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Objectives: To automatically label chest radiographs and chest CTs regarding the detection of pulmonary infection in the report text, to calculate the number needed to image (NNI) and to investigate if these labels correlate with regional epidemiological infection data., Materials and Methods: All chest imaging reports performed in the emergency room between 01/2012 and 06/2022 were included (64,046 radiographs; 27,705 CTs). Using a regular expression-based text search algorithm, reports were labeled positive/negative for pulmonary infection if described. Data for regional weekly influenza-like illness (ILI) consultations (10/2013-3/2022), COVID-19 cases, and hospitalization (2/2020-6/2022) were matched with report labels based on calendar date. Positive rate for pulmonary infection detection, NNI, and the correlation with influenza/COVID-19 data were calculated., Results: Between 1/2012 and 2/2020, a 10.8-16.8% per year positive rate for detecting pulmonary infections on chest radiographs was found (NNI 6.0-9.3). A clear and significant seasonal change in mean monthly detection counts (102.3 winter; 61.5 summer; p < .001) correlated moderately with regional ILI consultations (weekly data r = 0.45; p < .001). For 2020-2021, monthly pulmonary infection counts detected by chest CT increased to 64-234 (23.0-26.7% per year positive rate, NNI 3.7-4.3) compared with 14-94 (22.4-26.7% positive rate, NNI 3.7-4.4) for 2012-2019. Regional COVID-19 epidemic waves correlated moderately with the positive pulmonary infection CT curve for 2020-2022 (weekly new cases: r = 0.53; hospitalizations: r = 0.65; p < .001)., Conclusion: Text mining of radiology reports allows to automatically extract diagnoses. It provides a metric to calculate the number needed to image and to track the trend of diagnoses in real time, i.e., seasonality and epidemic course of pulmonary infections., Clinical Relevance: Digitally labeling radiology reports represent previously neglected data and may assist in automated disease tracking, in the assessment of physicians' clinical reasoning for ordering radiology examinations and serve as actionable data for hospital workflow optimization., Key Points: • Radiology reports, commonly not machine readable, can be automatically labeled with the contained diagnoses using a regular-expression based text search algorithm. • Chest radiograph reports positive for pulmonary infection moderately correlated with regional influenza-like illness consultations (weekly data; r = 0.45; p < .001) and chest CT reports with the course of the regional COVID-19 pandemic (new cases: r = 0.53; hospitalizations: r = 0.65; p < 0.001). • Rendering radiology reports into data labels provides a metric for automated disease tracking, the assessment of ordering physicians clinical reasoning and can serve as actionable data for workflow optimization., (© 2023. The Author(s).)

Background/purpose: Patients with influenza infection during their period of admission may have worse computed tomography (CT) manifestation according to the clinical status. This study aimed to evaluate the CT findings of in-hospital patients due to clinically significant influenza pneumonia with correlation of clinical presentations., Methods: In this retrospective, single center case series, 144 patients were included. All in-hospital patients were confirmed influenza infection and underwent CT scan. These patients were divided into three groups according to the clinical status of the most significant management: (1) without endotracheal tube and mechanical ventilator (ETTMV) or extracorporeal membrane oxygenation (ECMO); (2) with ETTMV; (3) with ETTMV and ECMO. Pulmonary opacities were scored according to extent. Spearman rank correlation analysis was used to evaluate the correlation between clinical parameters and CT scores., Results: The predominant CT manifestation of influenza infection was mixed ground-glass opacity (GGO) and consolidation with both lung involvement. The CT scores were all reach significant difference among all three groups (8.73 ± 6.29 vs 12.49 ± 6.69 vs 18.94 ± 4.57, p < 0.05). The chest CT score was correlated with age, mortality, and intensive care unit (ICU) days (all p values were less than 0.05). In addition, the CT score was correlated with peak lactate dehydrogenase (LDH) level and peak C-reactive protein (CRP) level (all p values were less than 0.05). Concomitant bacterial infection had higher CT score than primary influenza pneumonia (13.02 ± 7.27 vs 8.95 ± 5.99, p < 0.05)., Conclusion: Thin-section chest CT scores correlated with clinical and laboratory parameters in in-hospital patients with influenza pneumonia., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article., (Copyright © 2023. Published by Elsevier B.V.)

Introduction: The seasonal flu is a very important reason for consultation every winter. Symptoms can quickly progress to severe pneumonia. Currently, few tools exist to assess the clinical severity of patients. The aim of this study is to demonstrate the role of lung ultrasound as a marker of severity in patients with influenza., Methods: 79 patients who arrived at the emergency department with flu-like symptoms were included. A pulmonary ultrasound looking for an interstitial syndrome or consolidation was performed. The qSOFA, the SOFA, the saturation, the PaO2/FiO2 ratio, the oxygen needs, the destination of the patient made it possible to establish the seriousness of the pathology of the patient. Ultrasound was then compared to these different tools., Results: The more the ultrasound became pathological, the more we observed a proportion of qSOFA (p = 0.001) and pathological SOFA (p = 0.009). Most patients with acute respiratory distress syndrome have pathological ultrasound (p < 0.001). The average admission saturation is 89.2 % in the "pathological ultrasound" group compared to 95.8 % in the "normal ultrasound" group (p < 0.001). Patients who required invasive therapies had pathological ultrasound (p < 0.001). Of the 28 patients with pathological ultrasound, 24 required hospitalization (p < 0.001)., Conclusion: Lung ultrasound is a major asset for assessing the severity of the patient with seasonal flu. In addition, ultrasound allows better monitoring of the patient by being able to influence the destination of the latter towards a return home or monitoring in intensive care.

Objective: The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases., Methods: Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared., Results: There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs., Conclusion: Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia., (© 2023 American Institute of Ultrasound in Medicine.)

