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1.
Phys Med Biol ; 2021 Feb 19.
Article in English | MEDLINE | ID: covidwho-1091288

ABSTRACT

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 CAP patients underwent thin-section CT. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features. An infection Size Aware Random Forest method (iSARF) was used for classification. Experimental results show that the proposed method yielded best performance when using the handcrafted features with sensitivity of 91.6%, specificity of 86.8%, and accuracy of 89.8% over state-of-the-art classifiers. Additional test on 734 subjects with thick slice images demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. Furthermore, the data of extracted features will be made available after the review process.

2.
Aging (Albany NY) ; 132021 Feb 11.
Article in English | MEDLINE | ID: covidwho-1084990

ABSTRACT

Coronavirus disease 2019 (COVID-19)-associated coagulation dysfunction is gaining attention. In particular, dynamic changes in the D-dimer level may be related to disease progression. Here, we explored whether elevated D-dimer level was related to multiple organ failure and a higher risk of death. This study included 158 patients with COVID-19 who were admitted to the intensive care unit (ICU) at Jinyintan Hospital in Wuhan, China between January 20, 2020 and February 26, 2020. Clinical and laboratory data were collected. The relationship between D-dimer elevation and organ dysfunction was analyzed, as were dynamic changes in inflammation and lipid metabolism. Approximately 63.9% of patients with COVID-19 had an elevated D-dimer level on ICU admission. The 14 day ICU mortality rate was significantly higher in patients with a high D-dimer level than in those with a normal D-dimer level. Patients with a D-dimer level of 10-40µg/mL had similar organ function on ICU admission to those with a D-dimer level of 1.5-10µg/mL. However, patients with higher levels of D-dimer developed organ injuries within 7 days. Furthermore, significant differences in inflammation and lipid metabolism markers were observed between the two groups. In conclusion, the D-dimer level is closely related to COVID-19 severity and might influence the likelihood of rapid onset of organ injury after admission.

3.
Exp Hematol Oncol ; 10(1): 6, 2021 Feb 01.
Article in English | MEDLINE | ID: covidwho-1058277

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with coagulation abnormalities which are indicators of higher mortality especially in severe cases. METHODS: We studied patients with proven COVID-19 disease in the intensive care unit of Jinyintan Hospital, Wuhan, China from 30 to 2019 to 31 March 2020. RESULTS: Of 180 patients, 89 (49.44 %) had died, 85 (47.22 %) had been discharged alive, and 6 (3.33 %) were still hospitalised by the end of data collection. A D-dimer concentration of > 0.5 mg/L on admission was significantly associated with 30 day mortality, and a D-dimer concentration of > 5 mg/L was found in a much higher proportion of non-survivors than survivors. Sepsis-induced coagulopathy (SIC) and disseminated intravascular coagulation (DIC) scoring systems were dichotomised as < 4 or ≥ 4 and < 5 or ≥ 5, respectively, and the mortality rate was significantly different between the two stratifications in both scoring systems. Enoxaparin was administered to 68 (37.78 %) patients for thromboembolic prophylaxis, and stratification by the D-dimer concentration and DIC score confirmed lower mortality in patients who received enoxaparin when the D-dimer concentration was > 2 than < 2 mg/L or DIC score was ≥ 5 than < 5. A low platelet count and low serum calcium concentration were also related to mortality. CONCLUSIONS: A D-dimer concentration of > 0.5 mg/L on admission is a risk factor for severe disease. A SIC score of > 4 and DIC score of > 5 may be used to predict mortality. Thromboembolic prophylaxis can reduce mortality only in patients with a D-dimer concentration of > 2 mg/L or DIC score of ≥ 5.

4.
Syst Biol Reprod Med ; 66(6): 343-346, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1020158

ABSTRACT

The World Health Organization has declared the outbreak of the coronavirus disease COVID-19, caused by SARS-CoV-2, a pandemic. This novel infectious disease has rapidly become a global health threat. Currently, there are limited data on the extent of reproductive system damage caused by COVID-19. We reviewed the potential risks for complications in the reproductive system caused by COVID-19 infection. In addition, based on the latest American Society for Reproductive Medicine (ASRM), and European Society of Human Reproduction and Embryology (ESHRE), recommendations regarding clinical and patient management, we provide a series of suggestions for infection control measures in reproductive medicine departments. With the gradual restoration of reproductive care services, reproductive departments in epidemic areas should actively seek to minimize COVID-19 infection of both healthcare workers and patients.

