Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach.

People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts' opinions. Later, the confidence level is measured using a novel Weighted Kendall's W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis.

Evaluating the Effectiveness of the COVID-19 Emergency Outbreak Prevention and Control Based on CIA-ISM.

The COVID-19 pandemic, characterized by high uncertainty and difficulty in prevention and control, has caused significant disasters in human society. In this situation, emergency management of pandemic prevention and control is essential to reduce the pandemic's devastation and rapidly restore economic and social stability. Few studies have focused on a scenario analysis of the entire emergency response process. To fill this research gap, this paper applies a cross impact analysis (CIA) and interpretive structural modeling (ISM) approach to analyze emergency scenarios and evaluate the effectiveness of emergency management during the COVID-19 crisis for outbreak prevention and control. First, the model extracts the critical events for COVID-19 epidemic prevention and control, including source, process, and resultant events. Subsequently, we generated different emergency management scenarios according to different impact levels and conducted scenario deduction and analysis. A CIA-ISM based scenario modeling approach is applied to COVID-19 emergency management in Nanjing city, China, and the results of the scenario projection are compared with actual situations to prove the validity of the approach. The results show that CIA-ISM based scenario modeling can realize critical event identification, scenario generation, and evolutionary scenario deduction in epidemic prevention and control. This method effectively handles the complexity and uncertainty of epidemic prevention and control and provides insights that can be utilized by emergency managers to achieve effective epidemic prevention and control.

Contemporary Approaches to the Discovery and Development of Broad-Spectrum Natural Product Prototypes for the Control of Coronaviruses.

The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.

The Potential Role of Probiotics in Protection against Influenza a Virus Infection in Mice.

Influenza A virus induces severe respiratory tract infection and results in a serious global health problem. Influenza infection disturbs the cross-talk connection between lung and gut. Probiotic treatment can inhibit influenza virus infection; however, the mechanism remains to be explored. The mice received Lactobacillus mucosae 1025, Bifidobacterium breve CCFM1026, and their mixture MIX for 19 days. Effects of probiotics on clinical symptoms, immune responses, and gut microbial alteration were evaluated. L. mucosae 1025 and MIX significantly reduced the loss of body weight, pathological symptoms, and viral loading. B. breve CCFM1026 significantly reduced the proportion of neutrophils and increased lymphocytes, the expressions of TLR7, MyD88, TRAF6, and TNF-α to restore the immune disorders. MIX increased the antiviral protein MxA expression, the relative abundances of Lactobacillus, Mucispirillum, Adlercreutzia, Bifidobacterium, and further regulated SCFA metabolism resulting in an enhancement of butyrate. The correlation analysis revealed that the butyrate was positively related to MxA expression (p < 0.001) but was negatively related to viral loading (p < 0.05). The results implied the possible antiviral mechanisms that MIX decreased viral loading and increased the antiviral protein MxA expression, which was closely associated with the increased butyrate production resulting from gut microbial alteration.

Risk factors and electrocardiogram characteristics for mortality in critical inpatients with COVID-19.

BACKGROUND: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. HYPOTHESIS: The possible risk factors that lead to death in critical inpatients with coronavirus disease 2019 (COVID-19) are not yet fully understood. METHODS: In this single-center, retrospective study, we enrolled 113 critical patients with COVID-19 from Renmin Hospital of Wuhan University between February 1, 2020 and March 15, 2020. Patients who survived or died were compared. RESULTS: A total of 113 critical patients with COVID-19 were recruited; 50 (44.3%) died, and 63 (55.7%) recovered. The proportion of patients with ventricular arrhythmia was higher in the death group than in the recovery group (P = .021) and was higher among patients with myocardial damage than patients without myocardial damage (P = .013). Multivariate analysis confirmed independent predictors of mortality from COVID-19: age > 70 years (HR 1.84, 95% CI 1.03-3.28), initial neutrophil count over 6.5 × 109 /L (HR 3.43, 95% CI 1.84-6.40), C-reactive protein greater than 100 mg/L (HR 1.93, 95% CI 1.04-3.59), and lactate dehydrogenase over 300 U/L (HR 2.90, 95% CI 1.26-6.67). Immunoglobulin treatment (HR 0.39, 95% CI 0.21-0.73) can reduce the risk of death. Sinus tachycardia (HR 2.94, 95% CI 1.16-7.46) and ventricular arrhythmia (HR 2.79, 95% CI 1.11-7.04) were independent ECG risk factors for mortality from COVID-19. CONCLUSIONS: Old age (>70 years), neutrophilia, C-reactive protein greater than 100 mg/L and lactate dehydrogenase over 300 U/L are high-risk factors for mortality in critical patients with COVID-19. Sinus tachycardia and ventricular arrhythmia are independent ECG risk factors for mortality from COVID-19.

Retrospective Study of Risk Factors for Myocardial Damage in Patients With Critical Coronavirus Disease 2019 in Wuhan.

BACKGROUND The novel severe acute respiratory syndrome coronavirus 2 threatens human health, and the mortality rate is higher in patients who develop myocardial damage. However, the possible risk factors for myocardial damage in patients with coronavirus disease 2019 (COVID-19) are not fully known. METHODS AND RESULTS Critical type patients were selected randomly from 204 confirmed COVID-19 cases occurring in Renmin Hospital of Wuhan University from February 1, 2020 to February 24, 2020. Univariate analyses were used to compare the 2 groups: the myocardial damage group and the non-myocardial damage group. A total of 82 critical patients with COVID-19 were recruited: 34 with myocardial damage and 48 without myocardial damage. A total of 30 patients died in the myocardial damage group, and 20 died in the non-myocardial damage group. In univariate analysis, the proportion of elderly patients (>70 years old, 70.59% versus 37.50%; P=0.003) and patients with cardiovascular disease (41.18% versus 12.50%; P=0.003) was higher among myocardial damage patients than among non-myocardial damage patients. Multivariate analysis showed that age >70 years old (hazard ratio [HR], 2.44; 95% CI, 1.01-5.40), CRP (C-reactive protein) >100 mg/L (HR, 1.92; 95% CI, 0.94-3.92), lactate dehydrogenase >300 U/L (HR, 2.67; 95% CI, 1.03-6.90), and lactic acid >3 mmol/L (HR, 3.25; 95% CI, 1.57-6.75) were independent risk factors for myocardial damage in patients with COVID-19. CONCLUSIONS Old age (>70 years old), CRP >100 mg/L, lactate dehydrogenase >300 U/L, and lactic acid >3 mmol/L are high-risk factors related to myocardial damage in critical patients with COVID-19.