Early arrival did not ensure the early acquisition of intravenous thrombosis for acute ischemic stroke during the COVID-19 pandemic.

BACKGROUND: Intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) is a time-dependent treatment with a narrow therapeutic time window, in which the time delay could result from the deadline effect. METHODS: One hospital-based cohort was recruited to detect the factors contributing to the deadline effect, where patients with the deadline effect were defined as those who were presented with the onset-to-door time (ODT) in the first 50%, while the door-to-needle time (DNT) was in the last quartile. DNT (in-hospital delay) was further subdivided into several time intervals [door-to-examination time (DET), door-to-imaging time (DIT), door-to-laboratory time (DLT), and decision-making time (DMT) of the patients or their proxies. RESULTS: A total of 186 IVT cases were enrolled, of which 17.2% (32/186) suffered a delay of the deadline effect. The median age was 66 years, and 35.5% were female. Baseline characteristics were similar between the two groups (all p > .05). For the comparisons of the time intervals, DIT (26 versus 15 min, p = .001) was significantly longer in the group with deadline effect, while the differences of DET, DLT, DMT, and ONT did not reach statistical significance (all p > .05). Upon multivariable adjustment in the binary logistic regression model, longer DIT [odds ratio (OR), 1.076; 95% confidence interval (CI), 1.036-1.118; p < .001], and history of coronary heart disease (OR, 3.898; 95%CI, 1.415-10.735; p = .008) were independently associated with deadline effect in the binary logistic regression model, while admitted in the working day (OR, 0.674; 95%CI, 0.096-0.907; p = .033), and having medical insurance (OR, 0.350; 95% CI, 0.132-0.931; p = .035) were negatively associated with the deadline effect. CONCLUSIONS: A speed-safety tradeoff phenomenon from the deadline effect was observed in 17.2% of IVT cases during the COVID-19 pandemic, where longer DIT contributed a lot to this time delay. Patients without medical insurance, or admitted in official holidays were more likely to experience a delay of the deadline effect.

Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm

Generating molecules with desired properties is an important task in chemistry and pharmacy. An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19. Data mining and artificial intelligence may be good ways to find an efficient method. Recently, both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule's properties. However, existing methods need to be improved in efficiency and performance. To solve these problems, we propose a method named the Chemical Genetic Algorithm for Large Molecular Space (CALM). Specifically, CALM employs a scalable and efficient molecular representation called molecular matrix. Then, we design corresponding crossover, mutation, and mask operators inspired by domain knowledge and previous studies. We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization. The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods, where the z tests performed on the results of several experiments show that our method is more than 99% likely to be significant. At the same time, based on the experimental results, we point out the insufficiency in the experimental evaluation standard which affects the fair evaluation of previous work. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0970-3.

Impact of the COVID-19 pandemic on research on marine plastic pollution – A bibliometric-based assessment

Fighting the COVID-19 pandemic has led to a dramatic increase in plastic waste, which has had a huge impact on the environment, including the marine environment. This work is aimed to evaluate the pattern of national research cooperation, research hotspots, and research evolution before and during the epidemic by systematically reviewing the publications on marine plastic pollution during 2015-2019 (before the pandemic) 2020-2022 (during the pandemic) using the Systematic Literature Review and Latent Semantic Analysis. The results show (i) Compared to pre-pandemic, publications on marine pollution during the COVID-19 pandemic declined briefly and then increased sharply. (ii) Compared with before the pandemic, the national cooperation model has changed during the pandemic, and four major research centers have been formed: Central European countries centered on Italy;Nordic countries centered on United Kingdom;South Korea;Asia and Africa centered on India A developing country and a Pacific Rim country centered on United States and China. (iii) The knowledge map of keyword clustering does not change significantly before and during the COVID-19: ecosystem, spatial distribution, environmental governance and biodegradation. However, there are differences in the sub-category research of the four types of keywords. (iv) The impact of marine plastics on organisms and the governance of marine plastic pollution have become a branch of knowledge that has evolved rapidly during the pandemic. The governance of marine plastic pollution and microplastics are expected to become an important research direction.

How Knowledge Sharing Affects Business Model Innovation: An Empirical Study from the Perspective of Ambidextrous Organizational Learning

In the era of knowledge economy and open innovation, it is especially important for organizations to learn how to store and utilize internal and external knowledge for the sustainability of business models. The ability to innovate is a necessity for sustainable development, thus this paper starting from the internal factors driving enterprises to realize business model innovation, from perspective of ambidextrous organizational learning, takes 257 managers in enterprises as samples to empirically study the mechanism of knowledge sharing on business model innovation. The results of regression analysis and structural equation model (SEM) path analysis show that knowledge sharing affects novel and efficient business model innovation through ambidextrous organizational learning, and ambidextrous organizational learning plays a complete mediating role. Both explorative and exploitative learning have a significant positive impact on the novel and efficient business model innovation, and explorative learning has a stronger promoting effect. Therefore, in the practice of enterprise business model innovation, leaders need to establish a system that can promote the willingness of employees to share knowledge. Organizations need to pay attention to the effectiveness of explorative learning, consider the actual demand of employees as much as possible, and mobilize the initiative of employees in the learning process. Organizations also are required to pay attention to the balance between explorative learning and exploitative learning.

