Metabolomic Profiling of Lungs from Mice Reveals the Variability of Metabolites in Pneumocystis Infection and the Metabolic Abnormalities in BAFF-R-Deficient Mice.

Purpose: The incidence of Pneumocystis pneumonia (PCP) in patients without human immunodeficiency virus (HIV) has been increasing. In this study, we aimed to investigate the metabolic changes in Pneumocystis infection and the metabolic abnormalities in B-cell-activating factor receptor (BAFF-R)-deficient mice with Pneumocystis infection. Methods: The important function of B cells during Pneumocystis infection is increasingly recognized. In this study, a Pneumocystis-infected mouse model was constructed in BAFF-R-/- mice and wild-type (WT) mice. Lungs of uninfected WT C57BL/6, WT Pneumocystis-infected, and BAFF-R-/- Pneumocystis-infected mice were used for metabolomic analyses to compare the metabolomic profiles among the groups, with the aim of exploring the metabolic influence of Pneumocystis infection and the influence of mature B-cell deficiency during infection. Results: The results indicated that many metabolites, mainly lipids and lipid-like molecules, were dysregulated in Pneumocystis-infected WT mice compared with uninfected WT C57BL/6 mice. The data also demonstrated significant changes in tryptophan metabolism, and the expression levels of key enzymes of tryptophan metabolism, such as indoleamine 2,3-dioxygenase 1 (IDO1), were significantly upregulated. In addition, B-cell development and function might be associated with lipid metabolism. We found a lower level of alitretinoin and the abnormalities of fatty acid metabolism in BAFF-R-/- Pneumocystis-infected mice. The mRNA levels of enzymes associated with fatty acid metabolism in the lung were upregulated in BAFF-R-/- Pneumocystis-infected mice and positively correlated with the level of IL17A, thus suggesting that the abnormalities of fatty acid metabolism may be associated with greater inflammatory cell infiltration in the lung tissue of BAFF-R-/- Pneumocystis-infected mice compared with the WT Pneumocystis-infected mice. Conclusion: Our data revealed the variability of metabolites in Pneumocystis-infected mice, suggesting that the metabolism plays a vital role in the immune response to Pneumocystis infection.

Research on Supply Chain Financial Risk Prevention Based on Machine Learning.

Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data

Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data;innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time;and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows 'centripetal centralization at daytime, centrifugal dispersion at night';spatiotemporal variation characteristics, that the interaction between the purpose of residents' activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China's 'surgical control and dynamic zero COVID-19';epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues.

Optimization, validation and analysis of a 2019-nCoV nucleic acid detection kit. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To optimize the sensitivity and specificity of a 2019-nCoV nucleic acid detection kit, so as to improve the positive detection rate and provide guidance for clinical use by comparison with different kits.

Retrospective Study of Critically Ill COVID-19 Patients With and Without Extracorporeal Membrane Oxygenation Support in Wuhan, China.

Background: Extracorporeal membrane oxygenation (ECMO) might benefit critically ill COVID-19 patients. But the considerations besides indications guiding ECMO initiation under extreme pressure during the COVID-19 epidemic was not clear. We aimed to analyze the clinical characteristics and in-hospital mortality of severe critically ill COVID-19 patients supported with ECMO and without ECMO, exploring potential parameters for guiding the initiation during the COVID-19 epidemic. Methods: Observational cohort study of all the critically ill patients indicated for ECMO support from January 1 to May 1, 2020, in all 62 authorized hospitals in Wuhan, China. Results: Among the 168 patients enrolled, 74 patients actually received ECMO support and 94 not were analyzed. The in-hospital mortality of the ECMO supported patients was significantly lower than non-ECMO ones (71.6 vs. 85.1%, P = 0.033), but the role of ECMO was affected by patients' age (Logistic regression OR 0.62, P = 0.24). As for the ECMO patients, the median age was 58 (47-66) years old and 62.2% (46/74) were male. The 28-day, 60-day, and 90-day mortality of these ECMO supported patients were 32.4, 68.9, and 74.3% respectively. Patients survived to discharge were younger (49 vs. 62 years, P = 0.042), demonstrated higher lymphocyte count (886 vs. 638 cells/uL, P = 0.022), and better CO2 removal (PaCO2 immediately after ECMO initiation 39.7 vs. 46.9 mmHg, P = 0.041). Age was an independent risk factor for in-hospital mortality of the ECMO supported patients, and a cutoff age of 51 years enabled prediction of in-hospital mortality with a sensitivity of 84.3% and specificity of 55%. The surviving ECMO supported patients had longer ICU and hospital stays (26 vs. 18 days, P = 0.018; 49 vs. 29 days, P = 0.001 respectively), and ECMO procedure was widely carried out after the supplement of medical resources after February 15 (67.6%, 50/74). Conclusions: ECMO might be a benefit for severe critically ill COVID-19 patients at the early stage of epidemic, although the in-hospital mortality was still high. To initiate ECMO therapy under tremendous pressure, patients' age, lymphocyte count, and adequacy of medical resources should be fully considered.