In March 2009, influenza A(H1N1) flu broke out and spread rapidly worldwide, and it has been circulating in local areas with various scales since then. Particularly, the outbreak and prevalence have occurred in China during 2023 extensively. At present, there is an absence of unified consensus on imaging diagnosis of severe influenza A (H1N1) flu pneumonia, which is not conducive to the standardized imaging diagnosis and clinical practice. Chinese experts including the Infection and Inflammatory Radiology Committee of the Chinese Research Hospital Association jointly formulate this consensus based on numerous references related to influenza A (H1N1) flu, meanwhile combining the methodological requirements of evidence-based medicine for guideline and standard formulation. This consensus aims to form a consensus on the diagnostic evidence, recommended imaging methods, diagnostic standard and differential diagnosis of severe influenza A(H1N1) flu pneumonia, and it is ought to provide clear diagnostic information and basis for relevant professional physicians and guide the clinical diagnosis and treatment of severe pneumonia caused by influenza A(H1N1) flu.

Purpose: It is known from histology studies that lung vessels are affected in viral pneumonia. However, their diagnostic potential as a chest CT imaging parameter has only rarely been exploited. The purpose of this study is to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset., Methods: In total, 509 non-enhanced chest CTs (NECTs) and 563 CT pulmonary angiograms (CTPAs) were included. Sub-groups were patients with healthy lungs (group&#95;NORM, n = 634) and those RT-PCR-positive for Influenza A/B (group&#95;INF, n = 159) and SARS-CoV-2 (group&#95;COV, n = 279). A lung vessel segmentation algorithm (LVSA) based on traditional image processing was developed, validated with a point-of-interest approach, and applied to a large clinical dataset. Total blood vessel volume in lung (TBV) and the blood vessel volume percentage (BV%) of three blood vessel size types were calculated and compared between groups: small (BV5%, cross-sectional area < 5 mm 2 ), medium (BV5-10%, 5-10 mm 2 ) and large (BV10%, >10 mm 2 )., Results: Sensitivity of the LVSA was 84.6% (95 %CI: 73.9-95.3) for NECTs and 92.8% (95 %CI: 90.8-94.7) for CTPAs. In viral pneumonia, besides an increased TBV, the main finding was a significantly decreased BV5% in group&#95;COV (n = 14%) and group&#95;INF (n = 15%) compared to group&#95;NORM (n = 18%) [p < 0.001]. At the same time, BV10% was increased (group&#95;COV n = 15% and group&#95;INF n = 14% vs. group&#95;NORM n = 11%; p < 0.001)., Conclusion: In COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. Automated LSVA allows researchers and clinicians to derive imaging parameters for large amounts of CTs. This can enhance the understanding of vascular changes, particularly in infectious lung diseases., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Viral pneumonia is usually community acquired and caused by influenza, parainfluenza, respiratory syncytial virus, human metapneumovirus, and adenovirus. Many of these infections are airway centric and chest imaging demonstrates bronchiolitis and bronchopneumonia, With the exception of adenovirus infections, the presence of lobar consolidation usually suggests bacterial coinfection. Community-acquired viral pathogens can cause more severe pneumonia in immunocompromised hosts, who are also susceptible to CMV and varicella infection. These latter 2 pathogens are less likely to manifest the striking airway-centric pattern. Airway-centric pattern is distinctly uncommon in Hantavirus pulmonary syndrome, a rare environmentally acquired infection with high mortality., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Muhammad Aftab et al.)

Both severe acute respiratory syndrome coronavirus 2 and influenza viruses cause similar clinical presentations. It is essential to assess severely ill patients presenting with a viral syndrome for diagnostic and prognostic purposes. We aimed to compare clinical and biochemical features between pneumonia patients with coronavirus disease 2019 (COVID-19) and H1N1. Sixty patients diagnosed with COVID-19 pneumonia and 61 patients diagnosed with influenza pneumonia were hospitalized between October 2020-January 2021 and October 2017-December 2019, respectively. All the clinical data and laboratory results, chest computed tomography scans, intensive care unit admission, invasive mechanical ventilation, and outcomes were retrospectively evaluated. The median age was 65 (range 32-96) years for patients with a COVID-19 diagnosis and 58 (range 18-83) years for patients with influenza (p = 0.002). The comorbidity index was significantly higher in patients with COVID-19 (p = 0.010). Diabetes mellitus and hypertension were statistically significantly more common in patients with COVID-19 (p = 0.019, p = 0.008, respectively). The distribution of severe disease and mortality was not significantly different among patients with COVID-19 than influenza patients (p = 0.096, p = 0.049).). In comparison with inflammation markers; C-reactive protein (CRP) levels were significantly higher in influenza patients than patients with COVID-19 (p = 0.033). The presence of sputum was predictive for influenza (odds ratio [OR] 0.342 [95% confidence interval [CI], 2.1.130-0.899]). CRP and platelet were also predictive for COVID-19 (OR 4.764 [95% CI, 1.003-1.012] and OR 0.991 [95% CI 0.984-0.998], respectively. We conclude that sputum symptoms by itself are much more detected in influenza patients. Besides that, lower CRP and higher PLT count would be discriminative for COVID-19., (© 2021 Wiley Periodicals LLC.)

Objectives: This study aimed to explore clinical indexes for management of severe/critically ill patients with COVID-19, influenza A H7N9, and H1N1 pneumonia by comparing hematological and radiological characteristics., Methods: Severe/critically ill patients with COVID-19, H7N9, and H1N1 pneumonia were retrospectively enrolled. The demographic data, clinical manifestations, hematological parameters, and radiological characteristics were compared., Results: In this study, 16 cases of COVID-19, 10 cases of H7N9, and 13 cases of H1N1 who met severe/critically ill criteria were included. Compared with COVID-19, H7N9 and H1N1 groups had more chronic diseases (80% and 92.3% vs. 25%, p < 0.05), higher APACHE Ⅱ scores (16.00 ± 8.63 and 15.08 ± 6.24, vs. 5.50 ± 2.58, p < 0.05), higher mortality rates (40% and 46.2% vs. 0%, p < 0.05), significant lymphocytopenia (0.59 ± 0.31 × 10 9 /L and 0.56 ± 0.35 × 10 9 /L vs. 0.97 ± 0.33 × 10 9 /L, p < 0.05), and elevated neutrophil-to-lymphocyte ratio (NLR; 14.67 ± 6.10 and 14.64 ± 10.36 vs. 6.29 ± 3.72, p < 0.05). Compared with the H7N9 group, ground-glass opacity (GGO) on chest CT was common in the COVID-19 group (p = 0.028), while pleural effusion was rare (p = 0.001)., Conclusions: The NLR can be used as a clinical parameter for the predication of risk stratification and outcome in COVID-19 and influenza A pneumonia. Manifestations of pleural effusion or GGO in chest CT may be helpful for the identification of different viral pneumonia., (© 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

