Jiufa Cui

Bio: Jiufa Cui is an academic researcher from Qingdao University. The author has contributed to research in topics: Medicine & Radiology. The author has an hindex of 1, co-authored 2 publications receiving 1626 citations.

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV).

Abstract: In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.

Correlation between lower lumbar multifidus muscles fatty atrophy and corresponding level degenerative diseases in patients with low back pain using MRI

Abstract: To investigate the potential correlation between multifidus muscles (MM) fatty atrophy and MR signs of lumbar spine degeneration in patients with low back pain (LBP). Following IRB approval, lumbar spine MRI of 518 patients (278 females and 240 males; age, 10–92 years) with LBP were retrospectively reviewed by two experienced musculoskeletal radiologists. MM fatty atrophy was graded at L4–5 and L5–S1 levels per modified Goutallier classification. Disc degeneration, herniation was graded according to Pfirrmann classification, and facet joint osteoarthritis was graded according to Fujiwara classification. Gender, age and body mass index (BMI) were recorded as well. Mann–Whitney U test was used to determine whether there was a difference in fatty atrophy between woman and man. Kendall’s tau-b coefficient and ordinal multiple logistic regression were used to evaluate relationship between MM fatty atrophy and signs of lumbar spine degeneration. Intra-observer agreement was tested using weighted Kappa. There was significant difference among the grades of fatty atrophy between woman and man. Both at L4–L5 and L5–S1 levels, correlation between MM fatty atrophy with age, disc degeneration, facet joint osteoarthritis was significant (p < 0.001). No correlation was found between MM fatty atrophy with BMI, disc herniation at L4–5 (p = 0.187, 0.307) and L5–S1 (p = 0.307, 0.927). Among the independent variables included in the logistic regression model, gender, age and facet joint osteoarthritis at L4–5 level were statistically significant. Older people (OR = 1.11 at L4–5 level; OR = 1.08 at L5–S1 level), women (OR = 5.88 at L4–5 level; OR = 7.46 at L5–S1 level) with higher-grade facet joint osteoarthritis at L4–5 level (OR = 0.14, 0.26, 0.34; Grade 4 as reference) are more prone to MM fat degeneration than younger people, man and lower-grade facet joint osteoarthritis (p < 0.001). Intra-observer agreement was good with Kappa value range from 0.79 to 0.98. In patients with low back pain, MM fatty atrophy in the lower lumbar spine was related to age, gender, facet joint osteoarthritis at L4–5 levels.

Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases.

Abstract: Chest CT had higher sensitivity for diagnosis of COVID-19 as compared with initial reverse-transcription polymerase chain reaction from swab samples in the epidemic area of China.

The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status.

Abstract: An acute respiratory disease, caused by a novel coronavirus (SARS-CoV-2, previously known as 2019-nCoV), the coronavirus disease 2019 (COVID-19) has spread throughout China and received worldwide attention. On 30 January 2020, World Health Organization (WHO) officially declared the COVID-19 epidemic as a public health emergency of international concern. The emergence of SARS-CoV-2, since the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, marked the third introduction of a highly pathogenic and large-scale epidemic coronavirus into the human population in the twenty-first century. As of 1 March 2020, a total of 87,137 confirmed cases globally, 79,968 confirmed in China and 7169 outside of China, with 2977 deaths (3.4%) had been reported by WHO. Meanwhile, several independent research groups have identified that SARS-CoV-2 belongs to β-coronavirus, with highly identical genome to bat coronavirus, pointing to bat as the natural host. The novel coronavirus uses the same receptor, angiotensin-converting enzyme 2 (ACE2) as that for SARS-CoV, and mainly spreads through the respiratory tract. Importantly, increasingly evidence showed sustained human-to-human transmission, along with many exported cases across the globe. The clinical symptoms of COVID-19 patients include fever, cough, fatigue and a small population of patients appeared gastrointestinal infection symptoms. The elderly and people with underlying diseases are susceptible to infection and prone to serious outcomes, which may be associated with acute respiratory distress syndrome (ARDS) and cytokine storm. Currently, there are few specific antiviral strategies, but several potent candidates of antivirals and repurposed drugs are under urgent investigation. In this review, we summarized the latest research progress of the epidemiology, pathogenesis, and clinical characteristics of COVID-19, and discussed the current treatment and scientific advancements to combat the epidemic novel coronavirus.

Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR

Abstract: In a series of 51 patients with chest CT and real-time polymerase chain reaction assay (RT-PCR) performed within 3 days, the sensitivity of CT for 2019 novel coronavirus infection was 98% and that ...

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Abstract: The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.