Measles-Associated Severe Pneumonia in a Patient with HBeAg-Negative Chronic Hepatitis B: A Case Report

Background: Every year, approximately 800,000 people die from liver diseases associated with hepatitis B virus (HBV) infection. Complications outside the liver are common, such as fungal lung infections and viral infections. These complications may be associated with poor immune function, thus making clinical treatment difficult and increasing the risk of death. Therefore, HBV-infection-related liver diseases are worthy of clinical attention and further research. Case summary: We report a case of HBeAg-negative chronic hepatitis B in which the patient received entecavir as an anti-HBV treatment after liver dysfunction. During the treatment, the patient was diagnosed with measles and severe viral pneumonia. After comprehensive treatment, including active antiviral medications and mechanical ventilation, the patient recovered and was discharged. Conclusion: HBV infection causes liver damage, affects immune function, and is likely to be associated with viral infections such as measles. Consequently, infections may lead to complications, such as severe viral pneumonia, that endanger patients’ lives. To decrease complications and mortality, better understanding of the disease is necessary to enable early diagnosis.

Agreement between low-dose and ultra-low-dose chest CT for the diagnosis of viral pneumonia imaging patterns during the COVID-19 pandemic

Background Chest CT scan has an important role in the diagnosis and management of COVID-19 infection. A major concern in radiologic assessment of the patients is the radiation dose. Research has been done to evaluate low-dose chest CT in the diagnosis of pulmonary lesions with promising findings. We decided to determine diagnostic performance of ultra-low-dose chest CT in comparison to low-dose CT for viral pneumonia during the COVID-19 pandemic. Results 167 patients underwent both low-dose and ultra-low-dose chest CT scans. Two radiologists blinded to the diagnosis independently examined ultra-low-dose chest CT scans for findings consistent with COVID-19 pneumonia. In case of any disagreement, a third senior radiologist made the final diagnosis. Agreement between two CT protocols regarding ground-glass opacity, consolidation, reticulation, and nodular infiltration were recorded. On low-dose chest CT, 44 patients had findings consistent with COVID-19 infection. Ultra-low-dose chest CT had sensitivity and specificity values of 100% and 98.4%, respectively for diagnosis of viral pneumonia. Two patients were falsely categorized to have pneumonia on ultra-low-dose CT scan. Positive predictive value and negative predictive value of ultra-low-dose CT scan were respectively 95.7% and 100%. There was good agreement between low-dose and ultra-low-dose methods (kappa = 0.97; P < 0.001). Perfect agreement between low-dose and ultra-low-dose scans was found regarding diagnosis of ground-glass opacity (kappa = 0.83, P < 0.001), consolidation (kappa = 0.88, P < 0.001), reticulation (kappa = 0.82, P < 0.001), and nodular infiltration (kappa = 0.87, P < 0.001). Conclusion Ultra-low-dose chest CT scan is comparable to low-dose chest CT for detection of lung infiltration during the COVID-19 outbreak while maintaining less radiation dose. It can also be used instead of low-dose chest CT scan for patient triage in circumstances where rapid-abundant PCR tests are not available.

Diagnostics of lesions of parenchymatic organs in COVID-19 with the application of digital software processing of computer tomography images

BACKGROUND. In the third wave of the pandemic, the coronavirus disease 2019 (COVID-19) was more aggressive. The available information on the pathogenesis of respiratory failure was supplemented with new data. Up-date information about the respiratory failure pathogenesis was acquired. It has been shown that the SARS-CoV-2 virus leads to disappearance of white pulp cells in the spleen. In this tissue immune cells mature and differentiate, among others T- and B-lymphocytes, which are responsible for premunition. The study of the structure and function of the spleen has become even more urgent. Some authors note a change in the size of the spleen during ultrasonography and chest computed tomography (CT), which correlate with indicators of the pneumonia“s severity. The study of the structure and function of the spleen has become even more urgent. OBJECTIVE. To study is to evaluate changes in the structure of solid organs (lungs, liver and spleen) in patients with a complicated community-acquired viral pneumonia COVID-19 by means of software digital processing of CT scan data and their comparison with pathomorphological changes. MATERIALS AND METHODS. The analysis of CT data in patients with a complicated community-acquired viral pneumonia COVID-19, who were treated at the SI “National institute of phthisiology and pulmonology named. F.G. Yanovsky of the NAMS of Ukraine”. CT WGC was performed on an Aquilion TSX-101A «Tochiba» scanner (Japan), followed by digital software processing of CT images using the Dragonfly software. Histological preparations were obtained as a result of traditional alcoholic histological tracing of tissue samples, embedded in paraffin blocks. To obtain micrographs, an Olympus BX51 microscope was used with an Olympus DP73 digital camera and a CellSens computer program for image processing. RESULTS AND DISCUSSION. The obtained results of digital software processing of CT images clearly correlate with autopsy histological examination of tissues of the same solid organs. Changes in the structure of the spleen occur earlier than in other solid organs, which gives reason to use these changes for diagnostic purposes. Digital processing of CT images of the spleen allows determining the severity of the disease, predicting its further course and evaluating the effectiveness of treatment. CONCLUSIONS. In patients with a complicated viral (COVID-19) community-acquired pneumonia changes (which can be determined by digital software processing of CT data) in the structure of solid organs, especially in lungs and spleen, were observed and they correlate with pathomorphological changes.

