ABSTRACT
Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition leading to severe pulmonary injuries, and proteomic analysis of bronchoalveolar lavage fluid (BALF) might elucidate potential biomarkers for diagnosis and targets for treatment of ARDS. Methods: Through iTRAQ analysis, we investigated paired BALF samples from three ARDS patients in the acute and recovery phases. The proteins sharing the same expression patterns between the two ARDS phases among different patients were determined as co-upregulated and co-downregulated proteins (CUDPs), and differentially expressed proteins (DEPs), whose fold change > 1.2 and P value < 0.05, were selected from CUDPs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to determine the enriched functions and pathways of the CUDPs. Protein-protein interaction (PPI) network was generated at STRING database, and hub genes were identified by the Cytoscape software. A549 cells were treated by lipopolysaccharide (LPS) to simulate alveolar epithelial cells in ARDS. Results: We identified 374 CUDPs and 53 DEPs. The GO analysis indicated that the most significantly enriched function was neutrophil mediated immunity response, and the KEGG analysis revealed that the 374 CUDPs were most significantly enriched in Coronavirus disease COVID-19 interaction. RPSA was discovered as the most top hub gene among DEPs, and was downregulated at protein levels during ARDS recovery. Moreover, we further confirmed that both RNA and protein level of RPSA increased upon inflammatory stimulation in vitro. Conclusion: Our results proposed RPSA as a candidate for biomarker and therapeutic target of ARDS.
Subject(s)
Coronavirus Infections , Respiratory Distress Syndrome , Lung Injury , COVID-19ABSTRACT
COVID-19 is a huge catastrophe of global proportions, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and COVID-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the LightGBM, NNAT and LSTM machine learning models worked better in unstable energy series, while the ARIMA statistical model still had an advantage in stable energy time series. Overall, the machine learning models outperformed the traditional models during COVID-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during COVID-19.
Subject(s)
COVID-19ABSTRACT
Background: ： Novel coronavirus disease（COVID-19）has become a worldwide pandemic and precise fatality data by age group are needed urgently. This study to delineate the clinical characteristics and outcome of COVID-19 patients aged ≥75 years and identify the risk factors of in-hospital death. Methods: ： A total of 141 consecutive patients aged ≥75 years who were admitted to the hospital between 12 th and 19 th February 2020. In-hospital death, clinical characteristics and laboratory findings on admission were obtained from medical records. The final follow-up observation was 31 st March 2020. Results: ： The median age was 81 years (84 female, 59.6%). Thirty-eight (27%) patients were classified as severe or critical cases. 18 (12.8%) patients had died in hospital and the remaining 123 were discharged. Patients who died were more likely to present with fever (38.9% vs. 7.3%); low percutaneous oxygen saturation(SpO 2 ) (55.6% vs. 7.3%); reduced lymphocytes (72.2% vs. 35.8%) and platelets (27.8% vs. 4.1%); and increased D-dimer (94.4% vs. 42.3%), creatinine (50.0% vs. 22.0%), lactic dehydrogenase (LDH) (77.8% vs. 30.1%), high sensitivity troponin I (hs-TnI) (72.2% vs. 14.6%), and N-terminal pro-brain natriuretic peptide (NT-proBNP) (72.2% vs. 6.5%; all P<0.05) than patients who recovered. Male sex (odds ratio [OR]=13.1, 95% confidence interval[CI] 1.1 to 160.1, P=0.044), body temperature >37.3°C (OR=80.5, 95% CI 4.6 to 1407.6, P=0.003), SpO 2 ≤90% (OR=70.1, 95% CI 4.6 to 1060.4, P=0.002), and NT-proBNP>1800ng/L (OR=273.5, 95% CI 14.7 to 5104.8, P<0.0001) were independent risk factors of in-hospital death. Conclusions: ： In-hospital fatality among COVID-19 patients can be estimated by sex and on-admission measurements of body temperature, SpO 2 , and NT-proBNP.
