Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

To control the spread of Corona Virus Disease , screening large numbers of suspected cases for appropriate quarantine and treatment is a priority.Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with significant false negative results. Fast and accurate diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we aimed to develop a deep learning method that could extract COVID-19's graphical features in order to provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control.Methods：We collected 1,119 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results:The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion:These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.

The fiber structures of tumor microenvironment (TME) are well‐known in regulating tumor cell behaviors, and the plastic remolding of TME has recently been suggested to enhance tumor metastasis as well. However, the interrelationship between the fiber microarchitecture and matrix plasticity is inextricable by existing in vitro models. The individual roles of fiber microarchitecture and matrix plasticity in tuning tumor cell behaviors remain elusive. This study develops an interpenetrating collagen–alginate hydrogel platform with independently tunable matrix plasticity and fiber microarchitecture through an interpenetrating strategy of alginate networks and collagen I networks. With this hydrogel platform, it is demonstrated that tumor cells in high plasticity hydrogels are more extensive and aggressive than in low plasticity hydrogels and fiber structures only have influence in high plasticity hydrogels. The study further elucidates the underlying mechanisms through analyzing the distribution of forces within the matrix and tracking the focal adhesions (FAs) and finds that highly plastic hydrogels can activate the FAs formation, whereas the maturation and stability of FAs are dominated by fiber dispersion. This study not only establishes new ideas on how cells interact with TME cues but also would help to further finely tailor engineered hydrogel platforms for studying tumor behaviors in vitro.

Inside Back Cover In article number 2200570, Ma, Xu, and co‐workers highlight the biophysical cue, a new hallmark of cancer, serving as a significant factor in tumor microenvironment forming a physical microenvironment, including extracellular matrix microarchitecture, stiffness, interstitial fluid pressure, and solid stress. It plays an essential role in affecting radiotherapy efficacy. Meanwhile, radiotherapy also affects tumor physical microenvironment, leading to different cancer cell behaviors. Targeting tumor physical microenvironment may greatly improve radiosensitivity.

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