scholarly journals Novel coronavirus pneumonia: Clinical manifestation and computed tomography features

Author(s):  
Xiao Wang ◽  
Weijian Wang ◽  
Yong Zhang ◽  
Jingliang Cheng
2016 ◽  
pp. 106-109
Author(s):  
Hoang Minh Thi Nguyen ◽  
Huu Tri Nguyen ◽  
Thanh Thao Nguyen

Obturator hernia is a rare pelvic hernia which accounts for 1% of all abdominal hernia. Clinical manifestation is ussually unspecific. Obturator hernia is often diagnosed by computed tomography or ultrasound. We present a case of obturator hernia in an elderly women who was successfully diagnosed and treated at Hue Univeristy of Medicine and Pharmacy. Key words: obturator hernia, mechanical obstruction, intestinal obstruction, Richter obturator hernia, strangulation


2020 ◽  
Vol 144 (10) ◽  
pp. 1217-1222 ◽  
Author(s):  
Hui Yang ◽  
Bin Hu ◽  
Sudong Zhan ◽  
Li-ye Yang ◽  
Guoping Xiong

Context.— The pandemic of a novel coronavirus, termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented global health burden. Objective.— To investigate the effect of the SARS-CoV-2 infection on maternal, fetal, and neonatal morbidity and other poor obstetrical outcomes. Design.— All suspected cases of pregnant women with coronavirus disease 2019 (COVID-19) admitted into one center in Wuhan from January 20 to March 19, 2020, were included. Detailed clinical data of those pregnancies with COVID-19 were retrospectively collected and analyzed. Results.— Twenty-seven pregnant women (4 early pregnancies included) with laboratory or clinically confirmed SARS-CoV-2 infection and 24 neonates born to the 23 women in late pregnancy were analyzed. On admission, 46.2% (13 of 27) of the patients had symptoms, including fever (11 of 27), cough (9 of 27), and vomiting (1 of 27). Decreased total lymphocytes count was observed in 81.5% (22 of 27) of patients. Twenty-six patients showed typical viral pneumonia by chest computed tomography scan, whereas 1 patient confirmed with COVID-19 infection showed no abnormality on chest computed tomography. One mother developed severe pneumonia 3 days after her delivery. No maternal or perinatal death occurred. Moreover, 1 early preterm newborn born to a mother with the complication of premature rupture of fetal membranes, highly suspected to have SARS-CoV-2 infection, was SARS-CoV-2 negative after repeated real-time reverse transcriptase polymerase chain reaction testing. Statistical differences were observed between the groups of women in early and late pregnancy with COVID-19 in the occurrence of lymphopenia and thrombocytopenia. Conclusions.— No major complications were reported among the studied cohort, though 1 serious case and 1 perinatal infection were observed. Much effort should be made to reduce the pathogenic effect of COVID-19 infection in pregnancies.


2020 ◽  
Vol 21 (4) ◽  
pp. 501 ◽  
Author(s):  
Jiangping Wei ◽  
Huaxiang Xu ◽  
Jingliang Xiong ◽  
Qinglin Shen ◽  
Bing Fan ◽  
...  

2020 ◽  
Vol 12 (5) ◽  
pp. 1011-1023 ◽  
Author(s):  
Nilanjan Dey ◽  
V. Rajinikanth ◽  
Simon James Fong ◽  
M. Shamim Kaiser ◽  
Mufti Mahmud

Abstract The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.


2021 ◽  
Author(s):  
Ramamurthy S. Komatlapalli ◽  
Abhishek Jagdishchander Arora ◽  
Rajani Thakur

Abstract Context: Quantitative and semi-quantitative indicators of lung involvement in COVID-19 could help to stratify the patients and thus help in triaging and speeding up the entire workflow in hospitals as patients with higher severity scores require early therapeutic intervention and critical care.Objective: To calculate Computed Tomography (CT) severity score for COVID-19 infection based on lobar involvement of the disease and correlate the score with oxygen saturation levels (SpO2) of the patient and further predict oxygen therapy requirement.Settings and Design: Prospective study.Methods and Material: This is a prospective study of 154 proven novel coronavirus (SARS-CoV-2) infected (COVID-19) patients. SpO2 values of all the patients were obtained within 6 hours of scan. All the scans were reviewed and semi-quantitative CT score was calculated based on the extent of lobar involvement Statistical analysis used: Scatter plot correlation and ROC curve analysis were performed. Results: CT score and SpO2 values of patients were plotted in scatter plot chart and Pearson correlation co-efficient (r) was calculated, which was -0.836 suggesting a strong negative correlation. Forty-six patients were given oxygen therapy and they had oxygen saturation value ≤ 94% with CT score ranging from 10-22. ROC curve analysis was performed to determine and reach an optimum cut off value of 11 for oxygen therapy requirement with sensitivity and specificity of 95.83% and 95.58% respectively. Conclusions: CT score in COVID-19 patients has strong negative correlation with oxygen saturation and it definitely helped to predict the requirement of the oxygen therapy in our study.


Author(s):  
Ling JIANG ◽  
Shao-Hua LIN ◽  
Jun-Jie LIN

We report a case of atypical clinical manifestation of pneumonia infected by 2019-novel coronavirus, which is helpful to improve the understanding of the clinical characteristics of pneumonia caused by the virus. At the same time, some suggestions on the discharge criteria and hierarchical management of admission of 2019-nCoV pneumonia are put forward. The results are constructive for effective prevention and control of 2019-nCoV pneumonia and optimizing patient process management in China.


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