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The emergence and spread of COVID-19 since December, 2019, has brought great challenges to global public health. As of April 23, 2020, more than 2•5 million confirmed cases and more than 175 000 deaths had been reported globally. 1 Respiratory tract manifestations such as fever and cough are the most commonly reported symptoms in patients with COVID-19. 2 Evidence of digestive system involvement in patients with COVID-19 was first reported by a group in China. 3 Emerging data showed that the gastrointestinal tract and liver might also represent target organs of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the basis of the findings that angiotensin-converting enzyme 2 (ACE2), the major receptor of SARS-CoV-2, is expressed in the gastro intestinal tract as well as liver cells. 4 The detection of SARS-CoV-2 viral RNA in patients' stool and the potential for faecal-oral transmission has raised

A novel coronavirus recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn). Source codes and datasets are available at our GitHub.

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Background A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. Conclusions The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

Cell‐free enzymatic catalysis (CFEC) is an emerging biotechnology that enable the biological transformations in complex natural networks to be imitated. This biomimetic approach allows industrial products such as biofuels and biochemical to be manufactured in a green manner. Nevertheless, the main challenge in CFEC is the poor stability, which restricts the effectiveness and lifetime of enzymes in sophisticated applications. Immobilization of the enzymes within solid carriers is considered an efficient strategy for addressing these obstacles. Specifically, putting an “armor‐like” porous metal–organic framework (MOF) exoskeleton tightly around the enzymes not only shields the enzymes against external stimulus, but also allows the selective transport of guests through the accessible porous network. Herein we present the concept of this biotechnology of MOF‐entrapped enzymes and its cutting‐edge applications.

BackgroundNivolumab 3 mg/kg every 2 weeks (Q2W) has shown benefit versus the standard of care in melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma (RCC). However, flat dosing is expected to shorten preparation time and improve ease of administration. With knowledge of nivolumab safety, efficacy, and pharmacokinetics across a wide dose range in body weight (BW) dosing, assessment of the benefit–risk profile of a 240-mg flat dose relative to the approved 3-mg/kg dose was approached by quantitative clinical pharmacology.Patients and methodsA flat dose of 240 mg was selected based on its equivalence to the 3-mg/kg dose at the median BW of ∼80 kg in patients in the nivolumab program. The benefit–risk profile of nivolumab 240 mg was evaluated by comparing exposures at 3 mg/kg Q2W and 240 mg Q2W across BW and tumor types; clinical safety at 3 mg/kg Q2W by BW and exposure quartiles in melanoma, NSCLC, and RCC; and safety and efficacy at 240 mg Q2W relative to 3 mg/kg Q2W in melanoma, NSCLC, and RCC.ResultsThe median nivolumab exposure and its distribution at 240 mg Q2W were similar to 3 mg/kg Q2W in the simulated population. Safety analyses did not demonstrate a clinically meaningful relationship between BW or nivolumab exposure quartiles and frequency or severity of adverse events. The predicted safety and efficacy were similar across nivolumab exposure ranges achieved with 3 mg/kg Q2W or 240 mg Q2W flat dose.ConclusionBased on population pharmacokinetic modeling, established flat exposure–response relationships for efficacy and safety, and clinical safety, the benefit–risk profile of nivolumab 240 mg Q2W was comparable to 3 mg/kg Q2W. The quantitative clinical pharmacology approach provided evidence for regulatory decision-making on dose modification, obviating the need for an independent clinical study.

This work reports a new protein-directed, hydrogen-bonded assembly strategy to organize proteins and organic linkers into robust hybrid frameworks. The pconjugated carboxylate linkers are feasible to be anchored on the peptide backbone of proteins through hydrogen-bonded interaction and then by