Influenza virus-associated encephalopathy/encephalitis is a rare entity in adults that can lead to severe neurological sequelae and even death. The clinical presentation can be quite diverse. This absence of a typical presentation along with the difficulty detecting the virus in the cerebrospinal fluid represents a diagnostic challenge. We present the case of a 79-year-old male with sudden onset of decreased consciousness and signs of right hemisphere damage. The presence of influenza A (H3N2) virus in respiratory sample along with compatible findings in cranial magnetic resonance led to the diagnosis. The patient died without responding to treatment with antivirals and immunomodulators and the anatomopathological study did not detect infectious agent. Early diagnostic suspicion is essential to establish adequate treatment and improve the prognosis., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task., Methods: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest., Results: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%)., Conclusion: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT., (Copyright: © Iranian Journal of Medical Sciences.)

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different ( p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different ( p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment ( p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.

In patients with influenza, cardiac and lung ultrasound may help determine the severity of illness and predict clinical outcomes. To determine the ultrasound characteristics of influenza and define the spectrum of lung and cardiac findings in patients with suspected influenza A or B, we conducted a prospective observational study in patients presenting to the emergency department at a tertiary care academic institution. An ultrasound protocol consisting of cardiac, lung and inferior vena cava scans was performed within 6 h of admission. We compared the ultrasound findings in cases with positive and negative influenza polymerase chain reaction, while controlling for comorbidities. We enrolled 117 patients, 41.9% of whom (49/117) tested positive for influenza. In those with influenza, ultrasound confirmed preserved left ventricular and right ventricular (RV) function in 81.3% of patients. The most common cardiac pathology was RV dilation (10.4%), followed by left ventricular systolic dysfunction (8.3%). Patients with negative influenza polymerase chain reaction with RV dysfunction demonstrated higher hospital admission than those those with normal RV function (45.1%, 23/51, vs. 17.9%, 5/28; p = 0.016). B-lines were prevalent in both influenza and non-influenza groups (40.8% and 69.1%, respectively; p = 0.013). Lung consolidation was identified in only 8.25% of patients with influenza. In conclusion, in patients with influenza we were unable to define distinct ultrasound features specific to influenza A or B, suggesting that ultrasound may not be beneficial in diagnosing influenza nor in evaluating its severity., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest., (Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Objectives: To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza., Materials and Methods: This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and 3 radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings., Results: The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared to the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the 3 radiologists was 65.8%., Conclusion: Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Serious bacterial infection rates in febrile infants < 60 days are about 8-11%. Less than 1% of febrile infants with no respiratory symptoms will have pneumonia however, chest radiography (CXR) rates remain between 30 and 60%. Rapid Respiratory Syncytial Virus (RSV) and influenza (flu) testing is common, however, there is not enough data to determine if febrile infants without any respiratory symptoms should be tested. The goal of this study is to determine the rate of positive CXR and RSV/flu results in febrile infants with no respiratory symptoms and no sick contacts., Methods: Well-appearing febrile infants between 7 and 60 days of age who presented to the pediatric emergency department (PED) from September 1st, 2015 through October 30th, 2017 were enrolled. Demographic data, respiratory symptoms, CXR findings and RSV/flu results were collected. SAS statistical software was used for analysis., Results: 129 infants met enrollment criteria. Of the 129 infants, 58 (45.0%) had no respiratory symptoms and no sick contacts. Of these 58, 36 (62.1%) received a CXR and none of them had any abnormal findings, 48 (82.8%) had RSV/flu testing, no patients tested positive for RSV and only one patient tested positive for flu. Costs of CXR and RSV/flu testing for this cohort was $19,788., Conclusion: The absence of positive CXRs in this patient population reinforces the current recommendations that CXR is not indicated. The low incidence of RSV/flu indicate that routine testing may not be necessary in this population especially outside of the flu season. Reduced testing could decrease overall costs to the healthcare system as well as radiation exposure to this population., (© 2021. The Author(s).)

The last step in influenza virus replication involves the assembly of viral components on the infected cell's plasma membrane followed by budding of intact virus from the host cell surface. Because viral neuraminidase and hemagglutinin are both inserted into the host cell's membrane during this process, influenza virus-infected cells are distinguished from uninfected cells by the presence of viral neuraminidase and hemagglutinin on their cell surfaces. In an effort to exploit this difference in cell surface markers for development of diagnostic and therapeutic agents, we have modified an influenza neuraminidase inhibitor, zanamivir, for targeting of attached imaging and therapeutic agents selectively to influenza viruses and virus-infected cells. We have designed here a zanamivir-conjugated rhodamine dye that allows visual monitoring of binding, internalization, and intracellular trafficking of the fluorescence-labeled neuraminidase in virus-infected cells. We also synthesize a zanamivir- 99m Tc radioimaging conjugate that permits whole body imaging of the virus's biodistribution and abundance in infected mice. Finally, we create both a zanamivir-targeted cytotoxic drug (i.e., zanamivir-tubulysin B) and a viral neuraminidase-targeted CAR T cell and demonstrate that they are both able to kill viral neuraminidase-expressing cells without damaging healthy cells. Taken together, these data suggest that the influenza virus neuraminidase inhibitor, zanamivir, can be exploited to improve the diagnosis, imaging, and treatment of influenza virus infections.

