[Clinical characteristics and short-term prognosis of 22 cases with SARS-CoV-2 infection associated acute encephalopathy].

Objective: To investigate the clinical features and short-term prognosis of patients with SARS-CoV-2 infection associated acute encephalopathy (AE). Methods: Retrospective cohort study. The clinical data, radiological features and short-term follow-up of 22 cases diagnosed with SARS-CoV-2 infection associated AE in the Department of Neurology, Beijing Children's Hospital from December 2022 to January 2023 were retrospectively analyzed. The patients were divided into cytokine storm group, excitotoxic brain damage group and unclassified encephalopathy group according to the the clinicopathological features and the imaging features. The clinical characteristics of each group were analyzed descriptively. Patients were divided into good prognosis group (≤2 scores) and poor prognosis group (>2 scores) based on the modified Rankin scale (mRS) score of the last follow-up. Fisher exact test or Mann-Whitney U test was used to compare the two groups. Results: A total of 22 cases (12 females, 10 males) were included. The age of onset was 3.3 (1.7, 8.6) years. There were 11 cases (50%) with abnormal medical history, and 4 cases with abnormal family history. All the enrolled patients had fever as the initial clinical symptom, and 21 cases (95%) developed neurological symptoms within 24 hours after fever. The onset of neurological symptoms included convulsions (17 cases) and disturbance of consciousness (5 cases). There were 22 cases of encephalopathy, 20 cases of convulsions, 14 cases of speech disorders, 8 cases of involuntary movements and 3 cases of ataxia during the course of the disease. Clinical classification included 3 cases in the cytokine storm group, all with acute necrotizing encephalopathy (ANE); 9 cases in the excitotoxicity group, 8 cases with acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) and 1 case with hemiconvulsion-hemiplegia syndrome; and 10 cases of unclassified encephalopathy. Laboratory studies revealed elevated glutathione transaminase in 9 cases, elevated glutamic alanine transaminase in 4 cases, elevated blood glucose in 3 cases, and elevated D-dimer in 3 cases. Serum ferritin was elevated in 3 of 5 cases, serum and cerebrospinal fluid (CSF) neurofilament light chain protein was elevated in 5 of 9 cases, serum cytokines were elevated in 7 of 18 cases, and CSF cytokines were elevated in 7 of 8 cases. Cranial imaging abnormalities were noted in 18 cases, including bilateral symmetric lesions in 3 ANE cases and "bright tree appearance" in 8 AESD cases. All 22 cases received symptomatic treatment and immunotherapy (intravenous immunoglobulin or glucocorticosteroids), and 1 ANE patient received tocilizumab. The follow-up time was 50 (43, 53) d, and 10 patients had a good prognosis and 12 patients had a poor prognosis. No statistically significant differences were found between the two groups in terms of epidemiology, clinical manifestations, biochemical indices, and duration of illness to initiate immunotherapy (all P>0.05). Conclusions: SARS-CoV-2 infection is also a major cause of AE. AESD and ANE are the common AE syndromes. Therefore, it is crucial to identify AE patients with fever, convulsions, and impaired consciousness, and apply aggressive therapy as early as possible.

Levels of peripheral IL-6 and CD4+ and CD8+ T cells and their prognostic significance in COVID-19.

OBJECTIVE: The aim of this study was to discuss the prognostic significance of peripheral interleukin-6 (IL-6) and CD4+ and CD8+ T cells in COVID-19. PATIENTS AND METHODS: Eighty-four COVID-19 patients were retrospectively analyzed and classified into three groups, including the moderate group (15 cases), the serious group (45 cases), and the critical group (24 cases). The levels of peripheral IL-6, CD4+, and CD8+ T cells and CD4+/CD8+ were determined for each group. It was assessed whether these indicators were correlated to the prognosis and death risks of COVID-19 patients. RESULTS: The three groups of COVID-19 patients differed significantly in the levels of peripheral IL-6 and CD4+ and CD8+ cells. The IL-6 levels in the critical, moderate, and serious groups were increased successively, but the changed levels of CD4+ and CD8+ T cells were just opposite to that of IL-6 (p<0.05). The peripheral IL-6 level increased dramatically in the death group, while the levels of CD4+ and CD8+ T cells decreased significantly (p<0.05). The peripheral IL-6 level was significantly correlated with the level of CD8+ T cells and CD4+/CD8+ ratio in the critical group (p<0.05). The logistic regression analysis indicated a dramatic increase in the peripheral IL-6 level in the death group (p=0.025). CONCLUSIONS: The aggressiveness and survival of COVID-19 were highly correlated with the increases in IL-6 and CD4+/CD8+ T cells. The fatalities of COVID-19 individuals remained at increased incidence due to elevated peripheral IL-6 levels.

