The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

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Background: Coronavirus disease 2019 (COVID-19) has infected more than 4 million people within 4 months. There is an urgent need to properly identify high-risk cases that are more likely to deteriorate even if they present mild diseases on admission. Methods: A multicenter nested case-control study was conducted in four designated hospitals in China enrolling confirmed COVID-19 patients who were mild on admission. Baseline clinical characteristics were compared between patients with stable mild illness (stable mild group) and those who deteriorated from mild to severe illness (progression group). Results: From Jan 17, 2020, to Feb 1, 2020, 85 confirmed COVID-19 patients were enrolled, including 16 in the progression group and 69 in the stable mild group. Compared to stable mild group (n = 69), patients in the progression group (n = 16) were more likely to be older, male, presented with dyspnea, with hypertension, and with higher levels of lactase dehydrogenase and c-reactive protein. In multivariate logistic regression analysis, advanced age (odds ratio [OR], 1.012; 95% confidence interval [CI], 1.020-1.166; P = 0.011) and the higher level of lactase dehydrogenase (OR, 1.012; 95% CI, 1.001-1.024; P = 0.038) were independently associated with exacerbation in mild COVID-19 patients. Conclusion: Advanced age and high LDH level are independent risk factors for exacerbation in mild COVID-19 patients. Among the mild patients, clinicians should pay more attention to the elderly patients or those with high LDH levels.

The catalytic asymmetric synthesis of the anti‐COVID‐19 drug Remdesivir has been realized by the coupling of the P‐racemic phosphoryl chloride with protected nucleoside GS441524. The chiral bicyclic imidazole catalyst used is crucial for the dynamic kinetic asymmetric transformation (DyKAT) to proceed smoothly with high reactivity and excellent stereoselectivity (96 % conv., 22:1 SP:RP). Mechanistic studies showed that this DyKAT is a first‐order visual kinetic reaction dependent on the catalyst concentration. The unique chiral bicyclic imidazole skeleton and carbamate substituent of the catalyst are both required for the racemization process, involving the phosphoryl chloride, and subsequent stereodiscriminating step. A 10 gram scale reaction was also conducted with comparably excellent results, showing its potential for industrial application.

Temporal object detection has attracted significant attention, but most popular detection methods can not leverage the rich temporal information in videos. Very recently, many different algorithms have been developed for video detection task, but real-time online approaches are frequently deficient. In this paper, based on attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal signal-shot detector (TSSD) for real-world detection. Distinct from previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure including a low-level temporal unit as well as a high-level one (HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal attention module is specially tailored for background suppression and scale suppression while a ConvLSTM integrates attention-aware features through time. An association loss is designed for temporal coherence. Besides, online tubelet analysis (OTA) is exploited for identification. Finally, our method is evaluated on ImageNet VID dataset and 2DMOT15 dataset. Extensive comparisons on the detection and tracking capability validate the superiority of the proposed approach. Consequently, the developed TSSD-OTA is fairly faster and achieves an overall competitive performance in terms of detection and tracking. The source code will be made available.