We compared clinical symptoms, laboratory findings, radiographic signs and outcomes of COVID-19 and influenza to identify unique features. Depending on the heterogeneity test, we used either random or fixed-effect models to analyse the appropriateness of the pooled results. Overall, 540 articles included in this study; 75,164 cases of COVID-19 (157 studies), 113,818 influenza type A (251 studies) and 9266 influenza type B patients (47 studies) were included. Runny nose, dyspnoea, sore throat and rhinorrhoea were less frequent symptoms in COVID-19 cases (14%, 15%, 11.5% and 9.5%, respectively) in comparison to influenza type A (70%, 45.5%, 49% and 44.5%, respectively) and type B (74%, 33%, 38% and 49%, respectively). Most of the patients with COVID-19 had abnormal chest radiology (84%, p < 0.001) in comparison to influenza type A (57%, p < 0.001) and B (33%, p < 0.001). The incubation period in COVID-19 (6.4 days estimated) was longer than influenza type A (3.4 days). Likewise, the duration of hospitalization in COVID-19 patients (14 days) was longer than influenza type A (6.5 days) and influenza type B (6.7 days). Case fatality rate of hospitalized patients in COVID-19 (6.5%, p < 0.001), influenza type A (6%, p < 0.001) and influenza type B was 3%(p < 0.001). The results showed that COVID-19 and influenza had many differences in clinical manifestations and radiographic findings. Due to the lack of effective medication or vaccine for COVID-19, timely detection of this viral infection and distinguishing from influenza are very important., (© 2020 John Wiley & Sons Ltd.)

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.

Ground-glass opacity is a very frequent and unspecified finding in chest computed tomography. Therefore, it admits a wide range of differential diagnoses in the acute context, from viral pneumonias such as influenza virus, coronavirus disease 2019 and cytomegalovirus and even non-infectious lesions, such as vaping, pulmonary infarction, alveolar hemorrhage and pulmonary edema. For this diagnostic differentiation, ground glass must be correlated with other findings in imaging tests, with laboratory tests and with the patients' clinical condition. In the context of a pandemic, it is extremely important to remember the other pathologies with similar findings to coronavirus disease 2019 in the imaging exams.

Background: Both SARS-CoV-2 and influenza virus share similarities such as clinical features and outcome, laboratory, and radiological findings., Methods: Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words "COVID-19" and "Influenza"., Results: Eighteen articles were finally selected in adult patients. Comorbidities such as cardiovascular diseases, diabetes, and obesity were significantly higher in COVID-19 patients, while pulmonary diseases and immunocompromised conditions were significantly more common in influenza patients. The incidence rates of fever, vomiting, ocular and otorhinolaryngological symptoms were found to be significantly higher in influenza patients when compared with COVID-19 patients. However, neurologic symptoms and diarrhea were statistically more frequent in COVID-19 patients. The level of white cell count and procalcitonin was significantly higher in influenza patients, whereas thrombopenia and elevated transaminases were significantly more common in COVID-19 patients. Ground-grass opacities, interlobular septal thickening, and a peripheral distribution were more common in COVID-19 patients than in influenza patients where consolidations and linear opacities were described instead. COVID-19 patients were significantly more often transferred to intensive care unit with a higher rate of mortality., Conclusions: This study estimated differences of COVID-19 and influenza patients which can help clinicians during the co-circulation of the two viruses.

Background: In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia., Methods: A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis., Results: The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%)., Conclusions: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

Background: This study was to collect clinical features and computed tomography (CT) findings of Influenza-Like Illness (ILI) cases, and to evaluate the correlation between clinical data and the abnormal chest CT in patients with the Influenza-Like Illness symptoms. Methods: Patients with the Influenza-Like Illness symptoms who attended the emergency department of The Six Medical Center of The PLA General Hospital from February 10 to April 1, 2020 were enrolled. Clinical and imaging data of the enrolled patients were collected and analyzed. The association between clinical characteristics and abnormal chest CT was also analyzed. Results: A total of 148 cases were enrolled in this study. Abnormalities on chest CT were detected in 61/148 (41.2%) patients. The most common abnormal CT features were as follows: patchy consolidation 22/61(36.1%), ground-glass opacities 21/61(34.4%), multifocal consolidations 17/61(27.9%). The advanced age and underlying diseases were significantly associated with abnormal chest CT. Conclusions: Abnormal chest CT is a common condition in Influenza-Like Illness cases. The presence of advanced age and concurrent underlying diseases is significantly associated with abnormal chest CT findings in patients with ILI symptoms. The chest CT characteristic of ILI is different from the manifestation of COVID-19 infection, which is helpful for differential diagnosis., 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 © 2021 Shuai, Chen, Shan, Li, Ma, Lu and Li.)

Background: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus that was first discovered in December 2019 in Wuhan, China. With the growing numbers of community spread cases worldwide, the World Health Organization (WHO) declared the COVID-19 outbreak as a pandemic on March 11, 2020. Like influenza viruses, SARS-CoV-2 is thought to be mainly transmitted by droplets and direct contact, and COVID-19 has a similar disease presentation to influenza. Here we present a case of influenza A and COVID-19 co-infection in a 60-year-old man with end-stage renal disease (ESRD) on hemodialysis., Case Presentation: A 60-year-old man with ESRD on hemodialysis presented for worsening cough, shortness of breath, and diarrhea. The patient first developed a mild fever (37.8 °C) during hemodialysis 3 days prior to presentation and has been experiencing worsening flu-like symptoms, including fever of up to 38.6 °C, non-productive cough, generalized abdominal pain, nausea, vomiting, and liquid green diarrhea. He lives alone at home with no known sick contacts and denies any recent travel or visits to healthcare facilities other than the local dialysis center. Rapid flu test was positive for influenza A. Procalcitonin was elevated at 5.21 ng/mL with a normal white blood cell (WBC) count. Computed tomography (CT) chest demonstrated multifocal areas of consolidation and extensive mediastinal and hilar adenopathy concerning for pneumonia. He was admitted to the biocontainment unit of Nebraska Medicine for concerns of possible COVID-19 and was started on oseltamivir for influenza and vancomycin/cefepime for the probable bacterial cause of his pneumonia and diarrhea. Gastrointestinal (GI) pathogen panel and Clostridioides difficile toxin assay were negative. On the second day of admission, initial nasopharyngeal swab came back positive for SARS-CoV-2 by real-time reverse-transcriptase polymerase chain reaction (RT-PCR). The patient received supportive care and resumed bedside hemodialysis in strict isolation, and eventually fully recovered from COVID-19., Conclusions: We presented a case of co-infection of influenza and SARS-CoV-2 in a hemodialysis patient. The possibility of SARS-CoV-2 co-infection should not be overlooked even when other viruses including influenza can explain the clinical symptoms, especially in high-risk patients.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