COVID-19 Forecasting Based on Local Mean Decomposition and Temporal Convolutional Network

Since the outbreak of coronavirus disease 2019 (COVID-19) has resulted in a dramatic loss of human life and economic disruption worldwide from early 2020, numerous studies focusing on COVID-19 forecasting were presented to yield accurate predicting results. However, most existing methods could not provide satisfying forecasting performance due to tons of assumptions, poor capability to learn appropriate parameters, etc. Therefore, in this paper, we combine a traditional time series decomposition: local mean decomposition (LMD) with temporal convolutional network (TCN) as a general framework to overcome these shortcomings. Based on the particular architecture, it can solve weekly new confirmed cases forecasting problem perfectly. Extensive experiments show that the proposed model significantly outperforms lots of state-of-the-art forecasting methods, and achieves desirable performance in terms of root mean squared log error (RMSLE), mean absolute percentage error (MAPE), Pearson correlation (PCORR), and coefficient of determination (R-2). To be specific, it could reach 0.9739, 0.8908, and 0.7461 on R-2 when horizon is 1, 2, and 3 respectively, which proves the effectiveness and robustness of our LMD-TCN model.

Data-driven two-stage sparse distributionally robust risk optimization model for location allocation problems under uncertain environment

Robust optimization is a new modeling method to study uncertain optimization problems, which is to find a solution with good performance for all implementations of uncertain input. This paper studies the optimal location allocation of processing plants and distribution centers in uncertain supply chain networks under the worst case. Considering the uncertainty of the supply chain and the risk brought by the uncertainty, a data-driven two-stage sparse distributionally robust risk mixed integer optimization model is established. Based on the complexity of the model, a distribution-separation hybrid particle swarm optimization algorithm (DS-HPSO) is proposed to solve the model, so as to obtain the optimal location allocation scheme and the maximum expected return under the worst case. Then, taking the fresh-food supply chain under the COVID-19 as an example, the impact of uncertainty on location allocation is studied. This paper compares the data-driven two-stage sparse distributionally robust risk mixed integer optimization model with the two-stage sparse risk optimization model, and the data results show the robustness of this model. Moreover, this paper also discusses the impact of different risk weight on decision-making. Different decision makers can choose different risk weight and obtain corresponding benefits and optimal decisions. In addition, the DS-HPSO is compared with distribution-separation hybrid genetic algorithm and distributionally robust L-shaped method to verify the effectiveness of the algorithm.

Dynamic Role of Green Energy Efficiency and Climate Technologies in Realizing Environmental Sustainability: Fresh Insights From China

Amid rising industrialization and economic progress, China has shown exponential growth in energy and fossil fuel consumption;therefore, it faces great global concern and widespread criticism for energy and fuel conservation to reduce fuel-related emissions. In addition, the recent spread of COVID-19 instigates the impact of environmental pollution, exaggerates the virus intensity, and lowers people's immunity due to poor air quality. Therefore, this study explored the role of green energy efficiency and climate technologies in achieving carbon neutrality in China using an advanced quantile autoregressive distributed lag (QARDL) framework. The results indicated that green energy efficiency and climate technologies significantly reduce environmental pollution across all quantiles in the long run. In contrast, urbanization enhances environmental degradation at lower and higher emissions quantiles, while trade only promotes environmental pollution at lower quantiles. These findings suggested using alternative energy sources and carbon-reducing technologies to ensure a sustainable environment.