5.
Preprint | SciFinder | ID: ppcovidwho-5431

ABSTRACT

The present study relates to report the diagnosis and treatment process of a case of coronavirus disease 2019(COVID-19) complicated with acute pulmonary embolism

6.
Preprint | SciFinder | ID: ppcovidwho-5231

ABSTRACT

A review To report one case of severe coronavirus disease 2019 (COVID19) with prolonged mech ventilation in prone position, and to review the related literature A case of severe COVID19 underwent prolonged(8 days) prostrate ventilation due to clin needs was retrospectively analyzed, and the literature was reviewed Respiratory compliance and oxygenation index were improved after prolonged prostration, but the outcomes were poor due to septic shock and multiple organ failure After sedation, COVID19 patients with mech ventilation can be considered to receive prone position ventilation after exclusion of contraindication If the patient′s oxygenation and respiratory mechanics improve, the time of prone position can be prolonged according to the situation

7.
Aging (Albany NY) ; 13(2): 1591-1607, 2020 12 09.
Article in English | MEDLINE | ID: covidwho-977831

ABSTRACT

Coagulation dysfunction in critically ill patients with coronavirus disease 2019 (COVID-19) has not been well described, and the efficacy of anticoagulant therapy is unclear. In this study, we retrospectively reviewed 75 fatal COVID-19 cases who were admitted to the intensive care unit at Jinyintan Hospital (Wuhan, China). The median age of the cases was 67 (62-74) years, and 47 (62.7%) were male. Fifty patients (66.7%) were diagnosed with disseminated intra-vascular coagulation. Approximately 90% of patients had elevated D-dimer and fibrinogen degradation products, which decreased continuously after anticoagulant treatment and was accompanied by elevated albumin (all P<0.05). The median survival time of patients treated with anticoagulant was 9.0 (6.0-14.0) days compared with 7.0 (3.0-10.0) days in patients without anticoagulant therapy (P=0.008). After anticoagulation treatment, C-reactive protein levels decreased (P=0.004), as did high-sensitivity troponin (P=0.018), lactate dehydrogenase (P<0.001), and hydroxybutyrate dehydrogenase (P<0.001). In conclusion, coagulation disorders were widespread among fatal COVID-19 cases. Anticoagulant treatment partially improved hypercoagulability, prolonged median survival time, and may have postponed inflammatory processes and cardiac injury.


Subject(s)
Blood Coagulation Disorders/virology , /complications , Aged , Anticoagulants/therapeutic use , Blood Coagulation Disorders/drug therapy , China , Female , Humans , Intensive Care Units , Male , Middle Aged , Retrospective Studies
8.
Med Image Anal ; 68: 101910, 2020 Nov 26.
Article in English | MEDLINE | ID: covidwho-943426

ABSTRACT

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

9.
Med Image Anal ; 67: 101824, 2021 01.
Article in English | MEDLINE | ID: covidwho-888729

ABSTRACT

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.


Subject(s)
/classification , Pneumonia, Viral/classification , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Disease Progression , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Radiographic Image Interpretation, Computer-Assisted , Radiography, Thoracic , Severity of Illness Index , Time Factors
10.
Clin Nutr ; 2020 Oct 01.
Article in English | MEDLINE | ID: covidwho-808531

ABSTRACT

OBJECTIVE: To evaluate the nutritional risk and therapy in severe and critical patients with COVID-19. METHODS: A total of 523 patients enrolled from four hospitals in Wuhan, China. The inclusion time was from January 2, 2020 to February 15. Clinical characteristics and laboratory values were obtained from electronic medical records, nursing records, and related examinations. RESULTS: Of these patients, 211 (40.3%) were admitted to the ICU and 115 deaths (22.0%). Patients admitted to the ICU had lower BMI and plasma protein levels. The median Nutrition risk in critically ill (NUTRIC) score of 211 patients in the ICU was 5 (4, 6) and Nutritional Risk Screening (NRS) score was 5 (3, 6). The ratio of parenteral nutrition (PN) therapy in non-survivors was greater than that in survivors, and the time to start nutrition therapy was later than that in survivors. The NUTRIC score can independently predict the risk of death in the hospital (OR = 1.197, 95%CI: 1.091-1.445, p = 0.006) and high NRS score patients have a higher risk of poor outcome in the ICU (OR = 1.880, 95%CI: 1.151-3.070, p = 0.012). After adjusted age and sex, for each standard deviation increase in BMI, the risk of in-hospital death was reduced by 13% (HR = 0.871, 95%CI: 0.795-0.955, p = 0.003), and the risk of ICU transfer was reduced by 7% (HR = 0.932, 95%CI:0.885-0.981, p = 0.007). The in-hospital survival time of patients with albumin level ≤35 g/L was significantly decreased (15.9 d, 95% CI: 13.7-16.3, vs 24.2 d, 95% CI: 22.3-29.7, p < 0.001). CONCLUSION: Severe and critical patients with COVID-19 have a high risk of malnutrition. Low BMI and protein levels were significantly associated with adverse events. Early nutritional risk screening and therapy for patients with COVID-19 are necessary.

