. The same mechanisms are shared by other respiratory viruses. Since neurotropic properties of SARS-CoV-2 have been questioned, we investigated whether SARS-CoV-2 has a similar ictogenic potential to other respiratory non-neurotropic viruses. We conducted a retrospective study identifying 1141 patients with SARS-CoV-2 pneumonia and 146 patients with H1N1/H3N2 pneumonia. We found a similar prevalence of seizures in the two viral pneumonia (1.05% with SARS-CoV-2 vs 2.05% with influenza; p = 0.26). We detailed clinical, electroencephalographic, and neuroradiological features of each patient, together with the hypothesized pathogenesis of seizures. Previous epilepsy or pre-existing predisposing conditions (i.e., Alzheimer's disease, stroke, cerebral neoplasia) were found in one-third of patients that experienced seizures, while two-thirds of patients had seizures without known risk factors other than pneumonia in both groups. The prevalence of pre-existing predisposing conditions and disease severity indexes was similar in SARS-CoV-2 and H1N1/H3N2 pneumonia, thus excluding they could act as potential confounders. Considering all the patients with viral pneumonia together, previous epilepsy (p < 0.001) and the need for ventilatory support (p < 0.001), but not the presence of pre-existing predisposing conditions (p = 0.290), were associated with seizure risk. Our study showed that SARS-CoV-2 and influenza viruses share a similar ictogenic potential. In both these infections, seizures are rare but serious events, and can manifest without pre-existing predisposing conditions, in particular when pneumonia is severe, thus suggesting an interplay between disease severity and host response as a major mechanism of ictogenesis, rather than a virus-specific mechanism., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-1 score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.

The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early stage. In this study, a large margin piecewise linear classifier was developed to diagnose COVID-19 compared to a wide range of viral pneumonia, including SARS and MERS, using chest x-ray images. In the proposed method, a preprocessing pipeline was employed. Moreover, deep pre- and post-rectified linear unit (ReLU) features were extracted using the well-known VGG-Net19, which was fine-tuned to optimize transfer learning. Afterward, the canonical correlation analysis was performed for feature fusion, and fused deep features were passed into the LMPL classifier. The introduced method reached the highest performance in comparison with related state-of-the-art methods for two different schemes (normal, COVID-19, and typical viral pneumonia) and (COVID-19, SARS, and MERS pneumonia) with 99.39% and 98.86% classification accuracy, respectively., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment., (Copyright © 2021 Elsevier Ltd. All rights reserved.)