BACKGROUND Although influenza primarily affects the respiratory system, it can cause severe neurological complications, especially in younger children, but knowledge about the early indicators of acute necrotizing encephalopathy (ANE) is limited. The main purpose of this article is to summarize the clinical characteristics, diagnosis, and treatment of neurological complications of influenza in children, and to identify factors associated with ANE. MATERIAL AND METHODS This was a retrospective study of children with confirmed influenza with neurological complications treated between 01/2014 and 12/2019 at Guangzhou Women and Children's Medical Center. A receiver operating characteristics curve analysis was performed to determine the prognostic value of selected variables. RESULTS Sixty-three children with IAE (n=33) and ANE (n=30) were included. Compared with the IAE group, the ANE group showed higher proportions of fever and acute disturbance of consciousness, higher alanine aminotransferase, higher aspartate aminotransferase, higher creatinine kinase, higher procalcitonin, higher cerebrospinal fluid (CSF) protein, and lower CSF white blood cells (all P<0.05). The areas under the curve (AUCs) for procalcitonin and CSF proteins, used to differentiate IAE and ANE, were 0.790 and 0.736, respectively. The sensitivity and specificity of PCT >4.25 ng/ml to predict ANE were 73.3% and 100.0%, respectively. The sensitivity and specificity of CSF protein >0.48 g/L to predict ANE were 76.7% and 69.7%, respectively. Thirteen (43.3%) children with ANE and none with IAE died (P<0.0001). CONCLUSIONS High levels of CSF protein and serum procalcitonin might be used as early indicators for ANE. All children admitted with neurological findings, especially during the influenza season, should be evaluated for influenza-related neurological complications.

Background: Multicenter retrospective comparison of the first high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) and other viral pneumonias., Methods: We retrospectively collected clinical and imaging data from 262 cases of confirmed viral pneumonia in 20 hospitals in Yunnan Province, China, from March 1, 2015 to March 15, 2020. According to the virus responsible for the pneumonia, the pneumonias were divided into non-COVID-19 (141 cases) and COVID-19 (121 cases). The non-COVID-19 pneumonias comprised cytomegalovirus (CMV) (31 cases), influenza A virus (82 cases), and influenza B virus (20 cases). The differences in the basic clinical characteristics, lesion distribution, location and imaging signs among the four viral pneumonias were analyzed and compared., Results: Fever and cough were the most common clinical symptoms of the four viral pneumonias. Compared with the COVID-19 patients, the non-COVID-19 patients had higher proportions of fatigue, sore throat, expectorant and chest tightness (all P<0.000). In addition, in the CMV pneumonia patients, the proportions of acquired immunodeficiency syndrome (AIDS) and leukopenia were high (all PP<0.000). Comparison of the imaging findings of the four viral pneumonias showed that the pulmonary lesions of COVID-19 were more likely to occur in the peripheral and lower lobes of both lungs, whereas those of CMV pneumonia were diffusely distributed. Compared with the non-COVID-19 pneumonias, COVID-19 pneumonia was more likely to present as ground-glass opacity, intralobular interstitial thickening, vascular thickening and halo sign (all PP<0.05). In addition, in the early stage of COVID-19, extensive consolidation, fibrous stripes, subpleural lines, crazy-paving pattern, tree-in-bud, mediastinal lymphadenectasis, pleural thickening and pleural effusion were rare (all PP<0.05)., Conclusions: The HRCT findings of COVID-19 pneumonia and other viral pneumonias overlapped significantly, but many important differential imaging features could still be observed.

Purpose: The vascular enlargement (VE) pattern differs from previously described imaging patterns for pneumonia. This study aimed to investigate the incidence, computed tomography (CT) characteristics, and diagnostic value of the VE pattern in coronavirus disease 2019 (COVID-19)., Method: The CT data of 106 patients with COVID-19 from January 19 to February 29, 2020, and 52 patients with influenza virus pneumonia (IVP) from January 2018 to February 2020 were retrospectively collected. The incidences of the VE pattern between the two groups were compared. The CT manifestations of COVID-19 were analyzed with a particular focus on the VE pattern's specific CT signs, dynamic changes, and relationships with lesion size and disease severity., Results: Peripheral and multilobar ground-glass opacities (GGOs) or mixed GGOs with various sizes and morphologies were typical features of COVID-19 on initial CT. The VE pattern was more common in COVID-19 (88/106, 83.02 %) than in IVP (10/52, 19.23 %) on initial CT (P < 0.001). Three special VE-pattern-specific CT signs, including central vascular sign, ginkgo leaf sign, and comb sign, were identified. Four types of dynamic changes in the VE pattern were observed on initial and follow-up CT, which were closely associated with the evolution of lesions and the time interval from the onset of symptoms to initial CT scan. The VE pattern in COVID-19 was more commonly seen in larger lesions and patients with severe-critical type (all P < 0.001)., Conclusions: The VE pattern is a valuable CT sign for differentiating COVID-19 from IVP, which correlates with more extensive or serious disease. A good understanding of the CT characteristics of the VE pattern may contribute to the early and accurate diagnosis of COVID-19 and prediction of the evolution of lesions., (Copyright © 2020. Published by Elsevier B.V.)