12.
Syst Biol Reprod Med ; 66(6): 343-346, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-772823

ABSTRACT

The World Health Organization has declared the outbreak of the coronavirus disease COVID-19, caused by SARS-CoV-2, a pandemic. This novel infectious disease has rapidly become a global health threat. Currently, there are limited data on the extent of reproductive system damage caused by COVID-19. We reviewed the potential risks for complications in the reproductive system caused by COVID-19 infection. In addition, based on the latest American Society for Reproductive Medicine (ASRM), and European Society of Human Reproduction and Embryology (ESHRE), recommendations regarding clinical and patient management, we provide a series of suggestions for infection control measures in reproductive medicine departments. With the gradual restoration of reproductive care services, reproductive departments in epidemic areas should actively seek to minimize COVID-19 infection of both healthcare workers and patients.

13.
IEEE J Biomed Health Inform ; 24(10): 2798-2805, 2020 10.
Article in English | MEDLINE | ID: covidwho-742026

ABSTRACT

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/diagnosis , Tomography, X-Ray Computed/statistics & numerical data , Computational Biology , Coronavirus Infections/classification , Databases, Factual/statistics & numerical data , Deep Learning , Humans , Neural Networks, Computer , Pandemics/classification , Pneumonia, Viral/classification , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , Radiography, Thoracic/statistics & numerical data
14.
IEEE Trans Med Imaging ; 39(8): 2595-2605, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-690930

ABSTRACT

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.


Subject(s)
Coronavirus Infections/diagnostic imaging , Deep Learning , Image Interpretation, Computer-Assisted/methods , Pneumonia, Viral/diagnostic imaging , Algorithms , Betacoronavirus , Community-Acquired Infections/diagnostic imaging , Humans , Pandemics , ROC Curve , Radiography, Thoracic , Tomography, X-Ray Computed
15.
Int J Med Sci ; 17(12): 1773-1782, 2020.
Article in English | MEDLINE | ID: covidwho-680183

ABSTRACT

Rationale: Acute respiratory distress syndrome (ARDS) is one of the major reasons for ventilation and intubation management of COVID-19 patients but there is no noninvasive imaging monitoring protocol for ARDS. In this study, we aimed to develop a noninvasive ARDS monitoring protocol based on traditional quantitative and radiomics approaches from chest CT. Methods: Patients diagnosed with COVID-19 from Jan 20, 2020 to Mar 31, 2020 were enrolled in this study. Quantitative and radiomics data were extracted from automatically segmented regions of interest (ROIs) of infection regions in the lungs. ARDS existence was measured by Pa02/Fi02 <300 in artery blood samples. Three different models were constructed by using the traditional quantitative imaging metrics, radiomics features and their combinations, respectively. Receiver operating characteristic (ROC) curve analysis was used to assess the effectiveness of the models. Decision curve analysis (DCA) was used to test the clinical value of the proposed model. Results: The proposed models were constructed using 352 CT images from 86 patients. The median age was 49, and the male proportion was 61.9%. The training dataset and the validation dataset were generated by randomly sampling the patients with a 2:1 ratio. Chi-squared test showed that there was no significant difference in baseline of the enrolled patients between the training and validation datasets. The areas under the ROC curve (AUCs) of the traditional quantitative model, radiomics model and combined model in the validation dataset was 0.91, 0.91 and 0.94, respectively. Accordingly, the sensitivities were 0.55, 0.82 and 0.58, while the specificities were 0.97, 0.86 and 0.98. The DCA curve showed that when threshold probability for a doctor or patients is within a range of 0 to 0.83, the combined model adds more net benefit than "treat all" or "treat none" strategies, while the traditional quantitative model and radiomics model could add benefit in all threshold probability. Conclusions: It is feasible to monitor ARDS from CT images using radiomics or traditional quantitative analysis in COVID-19. The radiomics model seems to be the most practical one for possible clinical use. Multi-center validation with a larger number of samples is recommended in the future.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Lung/diagnostic imaging , Models, Theoretical , Pandemics , Pneumonia, Viral/complications , Tomography, X-Ray Computed , Adult , Algorithms , Area Under Curve , China/epidemiology , Coronavirus Infections/epidemiology , Datasets as Topic , Female , Humans , Image Processing, Computer-Assisted/methods , Male , Middle Aged , Pneumonia, Viral/epidemiology , ROC Curve , Retrospective Studies , Sampling Studies , Sensitivity and Specificity , Translational Medical Research/methods , Workflow
17.
Ann Transl Med ; 8(9): 594, 2020 May.
Article in English | MEDLINE | ID: covidwho-612191

