Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia.

INTRODUCTION: Computed tomography (CT) can be effective for the early screening and diagnosis of COVID-19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission). METHODS: We included all COVID-19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3-10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground-glass opacity, GGO) and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity. RESULTS: The ratios of some representative features differed between the remission stage and the progression phase, such as round-shape lesion (8.0% vs 34.4%), GGO (65.0% vs 87.5%), consolidation (62.0% vs 31.3%), large cable sign (59.0% vs 9.4%) and crazy-paving sign (20.0% vs 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign and crazy-paving sign were the most significant factors, based on a variable named "prediction of progression or remission" (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875). CONCLUSION: CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy-paving sign, may increase the recognition of the intrapulmonary development of COVID-19.

[Analysis of genomic copy number variation for a Chinese patient with split hand/split foot malformation].

OBJECTIVE: To employ single nucleotide polymorphisms (SNP) microarray to detect copy number variations (CNVs) for the diagnosis of disease and molecular classification. METHODS: For a patient with split-hand/split-foot malformation, genome-wide copy number variants SNP microarray was applied. Tiny copy number variations were verified by real-time fluorescent quantitative PCR. RESULTS: The results of SNP microarray has revealed that the patient has carried a 0.39 Mb duplication in 10q24.31-24.32 (102 955 122-103 348 688), which has encompassed genes including LBX1, BTRC and POLL. By real-time fluorescent quantitative PCR, duplicate area encompassing the pathogenic genes have been verified. The results for LBX1, BTRC, POLL genes were all consistent with the SNP microarray test. Moreover, a duplication was detected in exon 9 of FBXW4 gene which is in nearby. CONCLUSION: SNP chips can efficiently identify tiny CNVs (< 1.0 Mb). In combination with real-time fluorescence quantitative PCR, this may provide valuable information for prenatal diagnosis.

A novel RUNX2 missense mutation predicted to disrupt DNA binding causes cleidocranial dysplasia in a large Chinese family with hyperplastic nails.

BACKGROUND: Cleidocranial dysplasia (CCD) is a dominantly inherited disease characterized by hypoplastic or absent clavicles, large fontanels, dental dysplasia, and delayed skeletal development. The purpose of this study is to investigate the genetic basis of Chinese family with CCD. METHODS: Here, a large Chinese family with CCD and hyperplastic nails was recruited. The clinical features displayed a significant intrafamilial variation. We sequenced the coding region of the RUNX2 gene for the mutation and phenotype analysis. RESULTS: The family carries a c.T407C (p.L136P) mutation in the DNA- and CBFbeta-binding Runt domain of RUNX2. Based on the crystal structure, we predict this novel missense mutation is likely to disrupt DNA binding by RUNX2, and at least locally affect the Runt domain structure. CONCLUSION: A novel missense mutation was identified in a large Chinese family with CCD with hyperplastic nails. This report further extends the mutation spectrum and clinical features of CCD. The identification of this mutation will facilitate prenatal diagnosis and preimplantation genetic diagnosis.