Objective: To explore the different clinical and CT features distinguishing COVID-19 from H1N1 influenza pneumonia., Patients and Methods: We compared two independent cohorts of COVID-19 pneumonia (n=405) and H1N1 influenza pneumonia (n=78), retrospectively. All patients were confirmed by RT-PCR. Four hundred and five cases of COVID-19 pneumonia were confirmed in nine hospitals of Zhejiang province, China from January 21 to February 20, 2020. Seventy-eight cases of H1N1 influenza pneumonia were confirmed in our hospital from January 1, 2017 to February 29, 2020. Their clinical manifestations, laboratory test results, and CT imaging characteristics were compared., Results: COVID-19 pneumonia patients showed less proportions of underlying diseases, fever and respiratory symptoms than those of H1N1 pneumonia patients (p<0.01). White blood cell count, neutrophilic granulocyte percentage, C-reactive protein, procalcitonin, D-Dimer, and lactate dehydrogenase in H1N1 pneumonia patients were higher than those of COVID-19 pneumonia patients (p<0.05). H1N1 pneumonia was often symmetrically located in the dorsal part of inferior lung lobes, while COVID-19 pneumonia was unusually showed as a peripheral but non-specific lobe distribution. Ground glass opacity was more common in COVID-19 pneumonia and consolidation lesions were more common in H1N1 pneumonia (p<0.01). COVID-19 pneumonia lesions showed a relatively clear margin compared with H1N1 pneumonia. Crazy-paving pattern, thickening vessels, reversed halo sign and early fibrotic lesions were more common in COVID-19 pneumonia than H1N1 pneumonia (p<0.05). Pleural effusion in COVID-19 pneumonia was significantly less common than H1N1 pneumonia (p<0.01)., Conclusions: Compared with H1N1 pneumonia in Zhejiang, China, the clinical manifestations of COVID-19 pneumonia were more concealed with less underlying diseases and slighter respiratory symptoms. The more common CT manifestations of COVID-19 pneumonia included ground-glass opacity with a relatively clear margin, crazy-paving pattern, thickening vessels, reversed halo sign, and early fibrotic lesions, while the less common CT manifestations of COVID-19 pneumonia included consolidation and pleural effusion.

OBJECTIVE. The purpose of this study was to investigate differences in CT manifestations of coronavirus disease (COVID-19) pneumonia and those of influenza virus pneumonia. MATERIALS AND METHODS. We conducted a retrospective study of 52 patients with COVID-19 pneumonia and 45 patients with influenza virus pneumonia. All patients had positive results for the respective viruses from nucleic acid testing and had complete clinical data and CT images. CT findings of pulmonary inflammation, CT score, and length of largest lesion were evaluated in all patients. Mean density, volume, and mass of lesions were further calculated using artificial intelligence software. CT findings and clinical data were evaluated. RESULTS. Between the group of patients with COVID-19 pneumonia and the group of patients with influenza virus pneumonia, the largest lesion close to the pleura (i.e., no pulmonary parenchyma between the lesion and the pleura), mucoid impaction, presence of pleural effusion, and axial distribution showed statistical difference ( p < 0.05). The properties of the largest lesion, presence of ground-glass opacity, presence of consolidation, mosaic attenuation, bronchial wall thickening, centrilobular nodules, interlobular septal thickening, crazy paving pattern, air bronchogram, unilateral or bilateral distribution, and longitudinal distribution did not show significant differences ( p > 0.05). In addition, no significant difference was seen in CT score, length of the largest lesion, mean density, volume, or mass of the lesions between the two groups ( p > 0.05). CONCLUSION. Most lesions in patients with COVID-19 pneumonia were located in the peripheral zone and close to the pleura, whereas influenza virus pneumonia was more prone to show mucoid impaction and pleural effusion. However, differentiating between COVID-19 pneumonia and influenza virus pneumonia in clinical practice remains difficult.

Background: A pneumatocele is a transient thin-walled lesion and rare complication in adult pneumonia. A variety of infectious pathogens have been reported in children with pneumatoceles. We report the first case of adult pneumonia with pneumatocele formation that is likely caused by Streptococcus pyogenes and coinfection with influenza A virus., Case Presentation: A 64-year-old Japanese man presented with a one-week history of fever, sore throat, and arthralgia. He was referred to our university hospital for respiratory distress. He required mechanical ventilation in the intensive care unit (ICU). Bacterial culture detected S. pyogenes in the bronchoscopic aspirates, which was not detected in blood. Although a rapid influenza antigen test was negative, an influenza A polymerase chain reaction (PCR) test was positive. Therefore, he was diagnosed with coinfection of influenza A and group A streptococcus (GAS) pneumonia complicated by probable streptococcal toxic shock syndrome. A chest radiograph on admission showed diffuse patchy opacification and consolidation in the bilateral lung fields. Multiple thin-walled cysts appeared in both middle lung fields on computed tomography (CT). On the following day, the bilateral cysts had turned into a mass-like opacity. The patient died despite intensive care. An autopsy was performed. The pathology investigation revealed multiple hematomas formed by bleeding in pneumatoceles., Conclusions: There have been no previous reports of a pneumatocele complicated by S. pyogenes in an adult patient coinfected with influenza A. Further molecular investigation revealed that the S. pyogenes isolate had the sequence type of emm3.

OBJECTIVE. The purpose of this study was to compare clinical and chest CT findings in patients with influenza A (H1N1) pneumonia and coronavirus disease (COVID-19) pneumonia. MATERIALS AND METHODS. Thirty patients with diagnosed influenza A (H1N1) virus infection (group A) and 30 patients with diagnosed COVID-19 (group B) were retrospectively enrolled in the present study. The clinical characteristics and chest CT findings of the two groups were compared. RESULTS. Fever, cough, expectoration, and dyspnea were the main symptoms in both groups with viral pneumonia, with cough and expectoration more frequently found in group A. Lymphopenia, an elevated C-reactive protein level, and an increased erythrocyte sedimentation rate were common laboratory test findings in the two groups. The median time from symptom onset to CT in group A and group B was 6 and 15 days, respectively, and the median total CT score of the pulmonary lobes involved was 6 and 13, respectively. Linear opacification, crazy-paving sign, vascular enlargement, were more common in group B. In contrast, bronchiectasis and pleural effusion were more common in group A. Other common CT features, including peripheral or peribronchovascular distribution, ground-glass opacities (GGOs), consolidation, subpleural line, air bronchogram, and bronchial distortion, did not show statistical significance. CONCLUSION. On CT, the significant differences between influenza A (H1N1) pneumonia and COVID-19 pneumonia were findings of linear opacification, crazy-paving sign, vascular enlargement, pleural thickening, and pleural effusion, which were more common in patients with COVID-19 pneumonia, and bronchiectasis and pleural effusion, which were more common in patients with influenza A (H1N1) pneumonia. Other imaging findings, including peripheral or peribronchovascular distribution, ground-glass opacities (GGO), consolidation, subpleural line, air bronchogram, and bronchial distortion, were not significantly different between the two patient groups.