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven. Methods: An automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence. Results: The VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from -549 to -450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from -149 to -50 HU was related to a lowered risk of ARDS. Conclusions: The automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.

18.
J Am Coll Radiol ; 17(7): e29-e36, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-276626

ABSTRACT

OBJETIVO: Describir las estrategias, manejo de emergencias y los procedimientos de control de infecciones de nuestro departamento durante el brote de la enfermedad por coronavirus 2019 (COVID-19). MéTODOS: Creamos un equipo de manejo de emergencias. El equipo estableció varias medidas: Reconfiguración del flujo de trabajo en el departamento de radiología, distribución de material de protección personal y adiestramiento del personal, procedimientos para la obtención de imágenes en pacientes sospechosos o confirmados con COVID-19, así como para pacientes sin historial de exposición o síntomas. Aquellos con sospecha o confirmación de COVID-19 fueron escaneados en una unidad dedicada para ello. RESULTADOS: Del 21 de enero del 2020 hasta el 9 de marzo del 2020, 3,083 personas con sospecha o confirmación de COVID-19 recibieron CT de torax. Incluyendo los exámenes iniciales y repetidos, el número total de CT fue 3,340. Como resultado de nuestras medidas de precaución, ninguno de los miembros del personal del departamento de radiología fue infectado con COVID-19. CONCLUSIóN: Las estrategias de planificación y las protecciones adecuadas pueden ayudar a proteger a los pacientes y al personal contra una enfermedad altamente infecciosa. Y a la misma vez ayudar a mantener la capacidad de atender un volumen alto de pacientes.

19.
Clin Gastroenterol Hepatol ; 18(12): 2848-2851, 2020 11.
Article in English | MEDLINE | ID: covidwho-276149
20.
Acad Radiol ; 27(7): 910-921, 2020 07.
Article in English | MEDLINE | ID: covidwho-176152

ABSTRACT

RATIONALE AND OBJECTIVES: We aimed to assess the prevalence of significant computed tomographic(CT) manifestations and describe some notable features based on chest CT images, as well as the main clinical features of patients with coronavirus disease 2019(COVID-19). MATERIALS AND METHODS: A systematic literature search of PubMed, EMBASE, the Cochrane Library, and Web of Science was performed to identify studies assessing CT features, clinical, and laboratory results of COVID-19 patients. A single-arm meta-analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI). RESULTS: A total of 14 articles (including 1115 patients) based on chest CT images were retrieved. In the lesion patterns on chest CTs, we found that pure ground-glass opacities (GGO) (69%, 95% CI 58-80%), consolidation (47%, 35-60%) and "air bronchogram sign" (46%, 25-66%) were more common than the atypical lesion of "crazy-paving pattern" (15%, 8-22%). With regard to disease extent and involvement, 70% (95% CI 46-95%) of cases showed a location preference for the right lower lobe, 65% (58-73%) of patients presented with ≥3 lobes involvement, and meanwhile, 42% (32-53%) of patients had involvement of all five lobes, while 67% (55-78%) of patients showed a predominant peripheral distribution. An understanding of some important CT features might be helpful for medical surveillance and management. In terms of clinical features, muscle soreness (21%, 95% CI 15-26%) and diarrhea (7%, 4-10%) were minor symptoms compared to fever (80%, 74-87%) and cough (53%, 33-72%). CONCLUSION: Chest CT manifestations in patients with COVID-19, as well as its main clinical characteristics, might be helpful in disease evolution and management.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Biomarkers/metabolism , Bronchography/methods , C-Reactive Protein/metabolism , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Cough/virology , Diarrhea/virology , Female , Fever/virology , Humans , Leukopenia/virology , Lung/pathology , Lung/virology , Lymphopenia/virology , Male , Middle Aged , Myalgia/virology , Pandemics , Pneumonia, Viral/pathology , Reverse Transcriptase Polymerase Chain Reaction , Thorax
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