Acute aortic thrombosis in a patient with SARS-CoV-2 coronavirus disease (COVID-19)

One of the rare and life-threatening conditions is acute aortic thrombosis. We have described a case of thrombosis of the aorta and iliac arteries in a patient against the background of viral pneumonia COVID-19, with newly diagnosed diabetes mellitus and arterial hypertension.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis.Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs).Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71–1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03–1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73–1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05–1.40) with fungal pneumonia.Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT

ObjectiveTo develop an accurate and rapid computed tomography (CT)-based interpretable AI system for the diagnosis of lung diseases.BackgroundMost existing AI systems only focus on viral pneumonia (e.g., COVID-19), specifically, ignoring other similar lung diseases: e.g., bacterial pneumonia (BP), which should also be detected during CT screening. In this paper, we propose a unified sequence-based pneumonia classification network, called SLP-Net, which utilizes consecutiveness information for the differential diagnosis of viral pneumonia (VP), BP, and normal control cases from chest CT volumes.MethodsConsidering consecutive images of a CT volume as a time sequence input, compared with previous 2D slice-based or 3D volume-based methods, our SLP-Net can effectively use the spatial information and does not need a large amount of training data to avoid overfitting. Specifically, sequential convolutional neural networks (CNNs) with multi-scale receptive fields are first utilized to extract a set of higher-level representations, which are then fed into a convolutional long short-term memory (ConvLSTM) module to construct axial dimensional feature maps. A novel adaptive-weighted cross-entropy loss (ACE) is introduced to optimize the output of the SLP-Net with a view to ensuring that as many valid features from the previous images as possible are encoded into the later CT image. In addition, we employ sequence attention maps for auxiliary classification to enhance the confidence level of the results and produce a case-level prediction.ResultsFor evaluation, we constructed a dataset of 258 chest CT volumes with 153 VP, 42 BP, and 63 normal control cases, for a total of 43,421 slices. We implemented a comprehensive comparison between our SLP-Net and several state-of-the-art methods across the dataset. Our proposed method obtained significant performance without a large amount of data, outperformed other slice-based and volume-based approaches. The superior evaluation performance achieved in the classification experiments demonstrated the ability of our model in the differential diagnosis of VP, BP and normal cases.

Mesenchymal stem/stromal cell-based therapies for severe viral pneumonia: therapeutic potential and challenges

Severe viral pneumonia is a significant cause of morbidity and mortality globally, whether due to outbreaks of endemic viruses, periodic viral epidemics, or the rarer but devastating global viral pandemics. While limited anti-viral therapies exist, there is a paucity of direct therapies to directly attenuate viral pneumonia-induced lung injury, and management therefore remains largely supportive. Mesenchymal stromal/stem cells (MSCs) are receiving considerable attention as a cytotherapeutic for viral pneumonia. Several properties of MSCs position them as a promising therapeutic strategy for viral pneumonia-induced lung injury as demonstrated in pre-clinical studies in relevant models. More recently, early phase clinical studies have demonstrated a reassuring safety profile of these cells. These investigations have taken on an added importance and urgency during the COVID-19 pandemic, with multiple trials in progress across the globe. In parallel with clinical translation, strategies are being investigated to enhance the therapeutic potential of these cells in vivo, with different MSC tissue sources, specific cellular products including cell-free options, and strategies to ‘licence’ or ‘pre-activate’ these cells, all being explored. This review will assess the therapeutic potential of MSC-based therapies for severe viral pneumonia. It will describe the aetiology and epidemiology of severe viral pneumonia, describe current therapeutic approaches, and examine the data suggesting therapeutic potential of MSCs for severe viral pneumonia in pre-clinical and clinical studies. The challenges and opportunities for MSC-based therapies will then be considered.

Extensive bilateral diffuse infiltrates and deterioration of lung following infection with severe acute respiratory syndrome coronavirus 2 in a pregnant woman: a case report

Introduction Severe acute respiratory syndrome coronavirus 2 is the third member of the coronavirus family to cause global concern in the twenty-first century. Pregnant women are particularly at higher risk of developing severe viral pneumonia, possibly because of a partial immune suppression during their pregnancy. Under such critical and rapidly evolving circumstances, these poor findings might be helpful for the treatment of infected pregnant women with the 2019 novel coronavirus. Case presentation In this study, we report the case of a 33-year-old Asian pregnant woman at 25 gestational weeks with coronavirus disease 2019 who developed severe complications, including hypoxemia, acute respiratory distress syndrome, pulmonary infiltration, and bilateral pleural effusion. She died 1 month after admission to the hospital. Conclusion Pregnant populations are especially at higher risk of viral pneumonia development caused by severe acute respiratory syndrome coronavirus 2. Further research on the prevention and treatment of the new coronavirus is necessary.