Coronavirus disease 2019 (COVID-19) is the most important global public health issue that we currently face. We aimed to explore the clinical features of patients with COVID-19 and compared them with those of hospitalized community-acquired pneumonia (CAP) patients caused by influenza virus during the same period.From Jan 1, to Mar 4, 2020, patients with COVID-19 or CAP caused by influenza virus who were admitted to the First Affiliated Hospital of Xiamen University were consecutively screened for enrollment.A total of 35 COVID-19 patients and 22 CAP patients caused by influenza virus were included in this study. Most of COVID-19 patients had characteristics of familial clustering (63%), however, in the other group, there was no similar finding. The percentages of patients with a high fever (the highest recorded temperature was ≥39.0°C; 11% vs 45% [COVID-19 vs CAP groups, respectively]), dyspnea (9% vs 59%), leukocytosis (3% vs 32%), elevated C-reactive protein concentrations (>10 mg/L, 48% vs 86%), elevated procalcitonin levels (>0.1 ng/ml, 15% vs 73%), PaO2/FiO2 <200 mm Hg (4% vs 22%), and infiltration on imaging (29% vs 68%) in the COVID-19 group were less than those same indices in the hospitalized CAP patients caused by influenza virus. Ground-glass opacity with reticular pattern (63%) and interlobular septal thickening (71%) in chest CT were commonly observed in the COVID-19 group.COVID-19 and CAP caused by influenza virus appear to share some similarities in clinical manifestaions but they definitely have major distinctions. Influenza infection remains a health problem even during COVID-19 pandemic.

Objectives: To investigate the different CT characteristics which may distinguish influenza from 2019 coronavirus disease (COVID-19)., Methods: A total of 13 confirmed patients with COVID-19 were enrolled from January 16, 2020, to February 25, 2020. Furthermore, 92 CT scans of confirmed patients with influenza pneumonia, including 76 with influenza A and 16 with influenza B, scanned between January 1, 2019, to February 25, 2020, were retrospectively reviewed. Pulmonary lesion distributions, number, attenuation, lobe predomination, margin, contour, ground-glass opacity involvement pattern, bronchial wall thickening, air bronchogram, tree-in-bud sign, interlobular septal thickening, intralobular septal thickening, and pleural effusion were evaluated in COVID-19 and influenza pneumonia cohorts., Results: Peripheral and non-specific distributions in COVID-19 showed a markedly higher frequency compared with the influenza group (p < 0.05). Most lesions in COVID-19 showed balanced lobe localization, while in influenza pneumonia they were predominantly located in the inferior lobe (p < 0.05). COVID-19 presented a clear lesion margin and a shrinking contour compared with influenza pneumonia (p < 0.05). COVID-19 had a patchy or combination of GGO and consolidation opacities, while a cluster-like pattern and bronchial wall thickening were more frequently seen in influenza pneumonia (p < 0.05). The lesion number and attenuation, air bronchogram, tree-in-bud sign, interlobular septal thickening, and intralobular septal thickening were not significantly different between the two groups (all p > 0.05)., Conclusions: Though viral pneumonias generally show similar imaging features, there are some characteristic CT findings which may help differentiating COVID-19 from influenza pneumonia., Key Points: • CT can play an early warning role in the diagnosis of COVID-19 in the case of no epidemic exposure. • CT could be used for the differential diagnosis of influenza and COVID-19 with satisfactory accuracy. • COVID-19 had a patchy or combination of GGO and consolidation opacities with peripheral distribution and balanced lobe predomination.

Background: Since the outbreak of Coronavirus Disease 2019 (COVID-19) in Wuhan, considerable attention has been paid to its epidemiology and clinical characteristics in children. However, it is also crucial for clinicians to differentiate COVID-19 from other respiratory infectious diseases, such as influenza viruses., Methods: This was a retrospective study. Two groups of COVID-19 patients (n=57) and influenza A patients (n=59) were enrolled. We analyzed and compared their clinical manifestations, imaging characteristics and treatments., Results: The proportions of cough (70.2%), fever (54.4%) and gastrointestinal symptoms (14.1%) in COVID-19 patients were lower than those of influenza A patients (98.3%, P<0.001; 84.7%, P<0.001; and 35.6%, P=0.007; respectively). In addition, COVID-19 patients showed significantly lower levels of leukocytes (7.87 vs. 9.89×10 9 L -1 , P=0.027), neutrophils (2.43 vs. 5.16×10 9 L -1 , P<0.001), C-reactive protein (CRP; 3.7 vs. 15.1mg/L, P=0.001) and procalcitonin (PCT; 0.09 vs. 0.68mm/h, P<0.001), while lymphocyte levels (4.58 vs. 3.56×109L -1 ; P=0.006) were significantly higher compared with influenza A patients. In terms of CT imaging, ground-glass opacification in chest CT was more common in COVID-19 patients than in influenza A patients (42.1% vs. 15%, P=0.032). In contrast, consolidation was more common in influenza A patients (25%) than in COVID-19 patients (5.2%, P=0.025)., Conclusion: The clinical manifestations and laboratory tests of COVID-19 children are milder than those of influenza A children under 5 years. Additionally, imaging results more commonly presented as ground-glass opacities in COVID-19 patients., (Copyright © 2020. Published by Elsevier Ltd.)

OBJECTIVE. The purpose of this article is to review new pediatric lung disorders-including disorders that have occurred in recent years years such as severe acute respiratory syndrome (SARS), swine-origin influenza A (H1N1), Middle East respiratory syndrome (MERS), e-cigarette or vaping product use-associated lung injury (EVALI), and coronavirus disease (COVID-19) pneumonia-to enhance understanding of the characteristic imaging findings. CONCLUSION. Although the clinical symptoms of SARS, H1N1, MERS, EVALI, and COVID-19 pneumonia in pediatric patients may be nonspecific, some characteristic imaging findings have emerged or are currently emerging. It is essential for radiologists to have a clear understanding of the characteristic imaging appearances of these lung disorders in pediatric patients to ensure optimal patient care.

This review highlights the ultrasound findings reported from a number of studies and case reports and discusses the unifying findings from coronavirus disease (COVID-19) patients and from the avian (H7N9) and H1N1 influenza epidemics. We discuss the potential role for portable point-of-care ultrasound (PPOCUS) as a safe and effective bedside option in the initial evaluation, management, and monitoring of disease progression in patients with confirmed or suspected COVID-19 infection.

