Coronavirus Disease 2019 (COVID-19) has recently become a public emergency and a worldwide pandemic. However, the information on the risk factors associated with the mortality of COVID-19 and of their prognostic potential is limited. In this retrospective study, the clinical characteristics, treatment and outcome data were collected and analyzed from 676 COVID-19 patients stratified into 140 non-survivors and 536 survivors. We found that the levels of Dimerized plasmin fragment D (D-dimer), C-reactive protein (CRP), lactate dehydrogenase (LDH), procalcitonin (PCT) were significantly higher in non-survivals on admission (non-survivors vs. survivors: D-Dimer ≥ 0.5 mg/L, 83.2% vs. 44.9%, P<0.01; CRP ≥10 mg/L, 50.4% vs. 6.0%, P<0.01; LDH ≥ 250 U/L, 73.8% vs. 20.1%, P<0.01; PCT ≥ 0.5 ng/ml, 27.7% vs. 1.8%, P<0.01). Moreover, dynamic tracking showed D-dimer kept increasing in non-survivors, while CRP, LDH and PCT remained relatively stable after admission. D-dimer has the highest C-index to predict in-hospital mortality, and patients with D-dimer levels ≥0.5 mg/L had a higher incidence of mortality (Hazard Ratio: 4.39, P<0.01). Our study suggested D-dimer could be a potent marker to predict the mortality of COVID-19, which may be helpful for the management of patients.

This study aimed to analyze aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio in COVID-19 patients. After exclusion, 567 inpatients were included in this study and separated into two groups according to their AST/ALT ratio on admission. Death was regarded as poor prognosis in this study. Of 567 patients, 200 (35.3%) had AST/ALT ≥ 1.38. Of the 200 patients, older age (median age 60 years), myalgia (64 [32%] cases), fatigue (91 [45.5%] cases), some comorbidities and outcomes were significantly different from patients with AST/ALT < 1.38. They also had worse chest computed tomography (CT) findings, laboratory results and severity scores. Levels of platelet count (OR 0.995, 95% CI [0.992–0.998]) and hemoglobin (OR 0.984, 95% CI [0.972–0.995]) were independently associated with AST/ALT ≥ 1.38 on admission. Furthermore, a high AST/ALT ratio on admission was an independent risk factor for poor prognosis (OR 99.9, 95% CI [2.1–4280.5]). In subsequent monitoring, both survivors and non-survivors showed decreased AST/ALT ratio during hospitalization. In conclusion, high AST/ALT ratio might be the indication of worse status and outcomes in COVID-19 patients.

Objective This study aimed to investigate the abilities of long non‐coding RNA PVT1 (lnc‐PVT1) and microRNA‐146a (miR‐146a) in predicting chronic obstructive pulmonary disease (COPD) susceptibility and acute exacerbation risk, moreover, to explore the association of lnc‐PVT1 with disease severity, inflammation, and miR‐146a in patients with COPD. Methods A total of 80 acute exacerbation of COPD (AECOPD) patients, 80 stable COPD patients, and 80 healthy controls (HCs) were consecutively recruited. Peripheral blood samples of all participants were collected to isolate peripheral blood mononuclear cells (PBMCs), and serum: PBMCs were used to detect lnc‐PVT1 and miR‐146a by RT‐qPCR; serum was used to detect inflammatory cytokines by ELISA. Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage of COPD was assessed. Results Lnc‐PVT1 expression was highest in AECOPD patients, followed by stable COPD patients and HCs. Receiver operating characteristic curves disclosed that lnc‐PVT1 distinguished AECOPD patients and stable COPD patients from HCs, also distinguished AECOPD patients from stable COPD patients. In AECOPD patients and stable COPD patients, lnc‐PVT1 expression positively correlated with GOLD stage and levels of TNF‐α, IL‐6, IL‐8, and IL‐17. Moreover, lnc‐PVT1 was negatively correlated with miR‐146a. For miR‐146a, its expression was lowest in AECOPD patients, followed by stable COPD patients and HCs, and it predicted reduced COPD susceptibility and decreased acute exacerbation risk; meanwhile, it negatively correlated with GOLD stage and inflammatory cytokine levels in AECOPD patients and stable COPD patients. Conclusion Lnc‐PVT1 assists the disease management and acute exacerbation risk monitoring of COPD via interaction with miR‐146a.

