SARS-CoV-2 Assembly and Egress Pathway Revealed by Correlative Multi-modal Multi-scale Cryo-imaging

Since the outbreak of the SARS-CoV-2 pandemic, there have been intense structural studies on purified recombinant viral components and inactivated viruses. However, investigation of the SARS-CoV-2 infection in the native cellular context is scarce, and there is a lack of comprehensive knowledge on SARS-CoV-2 replicative cycle. Understanding the genome replication, assembly and egress of SARS-CoV-2, a multistage process that involves different cellular compartments and the activity of many viral and cellular proteins, is critically important as it bears the means of medical intervention to stop infection. Here, we investigated SARS-CoV-2 replication in Vero cells under the near-native frozen-hydrated condition using a unique correlative multi-modal, multi-scale cryo-imaging approach combining soft X-ray cryo-tomography and serial cryoFIB/SEM volume imaging of the entire SARS-CoV-2 infected cell with cryo-electron tomography (cryoET) of cellular lamellae and cell periphery, as well as structure determination of viral components by subtomogram averaging. Our results reveal at the whole cell level profound cytopathic effects of SARS-CoV-2 infection, exemplified by a large amount of heterogeneous vesicles in the cytoplasm for RNA synthesis and virus assembly, formation of membrane tunnels through which viruses exit, and drastic cytoplasm invasion into nucleus. Furthermore, cryoET of cell lamellae reveals how viral RNAs are transported from double-membrane vesicles where they are synthesized to viral assembly sites; how viral spikes and RNPs assist in virus assembly and budding; and how fully assembled virus particles exit the cell, thus stablishing a model of SARS-CoV-2 genome replication, virus assembly and egress pathways.

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19

PurposeTo improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. Materials and MethodsA published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. ResultsTuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. ConclusionsPerformance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

COVID-19 Outpatient Screening: a Prediction Score for Adverse Events

BackgroundWe sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak. MethodsSingle-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed ratio (E/O). Discrimination was assessed by C-statistics (AUC). ResultsIn the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. ConclusionsCoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.

Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks

PurposeTo develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease evaluation and clinical risk stratification. Materials and MethodsA convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on anterior-posterior CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ~160,000 images from CheXpert and transfer learning on 314 CXRs from patients with COVID-19. The algorithm was evaluated on internal and external test sets from different hospitals, containing 154 and 113 CXRs respectively. The PXS score was correlated with a radiographic severity score independently assigned by two thoracic radiologists and one in-training radiologist. For 92 internal test set patients with follow-up CXRs, the change in PXS score was compared to radiologist assessments of change. The association between PXS score and subsequent intubation or death was assessed. ResultsThe PXS score correlated with the radiographic pulmonary disease severity score assigned to CXRs in the COVID-19 internal and external test sets ({rho}=0.84 and {rho}=0.78 respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment ({rho}=0.74). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operator characteristic curve=0.80 (95%CI 0.75-0.85)). ConclusionA Siamese neural network-based severity score automatically measures COVID-19 pulmonary disease severity in chest radiographs, which can be scaled and rapidly deployed for clinical triage and workflow optimization. SUMMARYA convolutional Siamese neural network-based algorithm can calculate a continuous radiographic pulmonary disease severity score in COVID-19 patients, which can be used for longitudinal disease evaluation and clinical risk stratification. KEY RESULTSO_LIA Siamese neural network-based severity score correlates with radiologist-annotated pulmonary disease severity on chest radiographs from patients with COVID-19 ({rho}=0.84 and {rho}=0.78 in internal and external test sets respectively). C_LIO_LIThe direction of change in the severity score in follow-up radiographs is concordant with radiologist assessment ({rho}=0.74). C_LIO_LIThe admission chest radiograph severity score can help predict subsequent intubation or death within three days of admission (receiver operator characteristic area under the curve=0.80). C_LI

Acute liver injury secondary to sertraline.

Sertraline is widely prescribed to treat depression and anxiety disorders. However, hepatitis secondary to its use is a rare entity. We report the case of a 26-year-old woman in her 20th week of pregnancy presented with nausea, vomiting, malaise and dark urine. This occurred 6 months after sertraline 50 mg daily was started for the treatment of depression. Three weeks prior to her presentation, the dose of sertraline was increased to 100 mg daily. The patient's liver biochemical profile demonstrated increased transaminases. The biopsy of the liver showed lobular hepatitis, with a mild prominence of eosinophils, suggestive of a drug-induced or toxin-induced aetiology. Extensive biochemical work-up failed to show any other pathology to account for her hepatitis. Liver function tests normalised after cessation of sertraline, indicating a probable association between sertraline use and acute hepatocellular injury in our patient.

Active surveillance for prostate cancer: an Australian experience.

OBJECTIVE: To assess the patient and cancer characteristics as well as outcomes of a large cohort of Australian men who chose active surveillance (AS) as initial management of their low-risk prostate cancer. PATIENTS AND METHODS: Men treated by one surgeon who had chosen AS as the primary management for prostate cancer were identified from the records. The patient and cancer data recorded included: patient age, prostate-specific antigen (PSA) concentration at diagnosis, mode of prostate cancer detection. For prostate cancer diagnosed at prostate biopsy, data were collected for the number of cores taken as well as positive core number, cancer burden, and Gleason grade. Survival analysis was used to determine the duration of AS. RESULTS: In all, 154 men with low-risk prostate cancer with a median (range) age 63.0 (36-81) years and a mean (range) PSA concentration of 6.5 (0.3-22) ng/mL underwent AS. The median (range) duration of AS was 1.9 (0.1-16.6) years. AS was ceased in 29 patients (19%) after a mean (range) of 2.4 (0.2-7.9) years. Of these, 26 were upstaged, one chose curative treatment despite stable disease, and two died from disease not related to prostate cancer. Actuarial analysis on the probability of still being on AS after 5 years was 61.9% (95% confidence interval [CI] 46.2-74.2%) and after 10 years was 45.0% (95% CI 21.3-66.2%). While the period of follow-up is short, there were no biochemical recurrences in men who underwent curative treatment and no deaths from prostate cancer. CONCLUSION: AS is an acceptable mode of initial treatment in Australian men with low-risk prostate cancer.

Contraceptive methods and induced abortions and their association with the risk of colon cancer in Shanghai, China.

267400 female textile workers in Shanghai, who were administered a questionnaire at enrollment into a randomised trial of breast self-examination between October 1989 and October 1991, were followed up until the middle of 2000. Based on the 655 women who developed colon cancer, rate ratios (RRs) were estimated and trends in risk assessed using Cox Proportional Hazards Models. Risk was increased in women who used oral contraceptives for over 3 years (RR=1.56, 95% Confidence Interval (CI) 1.01-2.40). A possible increase in risk was also observed in women who received progestational injections during pregnancy (RR=1.24, 95% CI 0.95-1.62), but not in relation to the use of injectable contraceptives. A possible reduction in risk was associated with tubal ligation (RR=0.86, 95% CI 0.71-1.03) and ever having had an induced abortion (RR=0.84, 95% CI 0.71-1.00). No trends in risk were observed in relation to the duration of hormonal contraceptive use or the number of induced abortions. Additional studies of the possible roles contraceptives may play in the aetiology of colon cancer in women at low risk of this disease are warranted.