Estimating China's CO2 emissions under the influence of COVID-19 epidemic using a novel fractional multivariate nonlinear grey model

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China's economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China's economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China's CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China's CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China's CO2 emissions. Results show that China's CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition.

Molecular Insights of SARS-CoV-2 Infection and Molecular Treatments.

The coronavirus disease emerged in December 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome-related coronavirus 2 (SARS-CoV-2). Its rapid global spread has brought an international health emergency and urgent responses for seeking efficient prevention and therapeutic treatment. This has led to imperative needs for illustration of the molecular pathogenesis of SARS-CoV-2, identification of molecular targets or receptors, and development of antiviral drugs, antibodies, and vaccines. In this study, we investigated the current research progress in combating SARS-CoV-2 infection. Based on the published research findings, we first elucidated, at the molecular level, SARS-CoV-2 viral structures, potential viral host-cell-invasion, pathogenic mechanisms, main virus-induced immune responses, and emerging SARS-CoV-2 variants. We then focused on the main virus- and host-based potential targets and summarized and categorized effective inhibitory molecules based on drug development strategies for COVID-19 that can guide efforts for the identification of new drugs and treatment for this problematic disease. Current research and development of antibodies and vaccines were also introduced and discussed. We concluded that the main virus entry route- SARS-CoV-2 spike protein interaction with ACE2 receptors played a key role in guiding the development of therapeutic treatments against COVID-19. Four main strategies may be considered in developing molecular therapeutics, and drug repurposing is likely to be an easy, fast and low-cost approach in such a short period of time with urgent need of antiviral drugs. Additionally, the quick development of antibody and vaccine candidates has yielded promising results, but the wide-scale deployment of safe and effective COVID-19 vaccines remains paramount in solving the pandemic crisis. As new variants of the virus emerge, the efficacy of these vaccines and treatments must be closely evaluated. Finally, we discussed the possible challenges of developing molecular therapeutics for COVID-19 and suggested some potential future efforts. Despite the limited availability of literature, our attempt in this work to provide a relatively comprehensive overview of current SARS-CoV-2 studies can be helpful for quickly acquiring the key information of COVID-19 and further promoting this important research to control and diminish the pandemic.

Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients.

After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein-protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

A Nomogram Prediction of Length of Hospital Stay in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study.

Assessing the length of hospital stay (LOS) in patients with coronavirus disease 2019 (COVID-19) pneumonia is helpful in optimizing the use efficiency of hospital beds and medical resources and relieving medical resource shortages. This retrospective cohort study of 97 patients was conducted at Beijing You'An Hospital between January 21, 2020, and March 21, 2020. A multivariate Cox proportional hazards regression based on the smallest Akaike information criterion value was used to select demographic and clinical variables to construct a nomogram. Discrimination, area under the receiver operating characteristic curve (AUC), calibration, and Kaplan-Meier curves with the log-rank test were used to assess the nomogram model. The median LOS was 13 days (interquartile range [IQR]: 10-18). Age, alanine aminotransferase, pneumonia, platelet count, and PF ratio (PaO2/FiO2) were included in the final model. The C-index of the nomogram was 0.76 (95%confidence interval [CI] = 0.69-0.83), and the AUC was 0.88 (95%CI = 0.82-0.95). The adjusted C-index was 0.75 (95%CI = 0.67-0.82) and adjusted AUC 0.86 (95%CI = 0.73-0.95), both after 1000 bootstrap cross internal validations. A Brier score of 0.11 (95%CI = 0.07-0.15) and adjusted Brier score of 0.130 (95%CI = 0.07-0.20) for the calibration curve showed good agreement. The AUC values for the nomogram at LOS of 10, 20, and 30 days were 0.79 (95%CI = 0.69-0.89), 0.89 (95%CI = 0.83-0.96), and 0.96 (95%CI = 0.92-1.00), respectively, and the high fit score of the nomogram model indicated a high probability of hospital stay. These results confirmed that the nomogram model accurately predicted the LOS of patients with COVID-19. We developed and validated a nomogram that incorporated five independent predictors of LOS. If validated in a future large cohort study, the model may help to optimize discharge strategies and, thus, shorten LOS in patients with COVID-19.

Humoral immune response to circulating SARS-CoV-2 variants elicited by inactivated and RBD-subunit vaccines.

