Plasma cell myeloma initially diagnosed as light-chain deposition disease on liver biopsy: A case report and literature review.

RATIONALE: Light-chain deposition disease (LCDD) is a rare condition characterized by the abnormal deposition of monoclonal light chains (LCs) in multiple organs, leading to progressive organ dysfunction. Herein, we report a case of plasma cell myeloma initially diagnosed as LCDD on liver biopsy performed for prominent cholestatic hepatitis. PATIENT CONCERNS: A 55-year-old Korean man complained of dyspepsia as the main symptom. On abdominal computed tomography performed at another hospital, the liver showed mildly decreased and heterogeneous attenuation with mild periportal edema. Preliminary liver function tests revealed abnormal results. The patient was treated for an unspecified liver disease; however, his jaundice gradually worsened, prompting him to visit our outpatient hepatology clinic for further evaluation. Magnetic resonance cholangiography revealed liver cirrhosis with severe hepatomegaly of unknown cause. A liver biopsy was performed for the diagnosis. Hematoxylin and eosin staining revealed diffuse extracellular amorphous deposits in perisinusoidal spaces with compressed hepatocytes. The deposits, which morphologically resembled amyloids, were not stained by Congo red but stained strongly positive for kappa LCs and weakly positive for lambda LCs. DIAGNOSES: Therefore, the patient was diagnosed with LCDD. Further systemic examination revealed a plasma cell myeloma. INTERVENTIONS: Fluorescence in situ hybridization, cytogenetics, and next-generation sequencing tested in bone marrow showed no abnormalities. The patient initially received bortezomib/lenalidomide/dexamethasone as the treatment regimen for plasma cell myeloma. OUTCOMES: However, he died shortly thereafter because of coronavirus disease 2019 complications. LESSONS: This case demonstrates that LCDD may present with sudden cholestatic hepatitis and hepatomegaly, and may be fatal if patients do not receive appropriate and timely treatment because of delayed diagnosis. Liver biopsy is useful for the diagnosis of patients with liver disease of unknown etiology.

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments.

Impact of Government Intervention in Response to Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) led to the loss of lives and had serious social and economic effects. Countries implemented various quarantine policies to reduce the effects. The countries were divided into low- and high-risk groups based on the differences in quarantine policies and their levels of infection. Quarantine policies that significantly contributed to risk reduction were determined by analyzing 11 quarantine indicators for reducing the spread of COVID-19. The cross-tabulation and Chi-square tests were used to compare the quarantine policies by the groups. Multivariate logistic regression was used to determine the useful quarantine policies implemented by the low-risk group to verify quarantine policies for minimizing the negative effects. The analysis showed that the low- and medium-risk groups showed significant differences for 9 of the 11 indicators, and 4 of these differentiated the low- from the medium-risk group. Countries with strict quarantine policies related to workplace closure and staying at home were more likely to be included in the low-risk group. These policies had a significant impact in the low-risk countries and could contribute to reducing the spread and effects of COVID-19 in countries included in the high-risk group.

Prognostic Impact of Myosteatosis on Mortality in Hospitalized Patients with COVID-19.

Body composition, including sarcopenia, adipose tissue, and myosteatosis, is associated with unfavorable clinical outcomes in patients with coronavirus disease (COVID-19). However, few studies have identified the impact of body composition, including pre-existing risk factors, on COVID-19 mortality. Therefore, this study aimed to evaluate the effect of body composition, including pre-existing risk factors, on mortality in hospitalized patients with COVID-19. This two-center retrospective study included 127 hospitalized patients with COVID-19 who underwent unenhanced chest computed tomography (CT) between February and April 2020. Using the cross-sectional CT images at the L2 vertebra level, we analyzed the body composition, including skeletal muscle mass, visceral to subcutaneous adipose tissue ratio (VSR), and muscle density using the Hounsfield unit (HU). Of 127 patients with COVID-19, 16 (12.6%) died. Compared with survivors, non-survivors had low muscle density (41.9 vs. 32.2 HU, p < 0.001) and high proportion of myosteatosis (4.5 vs. 62.5%, p < 0.001). Cox regression analyses revealed diabetes (hazard ratio [HR], 3.587), myosteatosis (HR, 3.667), and a high fibrosis-4 index (HR, 1.213) as significant risk factors for mortality in patients with COVID-19. Myosteatosis was associated with mortality in hospitalized patients with COVID-19, independent of pre-existing prognostic factors.

Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis.

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.

Multicenter Analysis of Clinical Features and Prognosis of COVID-19 Patients with Hepatic Impairment.

