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1.
J Ethnopharmacol ; 277: 113888, 2021 Sep 15.
Article in English | MEDLINE | ID: covidwho-1056890

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: The coronavirus disease 2019 (COVID-19) has formed a global pandemic since late 2019. Benefitting from the application experience of Chinese Medicine (CM) for influenza and SARS, CM has been used to save patients at the early stage of COVID-19 outbreak in China. AIM OF THE STUDY: In order to evaluate the efficacy and safety of CM, and compare with Western Medicine (WM) for COVID-19, we conducted a retrospective case series study based on the patients in Wuhan Jinyintan Hospital, Wuhan, China. METHODS: The inclusion and exclusion criteria of data extraction were set for this retrospective study. All patients who were admitted by the Wuhan Jinyintan Hospital between January 17th and February 25th 2020 were considered. In addition, patients enrolled met the severe defined by the guidelines released by the National Health Commission of the People's Republic of China. In these cases included in the study, CM or WM treatment was selected according to the wishes of the patients at the beginning of hospitalization. The patients in CM group were treated with Huashi Baidu granule (137 g po, bid) combined with the injections of Xiyanping (100 mg iv, bid), Xuebijing (100 ml iv, bid) and Shenmai (60 ml iv, qd) according to the syndrome of epidemic toxin blocking the lung in the theory of Traditional Chinese Medicine. The WM group received antiviral therapy (including abidor capsule 0.2 g po, tid; Lopinavir-Ritonavir tablets, 500 mg po, bid), antibiotics (such as cefoperazone 2 g iv, bid; moxifloxacin hydrochloride tablets, 0.4 g po, qd) or corticosteroid therapy (such as methylprednisolone succinate sodium 40 mg iv, qd; prednisone, 30 mg po, qd). In addition, patients in both groups received routine supportive treatment, including oxygen inhalation, symptomatic therapy, and/or human intravenous immunoglobulin, and/or serum albumin, and treatment for underlying diseases. The clinical outcomes were evaluated based on changes related with clinical manifestations, computer tomography (CT) scan images, and laboratory examinations before and after the treatment. RESULTS: 55 severe COVID-19 patients, with 23 in CM group and 32 in WM group, were included for analyzed. There was no case of death, being transferred to ICU, or receiving invasive mechanical ventilation in two groups during hospitalization. The median time of SARS-CoV-2 RNA clearance in CM and WM group were 12 days and 15.5 days respectively, the ratio of nucleic acid negative conversion of CM group at different follow-up time points was significantly higher than that of WM group (HR: 2.281, P = 0.018). Further, the chest CT imaging showed more widely lung lesion opacity absorbed in the CM group. The high sensitivity C-reactive protein and serum ferritin decreased significantly in the CM group (P<0.05). There was no significant difference in adverse events in terms of liver function and renal function between the two groups. CONCLUSION: Based on this retrospective analysis from Wuhan Jinyintan Hospital, CM has better effects in SARS-CoV-2 RNA clearance, promoting lung lesion opacity absorbed and reducing inflammation in severe COVID-19 patients, which is effective and safe therapy for treating severe COVID-19 and reducing mortality.


Subject(s)
COVID-19/drug therapy , Medicine, Chinese Traditional/adverse effects , Medicine, Chinese Traditional/methods , Adrenal Cortex Hormones/therapeutic use , Adult , Aged , Anti-Bacterial Agents/therapeutic use , COVID-19/blood , COVID-19/diagnostic imaging , COVID-19/mortality , China , Female , Hospitalization , Humans , Inflammation/drug therapy , Kaplan-Meier Estimate , Lung/diagnostic imaging , Lung/pathology , Lymphopenia/drug therapy , Male , Middle Aged , RNA, Viral/analysis , RNA, Viral/drug effects , Retrospective Studies , SARS-CoV-2/drug effects , Tomography, X-Ray Computed , Treatment Outcome
2.
Energy ; : 119952, 2021.
Article in English | ScienceDirect | ID: covidwho-1046466

ABSTRACT

The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is proposed: it combines buffer operators and the DGGM(1,1) model. Based on the quarterly data of the industrial enterprises in Zhejiang Province of China from the first quarter of 2013 to the first quarter of 2020, the GM(1,1), DGGM(1,1), SVM, and AWBO-DGGM(1,1) models are employed, respectively, to simulate and forecast seasonal variations in electricity consumption, the added value, and electricity usage efficiency. The results indicate that the AWBO-DGGM(1,1) models can identify seasonal fluctuations and variations in time series data, and predict the impact of COVID-19 on industrial systems. The minimum mean absolute percentage errors (MAPEs) of the electricity consumption, added value, and electricity usage efficiency of industrial enterprises separately are 0.12%, 0.10%, and 3.01% in the training stage, while those in the test stage are 6.79%, 4.09%, and 2.25%, respectively. The electricity consumption, added value, and electricity usage efficiency of industrial enterprises in Zhejiang Province will still present a tendency to grow with seasonal fluctuations from 2020 to 2022. Of them, the added value is predicted to increase the fastest, followed by electricity consumption.

3.
Int J Environ Res Public Health ; 17(12)2020 06 25.
Article in English | MEDLINE | ID: covidwho-614073

ABSTRACT

The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364-61,327 during 17-22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817-79,780.


Subject(s)
Coronavirus Infections/epidemiology , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Betacoronavirus/isolation & purification , COVID-19 , China/epidemiology , Coronavirus Infections/virology , Humans , Models, Statistical , Pneumonia, Viral/virology , SARS-CoV-2
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