Longitudinal Study after Sputnik V Vaccination Shows Durable SARS-CoV-2 Neutralizing Antibodies and Reduced Viral Variant Escape to Neutralization over Time

Recent studies have shown a temporal increase in the neutralizing antibody potency and breadth to SARS-CoV-2 variants in coronavirus disease 2019 (COVID-19) convalescent individuals. Here, we examined longitudinal antibody responses and viral neutralizing capacity to the B.1 lineage virus (Wuhan related), to variants of concern (VOC;Alpha, Beta, Gamma, and Delta), and to a local variant of interest (VOI;Lambda) in volunteers receiving the Sputnik V vaccine in Argentina. Longitudinal serum samples (N = 536) collected from 118 volunteers obtained between January and October 2021 were used. The analysis indicates that while anti-spike IgG levels significantly wane over time, the neutralizing capacity for the Wuhan-related lineages of SARS-CoV-2 and VOC is maintained within 6 months of vaccination. In addition, an improved antibody cross-neutralizing ability for circulating variants of concern (Beta and Gamma) was observed over time postvaccination. The viral variants that displayed higher escape to neutralizing antibodies with respect to the original virus (Beta and Gamma variants) were the ones showing the largest increase in susceptibility to neutralization over time after vaccination. Our observations indicate that serum neutralizing antibodies are maintained for at least 6 months and show a reduction of VOC escape to neutralizing antibodies over time after vaccination. IMPORTANCE Vaccines have been produced in record time for SARS-CoV-2, offering the possibility of halting the global pandemic However, inequalities in vaccine accessibility in different regions of the world create a need to increase international cooperation. Sputnik V is a recombinant adenovirus-based vaccine that has been widely used in Argentina and other developing countries, but limited information is available about its elicited immune responses. Here, we examined longitudinal antibody levels and viral neutralizing capacity elicited by Sputnik V vaccination. Using a cohort of 118 volunteers, we found that while anti-spike antibodies wane over time, the neutralizing capacity to viral variants of concern and local variants of interest is maintained within 4 months of vaccination. In addition, we observed an increased cross-neutralization activity over time for the Beta and Gamma variants. This study provides valuable information about the immune response generated by a vaccine platform used in many parts of the world.

[Homologous modeling and binding ability analysis of Spike protein after point mutation of severe acute respiratory syndrome coronavirus 2 to receptor proteins and potential antiviral drugs]

OBJECTIVE: To explore the natural mutations in Spike protein (S protein) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the changes of affinity between virus and associated receptors or drug molecules before and after the mutation based on whole length sequencing results. METHODS: In the study, the bioinformatics analysis of all the published sequences of SARS-CoV-2 was conducted and thus the high frequency mutation sites were affirmed. Taking advantages of PolyPhen-2, the functional influence of each mutation in S protein was prospected. The 3D homologous modelling was performed by SWISS-MODEL to establish mutated S protein structural model, in which the protein-docking was then implemented with angiotensin-converting enzyme 2 (ACE2), dipeptidyl peptidase-4 (DPP4) and aminopeptidase N (APN) by ZDOCK, and the combining capacity of each mutated S protein evaluated by FiPD. Finally, the binding ability between mutated S proteins and anti-virus drugs were prospected and evaluated through AutoDock-Chimera 1.14. RESULTS: The mutations in specific region of S protein had greater tendency to destroy the S protein function by analysis of mutated S protein structure. Protein-receptor docking analysis between naturally mutated S protein and host receptors showed that, in the case of spontaneous mutation, the binding ability of S protein to ACE2 tended to be weakened, while the binding ability of DPP4 tended to be enhanced, and there was no significant change in the binding ability of APN. According to the computational simulation results of affinity binding between small molecular drugs and S protein, the affinity of aplaviroc with S protein was significantly higher than that of other small molecule drug candidates. CONCLUSION: The region from 400-1 100 amino acid in S protein of SARS-CoV-2 is the mutation sensitive part during natural state, which was more potential to mutate than other part in S protein during natural state. The mutated SARS-CoV-2 might tend to target human cells with DPP4 as a new receptor rather than keep ACE2 as its unique receptor for human infection. At the same time, aplaviroc, which was used for the treatment of human immunodeficiency virus (HIV) infection, may become a new promising treatment for SARS-CoV-2 and could be a potential choice for the development of SARS-CoV-2 drugs.