This paper proposes the concept “active energy agent (AEA)” to characterize the autonomous and interactive entities of power system. The future distribution network is a peer-to-peer (P2P) community based on numbers of AEAs. A two-stage “P2P Plus” mechanism is developed to address the electricity transaction within AEA community. In the first “P2P” stage, electricity is directly traded among AEAs via P2P price bidding. The model of P2P transaction is established, and the method of multi-dimensional willingness is adopted in price bidding. In the second “Plus” stage, the centralized coordination by distribution company (DisCo) is formulated as a constrained optimization problem, in which the objective is to maximize profit and the constraints are the basic rights of AEAs and line ratings of distribution network. A 30-bus test system including 29 AEAs and main grid is investigated. Numeric simulation results verify the effectiveness of the proposed models and methods regarding flow constraint. Comparative study reveals the economic motivations of AEAs to participate in P2P transaction, the efficiency of combined search, and the benefit of DisCo from pricing control.

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Background: Owing to the multifactorial nature of the pathogenesis of diabetic peripheral neuropathy (DPN), conventional drug therapies have not been effective. The application of stem cells transplantation may be useful for the treatment of DPN. This study was designed to assess the safety and therapeutic effects of autologous bone marrow mononuclear cells (BMMNCs) transplantation on the treatment of refractory DPN. Methods: One hundred and sixty-eight patients with refractory DPN were recruited and enrolled in the study. They received intramuscular injection of BMMNCs and followed at 1, 3, 6, 12, 18, 24, and 36 months after the transplantation. Clinical data, Toronto Clinical Scoring System (TCSS), and nerve conduction studies (NCSs) were compared before and after the transplantation. Results: The signs and symptoms of neuropathy were significantly improved after BMMNCs transplantation. The values of the TCSS scores at 1 month (9.68 ± 2.49 vs. 12.55 ± 2.19, P < 0.001) and 3 months (8.47 ± 2.39 vs. 12.55 ± 2.19, P < 0.001) after the treatment reduced significantly compared with the baseline value. This decrement remained persistent until the end of the study. The conduction velocity and action potential and sensory nerves were significantly improved after transplantation (3 and 12 months after the treatment vs. the baseline: motor nerve conduction velocity, 40.24 ± 2.80 and 41.00 ± 2.22 m/s vs. 38.21 ± 2.28 m/s, P < 0.001; sensory nerve conduction velocity, 36.96 ± 2.26 and 39.15 ± 2.61 m/s vs. 40.41 ± 2.22 m/s, P < 0.001; compound muscle action potential, 4.67 ± 1.05 and 5.50 ± 1.20 μV vs. 5.68 ± 1.08 μV, P < 0.001; sensory nerve action potential, 4.29 ± 0.99 and 5.14 ± 1.26 μV vs. 5.41 ± 1.14 μV, P < 0.001). No adverse event associated with the treatment was observed during the follow-up period. Conclusions: Autologous transplantation of BMMNCs may be an effective and promising therapeutic strategy for the treatment of refractory DPN.

This paper addresses decentralized energy trading among virtual power plants (VPPs) and proposes a peer-to-peer (P2P) mechanism, including two interactive layers: on the bottom layer, each VPP schedules/reschedules its internal distributed energy resources (DERs); and on the top layer, VPPs negotiate with each other on the trade price and quantity. The bottom-layer scheduling provides initial conditions for the top-layer negotiation, and the feedback of top-layer negotiation affects the bottom-layer rescheduling. The local scheduling/rescheduling of a VPP is formulated as a stochastic optimization problem, which takes into account the uncertainties of wind and photovoltaic power by using the scenarios-based method. In order to describe the capability of a seller VPP to generate more energy than the scheduled result, the concept of power generation potential is introduced and then considered during order initialization. The multidimensional willingness bidding strategy (MWBS) is modified and applied to the price bidding process of P2P negotiation. A 14-VPP case is studied by performing numerous computational experiments. The optimal scheduling model is effective and flexible to deal with VPPs with various configurations of DERs. The parallel price bidding with MWBS is adaptive to market situations and efficient due to its rapid convergence. It is revealed that VPPs can obtain higher profit by participating in P2P energy trading than from traditional centralized trading, and the proposed mechanism of two-layer “interactivity” can further increase VPPs’ benefits compared to its “forward” counterpart. The impacts of VPP configuration and VPP number are also studied. It is demonstrated that the proposed mechanism is applicable to most cases where VPPs manage some controllable DERs.

This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.