Background/Aims: Recent data indicate the presence of liver enzyme abnormalities in patients with coronavirus disease 2019 (COVID-19). We aimed to evaluate the clinical features and treatment outcomes of COVID-19 patients with abnormal liver enzymes. Methods: We performed a retrospective, multicenter study of 874 COVID-19 patients admitted to five tertiary hospitals from February 20 to April 14, 2020. Data on clinical features, laboratory parameters, medications, and treatment outcomes were collected until April 30, 2020, and compared between patients with normal and abnormal aminotransferases. Results: Abnormal aminotransferase levels were observed in 362 patients (41.1%), of which 94 out of 130 (72.3%) and 268 out of 744 (36.0%) belonged to the severe and non-severe COVID- 19 categories, respectively. The odds ratios (95% confidence interval) for male patients, patients with a higher body mass index, patients with severe COVID-19 status, and patients with lower platelet counts were 1.500 (1.029 to 2.184, p=0.035), 1.097 (1.012 to 1.189, p=0.024), 2.377 (1.458 to 3.875, p=0.001), and 0.995 (0.993 to 0.998, p>0.001), respectively, indicating an independent association of these variables with elevated aminotransferase levels. Lopinavir/ ritonavir and antibiotic use increased the odds ratio of abnormal aminotransferase levels after admission (1.832 and 2.646, respectively, both p<0.05). The median time to release from quarantine was longer (22 days vs 26 days, p=0.001) and the mortality rate was higher (13.0% vs 2.9%, p<0.001) in patients with abnormal aminotransferase levels. Conclusions: Abnormal aminotransferase levels are common in COVID-19 patients and are associated with poor clinical outcomes. Multivariate analysis of patients with normal aminotransferase levels on admission showed that the use of lopinavir/ritonavir and antibiotics was associated with abnormal aminotransferase levels; thus, careful monitoring is needed.

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

PURPOSE: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. METHOD: We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. RESULTS: For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. CONCLUSION: The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

Fibrosis-4 index as a predictor for mortality in hospitalised patients with COVID-19: a retrospective multicentre cohort study.

OBJECTIVE: The reliable risk factors for mortality of COVID-19 has not evaluated in well-characterised cohort. This study aimed to identify risk factors for in-hospital mortality within 56 days in patients with severe infection of COVID-19. DESIGN: Retrospective multicentre cohort study. SETTING: Five tertiary hospitals of Daegu, South Korea. PARTICIPANTS: 1005 participants over 19 years old confirmed COVID-19 using real-time PCR from nasopharyngeal and oropharyngeal swabs. METHODS: The clinical and laboratory features of patients with COVID-19 receiving respiratory support were analysed to ascertain the risk factors for mortality using the Cox proportional hazards regression model. The relationship between overall survival and risk factors was analysed using the Kaplan-Meier method. OUTCOME: In-hospital mortality for any reason within 56 days. RESULTS: Of the 1005 patients, 289 (28.8%) received respiratory support, and of these, 70 patients (24.2%) died. In multivariate analysis, high fibrosis-4 index (FIB-4; HR 2.784), low lymphocyte count (HR 0.480), diabetes (HR 1.917) and systemic inflammatory response syndrome (HR 1.714) were found to be independent risk factors for mortality in patients with COVID-19 receiving respiratory support (all p<0.05). Regardless of respiratory support, survival in the high FIB-4 group was significantly lower than in the low FIB-4 group (28.8 days vs 44.0 days, respectively, p<0.001). A number of risk factors were also significantly related to survival in patients with COVID-19 regardless of respiratory support (0-4 risk factors, 50.2 days; 49.7 days; 44.4 days; 32.0 days; 25.0 days, respectively, p<0.001). CONCLUSION: FIB-4 index is a useful predictive marker for mortality in patients with COVID-19 regardless of its severity.

Clinical outcomes of coronavirus disease 2019 in patients with pre-existing liver diseases: A multicenter study in South Korea.

BACKGROUND/AIMS: Although coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, the implication of pre-existing liver disease on the outcome of COVID-19 remains unresolved.
. METHODS: A total of 1,005 patients who were admitted to five tertiary hospitals in South Korea with laboratory-confirmed COVID-19 were included in this study. Clinical outcomes in COVID-19 patients with coexisting liver disease as well as the predictors of disease severity and mortality of COVID-19 were assessed.
. RESULTS: Of the 47 patients (4.7%) who had liver-related comorbidities, 14 patients (1.4%) had liver cirrhosis. Liver cirrhosis was more common in COVID-19 patients with severe pneumonia than in those with non-severe pneumonia (4.5% vs. 0.9%, P=0.006). Compared to patients without liver cirrhosis, a higher proportion of patients with liver cirrhosis required oxygen therapy; were admitted to the intensive care unit; had septic shock, acute respiratory distress syndrome, or acute kidney injury; and died (P<0.05). The overall survival rate was significantly lower in patients with liver cirrhosis than in those without liver cirrhosis (log-rank test, P=0.003). Along with old age and diabetes, the presence of liver cirrhosis was found to be an independent predictor of severe disease (odds ratio, 4.52; 95% confidence interval [CI], 1.20-17.02;P=0.026) and death (hazard ratio, 2.86; 95% CI, 1.04-9.30; P=0.042) in COVID-19 patients.
. CONCLUSION: This study suggests liver cirrhosis is a significant risk factor for COVID-19. Stronger personal protection and more intensive treatment for COVID-19 are recommended in these patients.