Teaching Pupils to Design Comparative Experiments with Web-Based Inquiry Unit During COVID-19 Pandemic: A Pilot Study

The COVID-19 pandemic caused many students away from the classroom. Its affecting region was so large and the inquiry learning had to move to online from offline. Although many studies had investigated the effectiveness of web-based inquiry learning, few of them conducted that under the pandemic. The pandemic took many new characters into education, such as the demand for the Internet. Hence, we conducted the pre-posttest quasi-experiment to investigate the effectiveness of online science inquiry during the pandemic. Under the instruction of teachers online, 30 fifth-grade students (19 males and 11 females) in a Chinese city completed a web-based inquiry learning program in the Web-based Inquiry Science Environment (WISE) platform. The experimental design ability test (EDAT) was conducted before and after web-based inquiry learning as the pre-test and post-test. The students’ attitude to web-based inquiry learning was also measured. The results showed, different from the studies before, the students’ score on experimental design ability decreased after web-based inquiry learning, especially in Asking Questions and Making Hypotheses subscales of EDAT significantly. No significant gender difference was detected. The students showed not a high attitude toward web-based inquiry learning. The possible factors causing that results and implications were discussed.

Global transmission dynamic of SIR model in the time of SARS-CoV-2.

This current work studies a new mathematical model for SARS-CoV-2. We show how immigration, protection, death rate, exposure, cure rate and interaction of infected people with healthy people affect the population. Our model is SIR model, which has three classes including susceptible, infected and recovered respectively. Here, we find the basic reproduction number and local stability through jacobean matrix. Lyapunvo function theory is used to calculate the global stability for the problem under investigation. Also a nonstandard finite difference sachem (NSFDS) is used to simulate the results.

Blocking Effect of Demethylzeylasteral on the Interaction between Human ACE2 Protein and SARS-CoV-2 RBD Protein Discovered Using SPR Technology

The novel coronavirus disease (2019-nCoV) has been affecting global health since the end of 2019, and there is no sign that the epidemic is abating. Targeting the interaction between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor is a promising therapeutic strategy. In this study, surface plasmon resonance (SPR) was used as the primary method to screen a library of 960 compounds. A compound 02B05 (demethylzeylasteral, CAS number: 107316-88-1) that had high affinities for S-RBD and ACE2 was discovered, and binding affinities (KD, μM) of 02B05-ACE2 and 02B05-S-RBD were 1.736 and 1.039 μM, respectively. The results of a competition experiment showed that 02B05 could effectively block the binding of S-RBD to ACE2 protein. Furthermore, pseudovirus infection assay revealed that 02B05 could inhibit entry of SARS-CoV-2 pseudovirus into 293T cells to a certain extent at nontoxic concentration. The compoundobtained in this study serve as references for the design of drugs which have potential in the treatment of COVID-19 and can thus accelerate the process of developing effective drugs to treat SARS-CoV-2 infections.

Comparison of Hospitalized Patients With ARDS Caused by COVID-19 and H1N1.

BACKGROUND: Since the outbreak of coronavirus disease 2019 (COVID-19) in China in December 2019, considerable attention has been focused on its elucidation. However, it is also important for clinicians and epidemiologists to differentiate COVID-19 from other respiratory infectious diseases such as influenza viruses. RESEARCH QUESTION: The aim of this study was to explore the different clinical presentations between COVID-19 and influenza A (H1N1) pneumonia in patients with ARDS. STUDY DESIGN AND METHODS: This analysis was a retrospective case-control study. Two independent cohorts of patients with ARDS infected with either COVID-19 (n = 73) or H1N1 (n = 75) were compared. Their clinical manifestations, imaging characteristics, treatments, and prognosis were analyzed and compared. RESULTS: The median age of patients with COVID-19 was higher than that of patients with H1N1, and there was a higher proportion of male subjects among the H1N1 cohort (P < .05). Patients with COVID-19 exhibited higher proportions of nonproductive coughs, fatigue, and GI symptoms than those of patients with H1N1 (P < .05). Patients with H1N1 had higher Sequential Organ Failure Assessment (SOFA) scores than patients with COVID-19 (P < .05). The Pao2/Fio2 of 198.5 mm Hg in the COVID-19 cohort was significantly higher than the Pao2/Fio2 of 107.0 mm Hg in the H1N1 cohort (P < .001). Ground-glass opacities was more common in patients with COVID-19 than in patients with H1N1 (P < .001). There was a greater variety of antiviral therapies administered to COVID-19 patients than to H1N1 patients. The in-hospital mortality of patients with COVID-19 was 28.8%, whereas that of patients with H1N1 was 34.7% (P = .483). SOFA score-adjusted mortality of H1N1 patients was significantly higher than that of COVID-19 patients, with a rate ratio of 2.009 (95% CI, 1.563-2.583; P < .001). INTERPRETATION: There were many differences in clinical presentations between patients with ARDS infected with either COVID-19 or H1N1. Compared with H1N1 patients, patients with COVID-19-induced ARDS had lower severity of illness scores at presentation and lower SOFA score-adjusted mortality.