How is China’s energy security affected by exogenous shocks? Evidence of China–US trade dispute and COVID-19 pandemic

The trade dispute between China and the United States (US) since 2018 and the global COVID-19 pandemic since 2020 has significantly impacted China’s economic development. As China’s energy sources heavily depend on imports, its economic viability is becoming more and more risky. This study proposes a novel conceptual framework, involving macroeconomic, industrial and geopolitical factors, to evaluate China’s energy security as a major player in the trade dispute. This study also provides a comprehensive strategy for policymakers to make better decisions on reforming renewable energy patterns to guarantee energy security and achieve geopolitical advantages. The PESTEL (political, economic, social, technical, environmental and legislative) and SWOT (strengths, weaknesses, opportunities and threats) analytical methods are applied to evaluate the factors and attributes of China’s energy development and energy security in the current background. The China-US bipartite game reciprocity model and the QSPM (Quantitative Strategic Planning Matrix) analysis are conducted to assess which energy security strategy and policy are more suitable to deal with China-US trade dispute. To enhance energy security, China should diversify its energy supply chain, develop new sources of energy supply, advance the shale gas technology, popularise cleaner power-generation plants, increase nuclear-energy safety, introduce energy-conservation measures, promote alternative-energy vehicles, engage in international energy diplomacy, and rebuild international energy transaction and settlement systems.

Spontaneous Binding of Potential COVID-19 Drugs to Human Serine Protease TMPRSS2 (preprint)

Effective treatment or vaccine is not yet available for combating SARS coronavirus 2 (SARSCoV-2) that caused the COVID-19 pandemic. Recent studies showed that two drugs, Camostat and Nafamostat, might be repurposed to treat COVID-19 by inhibiting human TMPRSS2 required for proteolytic activation of viral spike (S) glycoprotein. However, their molecular mechanisms of pharmacological action remain unclear. Here, we perform molecular dynamics simulations to investigate their native binding sites on TMPRSS2. We revealed that both drugs could spontaneously and stably bind to the TMPRSS2 catalytic center, and thereby inhibit its proteolytic processing of the S protein. Also, we found that Nafamostat is more specific than Camostat for binding to the catalytic center, consistent with reported observation that Nafamostat blocks the SARS-CoV-2 infection at a lower concentration. Thus, this study provides mechanistic insights into the Camostat and Nafamostat inhibition of the SARS-CoV-2 infection, and offers useful information for COVID-19 drug development.

Blood Biomarkers Dynamic Detection, Especially Eosinophils has Certain Value in the Clinical Diagnosis, Severity Classification and Prognosis Judgment of COVID-19 (preprint)

In December 2019, coronavirus disease 2019 (COVID-19) was first found in Wuhan, China and soon was reported all around the world. Novel coronavirus (COVID-19) is highly infectious and requires early detection, isolation, and treatment. We tried to find some useful information by analyzing the covid-19 screening data, so as to provide help for clinical practice. In this prospective study, we retrospectively analyzed the clinical data of 131 patients with COVID-19 and 119 controls. For confirmed cases, the data of blood routine examination were analyzed among severe patients and non-severe group. The blood routine examination results were dynamically observed in the survivors and nonsurvivors. We find that patients with COVID-19 have lower counts of leucocytes, lymphocytes, eosinophils, which were compared with controls (P < 0.001). In severe group, patients have the lower count of lymphocytes and eosinophils, but the higher leucocytes count (all P values < 0.01). Eosinophils have high diagnostic efficacy analysis of severe COVID-19, and its area under the curve reached 0.750. Patients whose eosinophils returned to normal early had significantly longer survival times than those who did not(P < 0.001). Patients with COVID-19 have abnormal peripheral blood routine examination results. Dynamic surveillance of peripheral blood system especially eosinophils is helpful in the diagnosis, assess the prognosis and prediction of severe COVID-19 cases.

Fast assessment of human receptor-binding capability of 2019 novel coronavirus (2019-nCoV) (preprint)

The outbreaks of 2002/2003 SARS, 2012/2015 MERS and 2019/2020 Wuhan respiratory syndrome clearly indicate that genome evolution of an animal coronavirus (CoV) may enable it to acquire human transmission ability, and thereby to cause serious threats to global public health. It is widely accepted that CoV human transmission is driven by the interactions of its spike protein (S-protein) with human receptor on host cell surface; so, quantitative evaluation of these interactions may be used to assess the human transmission capability of CoVs. However, quantitative methods directly using viral genome data are still lacking. Here, we perform large-scale protein-protein docking to quantify the interactions of 2019-nCoV S-protein receptor-binding domain (S-RBD) with human receptor ACE2, based on experimental SARS-CoV S-RBD-ACE2 complex structure. By sampling a large number of thermodynamically probable binding conformations with Monte Carlo algorithm, this approach successfully identified the experimental complex structure as the lowest-energy receptor-binding conformations, and hence established an experiment-based strength reference for evaluating the receptor-binding affinity of 2019-nCoV via comparison with SARS-CoV. Our results show that this binding affinity is about 73% of that of SARS-CoV, supporting that 2019-nCoV may cause human transmission similar to that of SARS-CoV. Thus, this study presents a method for rapidly assessing the human transmission capability of a newly emerged CoV and its mutant strains, and demonstrates that post-genome analysis of protein-protein interactions may provide early scientific guidance for viral prevention and control.