Competing Interests: Competing interests: None declared.

Objective: The COVID-19 pandemic and annual influenza epidemic are responsible for thousands of deaths globally. With a similarity in clinical as well as laboratory findings, there is a need to differentiate these two conditions on chest CT scan. This paper attempts to use existing literature to draw out differences in chest CT findings in COVID-19 and influenza., Methods: A search was conducted using PubMed. 17 original studies on chest CT findings in COVID-19 and influenza were identified for full-text review and data analysis. Findings . COVID-19 and influenza share similar chest CT findings. The differences found show that COVID-19 ground-glass opacities are usually peripherally located with the lower lobes being commonly involved, while influenza has a central, peripheral, or random distribution usually affecting the five lobes. Vascular engorgement, pleural thickening, and subpleural lines were reported in COVID-19 patients. In contrast, pneumomediastinum and pneumothorax were reported only in studies on influenza. Conclusion and Relevance . COVID-19 and influenza have overlapping chest CT features with few differences which can assist in telling apart the two pathologies. Additional studies are needed to further define the differences and degree between COVID-19 and influenza., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2020 Stephen O. Onigbinde et al.)

Introduction: We present the findings on high-resolution computed tomography (HRCT) of influenza A (H1N1) virus-associated pneumonia of 140 patients with acute and post-acute pneumonia, totaling 189 exams in a retrospective observational study evaluating the importance of HRCT as a diagnostic imaging method in the acute phase and in the follow-up of pneumonia., Methodology: We performed a retrospective observational study evaluating the HRCT findings of 140 adult patients with confirmed diagnosis of influenza A (H1N1) pneumonia and without other associated infectious processes. Chest X-ray exams were also performed in these patients., Results: The main HRCT findings of lung involvement were airspace consolidation (57 cases), ground-glass opacities (40 cases) and an association of both aspects (43 cases), with a predominantly bilateral and peripheral distribution., Conclusions: HRCT is able to distinguish small lesions, such as small areas of consolidation or ground glass opacities, with little increase in lung attenuation, when chest X-rays was normal, allowing a prompt diagnosis and treatment after imaging., Competing Interests: No Conflict of Interest is declared, (Copyright (c) 2020 Cristina AP Fontes, Alair Augusto SMD dos Santos, Solange A de Oliveira.)

Coronavirus disease 2019, also called COVID-19, is a global pandemic resulting in significant morbidity and mortality worldwide. In the United States, influenza infection occurs mainly during winter and several factors influence the burden of the disease, including circulating virus characteristics, vaccine effectiveness that season, and the duration of the season. We present a case series of 3 patients with coinfection of COVID-19 and influenza, with 2 of them treated successfully and discharged home. We reviewed the literature of patients coinfected with both viruses and discussed the characteristics, as well as treatment options.

Competing Interests: Die Autoren geben an, dass kein Interessenkonflikt besteht.

Background: Influenza virus infection (IVI) was reported to be associated with minor cardiac changes, mostly those detected on electrocardiogram with and without elevated blood markers of myocardial injury; however, the characteristics of myocardial involvement in association with IVI are poorly understood. This study used echocardiographic tissue imaging (tissue Doppler, strain, and strain rate) to evaluate changes in left atrial (LA) and left ventricular (LV) myocardial function after IVI., Methods and Results: We examined 20 adult individuals (mean age, 43 years) at 2 and 4 weeks after diagnosis of IVI. For myocardial functional variables, we obtained LV global longitudinal strain (GLS), LV early diastolic strain rate (e'sr), LA strain, and LA stiffness (E/e'/LA strain), in addition to data on tissue Doppler (s', e', and a') and myocardial performance index. Blood markers of myocardial injury were also examined. During follow-up, there were no significant changes in global chamber function such as LV ejection fraction, E/e', and LA volume. However, significant changes in myocardial function were observed, namely, in s' (8.0 ± 1.6 cm/s to 9.3 ± 1.5 cm/s; p = 0.01), e' (10.2 ± 2.8 cm/s to 11.4 ± 3.0 cm/s; p < 0.001), e'sr (1.43 ± 0.44 1/s to 1.59 ± 0.43 1/s; p = 0.005), and LA strain (35 ± 8% to 40 ± 12%; p = 0.025), and the myocardial performance index (0.52 ± 0.20 to 0.38 ± 0.09; p = 0.009), but not in a', LA stiffness, or GLS. Cardiac troponin T and creatinine kinase isoenzyme MB were not elevated significantly at any examination., Conclusions: Myocardial dysfunction during IVI recovery appeared to be transient particularly in the absence of myocardial injury. Echocardiographic tissue imaging may be useful to detect subclinical cardiac changes in association with IVI., Competing Interests: The authors have declared that no competing interests exist.

Backgroud: Organizing pneumonia (OP) is a rare complication of influenza infection that has substantial morbidity. We report the first case of OP associated with avian influenza H7N9 infection that had significant improvement with corticosteroid treatment., Case Presentation: A 35-year-old male admitted to intensive care unit because of respiratory failure. He was diagnosed as severe pneumonia caused by avian influenza H7N9 viral infection. After initial clinical improvement supported by extracorporeal membrane oxygenation (ECMO), the patient's condition worsened with persistent fever, refractory hypoxemia. Chest x-rays and computed tomographies showed areas of consolidation and ground glass opacification. Although OP was suspected and 1 mg/kg methylprednisolone was used, the patient's condition didn't improved considerably. An open lung biopsy was performed, and histopathological examination of the specimen was compatible with OP. The patient was treated with methylprednisolone 1.5 mg/kg for 5 days. ECMO was weaned on day 15, and he was discharged on day 71 with good lung recovery., Conclusions: To the best of our knowledge, this was the first case of successful management of refractory severe respiratory failure caused by avian influenza H7N9 infection complicated with OP. Refractory hypoxia with clinical manifestation and radiological findings compatible with OP, a differential diagnosis should be considered among patients at the second or third week of influenza H7N9 infection, especially in patients with clinical condition deteriorated after the primary influenza pneumonia was controlled. And a steroid dose of methylprednisolone 1.5 mg/kg may be suggested for treatment of OP associated with avian influenza H7N9 infection.