SARS-CoV-2 variants could induce immune escape by mutations on the receptor-binding domain (RBD) and N-terminal domain (NTD). Here we report the humoral immune response to circulating SARS-CoV-2 variants, such as 501Y.V2 (B.1.351), of the plasma and neutralizing antibodies (NAbs) elicited by CoronaVac (inactivated vaccine), ZF2001 (RBD-subunit vaccine) and natural infection. Among 86 potent NAbs identified by high-throughput single-cell VDJ sequencing of peripheral blood mononuclear cells from vaccinees and convalescents, near half anti-RBD NAbs showed major neutralization reductions against the K417N/E484K/N501Y mutation combination, with E484K being the dominant cause. VH3-53/VH3-66 recurrent antibodies respond differently to RBD variants, and K417N compromises the majority of neutralizing activity through reduced polar contacts with complementarity determining regions. In contrast, the 242-244 deletion (242-244Δ) would abolish most neutralization activity of anti-NTD NAbs by interrupting the conformation of NTD antigenic supersite, indicating a much less diversity of anti-NTD NAbs than anti-RBD NAbs. Plasma of convalescents and CoronaVac vaccinees displayed comparable neutralization reductions against pseudo- and authentic 501Y.V2 variants, mainly caused by E484K/N501Y and 242-244Δ, with the effects being additive. Importantly, RBD-subunit vaccinees exhibit markedly higher tolerance to 501Y.V2 than convalescents, since the elicited anti-RBD NAbs display a high diversity and are unaffected by NTD mutations. Moreover, an extended gap between the third and second doses of ZF2001 leads to better neutralizing activity and tolerance to 501Y.V2 than the standard three-dose administration. Together, these results suggest that the deployment of RBD-vaccines, through a third-dose boost, may be ideal for combating SARS-CoV-2 variants when necessary, especially for those carrying mutations that disrupt the NTD supersite.

A Novel Scoring System for Prediction of Disease Severity in COVID-19.

Background: A novel enveloped RNA beta coronavirus, Corona Virus Disease 2019 (COVID-19) caused severe and even fetal pneumonia in China and other countries from December 2019. Early detection of severe patients with COVID-19 is of great significance to shorten the disease course and reduce mortality. Methods: We assembled a retrospective cohort of 80 patients (including 56 mild and 24 severe) with COVID-19 infection treated at Beijing You'an Hospital. We used univariable and multivariable logistic regression analyses to select the risk factors of severe and even fetal pneumonia and build scoring system for prediction, which was validated later on in a group of 22 COVID-19 patients. Results: Age, white blood cell count, neutrophil, glomerular filtration rate, and myoglobin were selected by multivariate analysis as candidates of scoring system for prediction of disease severity in COVID-19. The scoring system was applied to calculate the predictive value and found that the percentage of ICU admission (20%, 6/30) and ventilation (16.7%, 5/30) in patients with high risk was much higher than those (2%, 1/50; 2%, 1/50) in patients with low risk (p = 0.009; p = 0.026). The AUC of scoring system was 0.906, sensitivity of prediction is 70.8%, and the specificity is 89.3%. According to scoring system, the probability of patients in high risk group developing severe disease was 20.24 times than that in low risk group. Conclusions: The possibility of severity in COVID-19 infection predicted by scoring system could help patients to receiving different therapy strategies at a very early stage. Topic: COVID-19, severe and fetal pneumonia, logistic regression, scoring system, prediction.

Interferon-Induced Transmembrane Protein 3 Genetic Variant rs12252-C Associated With Disease Severity in Coronavirus Disease 2019.

A major unanswered question in the current global coronavirus disease 2019 (COVID-19) outbreak is why severe disease develops in a small minority of infected individuals. In the current article, we report that homozygosity for the C allele of rs12252 in the interferon-induced transmembrane protein 3 (IFITM3) gene is associated with more severe disease in an age-dependent manner. This supports a role for IFITM3 in disease pathogenesis and the opportunity for early targeted intervention in at-risk individuals.

Longitudinal COVID-19 profiling associates IL-1RA and IL-10 with disease severity and RANTES with mild disease.

BACKGROUND: Identifying immune correlates of COVID-19 disease severity is an urgent need for clinical management, vaccine evaluation, and drug development. Here, we present a temporal analysis of key immune mediators, cytokines, and chemokines in blood of hospitalized COVID-19 patients from serial sampling and follow-up over 4 weeks. METHODS: A total of 71 patients with laboratory-confirmed COVID-19 admitted to Beijing You'an Hospital in China with either mild (53 patients) or severe (18 patients) disease were enrolled with 18 healthy volunteers. We measured 34 immune mediators, cytokines, and chemokines in peripheral blood every 4-7 days over 1 month per patient using a bioplex multiplex immunoassay. RESULTS: We found that the chemokine RANTES (CCL5) was significantly elevated, from an early stage of the infection, in patients with mild but not severe disease. We also found that early production of inhibitory mediators including IL-10 and IL-1RA were significantly associated with disease severity, and a combination of CCL5, IL-1 receptor antagonist (IL-1RA), and IL-10 at week 1 may predict patient outcomes. The majority of cytokines that are known to be associated with the cytokine storm in virus infections such as IL-6 and IFN-γ were only significantly elevated in the late stage of severe COVID-19 illness. TNF-α and GM-CSF showed no significant differences between severe and mild cases. CONCLUSION: Together, our data suggest that early intervention to increase expression of CCL5 may prevent patients from developing severe illness. Our data also suggest that measurement of levels of CCL5, as well as IL-1RA and IL-10 in blood individually and in combination, might be useful prognostic biomarkers to guide treatment strategies.