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The trauma chronicles
\"'Never, never, never give in', Winston Churchill's famous quotation best sums up the life of Stephen Westaby, the world-leading cardiothoracic surgeon. This book chronicles the triumphs and failures of his surgical life, the lives saved and extended, the innovations (such as artificial hearts) he developed, and his research discoveries. Having spent his childhood in the backstreets of a northern steel town, he went on to become one of the world's preeminent heart surgeons. HIs drive for perfection in his profession took him to the world-renowned Harefield Hospital, the foremost heart surgery centre in Birmingham, Alabama, the newly-created Cardiothoracic Centre in Oxford, and then in 2019 in Wuhan he was the first Western doctor to learn about Covid before the virus was identified. Following on from his two earlier best-selling works, Fragile Lives and The Knife's Edge this volume is written with humour and a doctor's reverence for life and his patients. The Trauma Chronicles gives an unmissable insight into the world of one of the greatest living heart surgeons\"--Publisher's description.
Book
Correction: Normal Thoracic Radiographic Appearance of the Cynomolgus Monkey (Macaca fascicularis)
The correct author affiliation is “the First Affiliated Hospital of Chongqing Medical University.”
(2014) Normal Thoracic Radiographic Appearance of the Cynomolgus Monkey (Macaca fascicularis).
Journal Article
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT–PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT–PCR assay and next-generation sequencing RT–PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT–PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
Artificial intelligence algorithms integrating chest computed tomography scans and clinical information can diagnose COVID-19 with similar accuracy as compared to a senior radiologist.
Journal Article
Individualized CT Imaging Protocol: Could Neck Circumference be a New Somatometric Parameter to Adjust Appropriate CT Protocol to Each Patient in Thorax?
Objectives: To evaluate whether neck circumference (NC) is an appropriate somatometric parameter for determining thorax CT protocol specific to each individual to avoid unnecessary radiation. Methods: Seventy-six patients undergoing non-contrast thorax CT were enrolled in this study. NC, body weight and height were measured and BMI was calculated before the imaging. The effective dose(ED) of the patient was calculated as milisievert(mSv).The visual image quality was assessed using on a 5-point scale. Results: There were high correlations between BMI, NC, and weight. There were also high correlations between these three somatometric parameters and CT effective dose. The correlation between NC and ED (r=0.839) was higher than the correlation between BMI and ED (r=0.635). Conclusion: It might be more accurate to determine the tube voltage, which is a parameter of CT protocol, according to NC value as NC was correlated in dose changes more accurately and with a higher proportion instead of BMI.
Journal Article
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.
Journal Article
Mechanical Control of Morphogenesis by Fat/Dachsous/Four-Jointed Planar Cell Polarity Pathway
During animal development, several planar cell polarity (PCP) pathways control tissue shape by coordinating collective cell behavior. Here, we characterize by means of multiscale imaging epithelium morphogenesis in the Drosophila dorsal thorax and show how the Fat/Dachsous/Four-jointed PCP pathway controls morphogenesis. We found that the proto-cadherin Dachsous is polarized within a domain of its tissue-wide expression gradient. Furthermore, Dachsous polarizes the myosin Dachs, which in turn promotes anisotropy of junction tension. By combining physical modeling with quantitative image analyses, we determined that this tension anisotropy defines the pattern of local tissue contraction that contributes to shaping the epithelium mainly via oriented cell rearrangements. Our results establish how tissue planar polarization coordinates the local changes of cell mechanical properties to control tissue morphogenesis.
Journal Article