Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

It was recently brought to our attention that our paper was missing information regarding when the patient chest computed tomography (CT) scans were obtained and that there were some discrepancies in the clinical metadata, associated with the very large image dataset, that we made publicly available through the China National Center for Bioinformation (http://ncov-ai.big.ac.cn/ download?lang=en). All of the chest CT and clinical metadata used in our prognostic analysis were collected from patients at the time of hospital admission, and we have now added this statement to the STAR Methods section of our paper. We believe that the errors in the clinical metadata were introduced when the chest CT images, clinical metadata, and codes were transferred to the web server, and we have now corrected the errors manually. Although these corrections do not alter any of the conclusions made in the paper, we do apologize for these errors and any confusion that they may have caused.

Accumulating evidence indicates that GABA acts beyond inhibitory synaptic transmission and regulates the development of inhibitory synapses in the vertebrate brain, but the underlying cellular mechanism is not well understood. We have combined live imaging of cortical GABAergic axons across time scales from minutes to days with single-cell genetic manipulation of GABA release to examine its role in distinct steps of inhibitory synapse formation in the mouse neocortex. We have shown previously, by genetic knockdown of GABA synthesis in developing interneurons, that GABA signaling promotes the maturation of inhibitory synapses and axons. Here we found that a complete blockade of GABA release in basket interneurons resulted in an opposite effect, a cell-autonomous increase in axon and bouton density with apparently normal synapse structures. These results not only demonstrate that GABA is unnecessary for synapse formation per se but also uncover a novel facet of GABA in regulating synapse elimination and axon pruning. Live imaging revealed that developing GABAergic axons form a large number of transient boutons, but only a subset was stabilized. Release blockade led to significantly increased bouton stability and filopodia density, increased axon branch extension, and decreased branch retraction. Our results suggest that a major component of GABA function in synapse development is transmission-mediated elimination of subsets of nascent contacts. Therefore, GABA may regulate activity-dependent inhibitory synapse formation by coordinately eliminating certain nascent contacts while promoting the maturation of other nascent synapses.

Common lung diseases are first diagnosed via chest X-rays. Here, we show that a fully automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by Coronavirus disease 2019 (COVID-19), assess its severity, and discriminate it from other types of pneumonia. The deep-learning system was developed by using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.88–0.99, between severe and non-severe COVID-19 with an AUC of 0.87, and between severe or non-severe COVID-19 pneumonia and other viral and non-viral pneumonia with AUCs of 0.82–0.98. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists, and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide clinical-decision support.

AIM: To investigate the characteristics and clinical value of chest computed tomography (CT) images of novel coronavirus pneumonia (NCP).MATERIALS AND METHODS: Clinical data and CT images of 80 cases of NCP were collected. The clinical manifestations and laboratory test results of the patients were analysed. The lesions in each lung segment of the patient's chest CT images were characterised. Lesions were scored according to length and diffusivity.RESULTS: The main clinical manifestations were fever, dry cough, fatigue, a little white sputum, or diarrhoea. A total of 1,702 scored lesions were found in the first chest CT images of 80 patients. The lesions were located mainly in the subpleural area of the lungs (92.4%). Most of the lesions were ground-glass opacity, and subsequent fusions could increase in range and spread mainly in the subpleural area. Pulmonary consolidation accounted for 44.1% of all of the lesions. Of the 80 cases, 76 patients (95%) had bilateral lung disease, four (5%) patients had unilateral lung disease, and eight (10%) patients had cord shadow.CONCLUSION: The chest CT of NCP patients is characterised by the onset of bilateral groundglass lesions located in the subpleural area of the lung, and progressive lesions that result in consolidation with no migratory lesions. Pleural effusions and mediastinal lymphadenopathy are rare. As patients can have inflammatory changes in the lungs alongside a negative early nucleic acid test, chest CT, in combination with epidemiological and laboratory tests, is a useful examination to evaluate the disease and curative effect.