COVID-19 classification of X-ray images using deep neural networks.

OBJECTIVES: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. METHODS: In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. RESULTS: Our model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97). CONCLUSION: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. KEY POINTS: • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings.

Mass spectrometry reveals the chemistry of formaldehyde cross-linking in structured proteins.

Whole-cell cross-linking coupled to mass spectrometry is one of the few tools that can probe protein-protein interactions in intact cells. A very attractive reagent for this purpose is formaldehyde, a small molecule which is known to rapidly penetrate into all cellular compartments and to preserve the protein structure. In light of these benefits, it is surprising that identification of formaldehyde cross-links by mass spectrometry has so far been unsuccessful. Here we report mass spectrometry data that reveal formaldehyde cross-links to be the dimerization product of two formaldehyde-induced amino acid modifications. By integrating the revised mechanism into a customized search algorithm, we identify hundreds of cross-links from in situ formaldehyde fixation of human cells. Interestingly, many of the cross-links could not be mapped onto known atomic structures, and thus provide new structural insights. These findings enhance the use of formaldehyde cross-linking and mass spectrometry for structural studies.