Subject(s)
Coronavirus Infections , Fever , COVID-19ABSTRACT
Background: To investigate the CT changes of different clinical types of COVID-19 pneumonia. Methods: : This retrospective study included 50 patients with COVID-19 from 16 January 2020 to 25 February 2020. We analyzed the clinical characteristics, CT characteristics and the pneumonia involvement of the patients between the moderate group and the severe and critical group, and the dynamic changes of severity with the CT follow-up time. Results: : There were differences in the CT severity score of the right lung in the initial CT, and total CT severity score in the initial and follow-up CT between the moderate group and the severe and critical group (all p <0.05). There was a quadratic relationship between total CT severity score and CT follow-up time in the severe and critical group (r 2 =0.137, p=0.008), the total CT severity score peaked at the second follow-up CT. There was no correlation between total CT severity score and CT follow-up time in the moderate group (p >0.05). There were no differences in the occurrence rate of CT characteristics in the initial CT between the two groups (all p >0.05). There were differences in the occurrence rate of ground-glass opacity and crazy-paving pattern in the second follow-up CT, and pleural thickening or adhesion in the third follow-up CT between the two groups (all p <0.05). Conclusions: : The CT changes of COVID-19 pneumonia with different severity were different, and the extent of pneumonia involvement by CT can help to assess the severity of COVID-19 pneumonia rather than the initial CT characteristics.
Subject(s)
Coronavirus Infections , Pneumonia , COVID-19ABSTRACT
Background: Coronavirus disease 2019(COVID-19) has been spreading globally, with severe impacts on health and economies. Nursing is an essential component of medical care, and nurses’ knowledge, attitude, and practice (KAP) about the prevention and control of the disease directly affects patient’s outcomes ,but there is little know about the nurses’ KAP during the COVID-19 epidemic.Methods: An anonymous survey was conducted using customized questionnaires designed by Sojump platform. 1323 nurses in Wuhan were the survey object for the KAP survey. The survey included 48 questions regarding the basic information of nurses and knowledge, attitude, and prevention measures for COVID-19. Results: A total of 1323 nurses in Wuhan were included in the survey, of whom women (95.7%) under 40 years old (94.6%) accounted for the majority. Nurses have a comprehensive knowledge of problems such as the COVID-19 susceptible population, isolation ward layout process, environmental cleaning and disinfection, hand hygiene measures, infectious disease case reporting processes, standard prevention measures, and personal protection level, with more than 60% accuracy. However, they have a general lack of knowledge about the route of transmission of COVID-19, the use of protective equipment, patient management, medical waste disposal, and occupational exposure emergency response, with less than 45% accuracy. There was a gap between the attitude and the practice of nurses (p <0.05). Moreover, the knowledge level of nurses has an impact on their attitude (OR 1.52, 95% CI 1.10-2.08, p = 0.008), and title (OR 1.48, 95% CI 1.01-2.15, p = 0.042), place of work (OR 1.59, 95% CI 1.36-1.85, p <0.001), and attitude (OR 1.93, 95% CI 1.52-2.46, p <0.001) had an impact on their practice.Conclusion: The nurses in Wuhan during the epidemic have insufficient knowledge about COVID-19 infection prevention and control, and there are gaps in attitude and practice, which further affect their attitude and practice. It is therefore necessary to strengthen relevant knowledge training on COVID-19 among nurses.
Subject(s)
COVID-19ABSTRACT
Background Tianmen had the highest case-fatality rate (CFR) among all the cities in China early in the transmission of SARS-CoV-2, but little is known about the details of the epidemic in Tianmen. Our study aims to reveal the causes of the high CFR from the aspects of clinical features, medical resources, and epidemic situation.Methods In this study, we report the admission and outcomes of 341 patients with confirmed COVID-19 who were admitted and hospitalized in the hospital (The First People's Hospital of Tianmen) from January 14 to February 25, 2020. Epidemic information and medical resources across different regions were collected and compared. Results All deaths from COVID-19 in Tianmen occurred in the hospital, and the proportion of patients with critical (8.5%) symptoms in the hospital is higher than the average in China. In addition, the number of affected patients in Tianmen is quite low, but the hospital and the whole city had the highest CFR in the early stage of the epidemic. Completely different from the stable CFR in Hubei Province, the CFR of COVID-19 in the hospital and city dropped dramatically, from the largest value in China to a more average level, within a few days. Analysis of government public documents showed that the per capita medical resources in Tianmen are worse than those of Wuhan and Hubei. Conclusions Our findings suggest that the inadequate capacity to respond to public health emergencies caused by relatively scarce health-care resources is a crucial factor contributing to the higher CFR of infectious diseases in regions with lower economic levels. Such countries and territories should implement strategies earlier to minimize the danger of COVID-19.
Subject(s)
COVID-19 , Emergencies , Communicable DiseasesABSTRACT
ObjectivesTo investigate the CT changes of different clinical types of COVID-19 pneumonia.MethodsThis retrospective study included 50 confirmed patients with COVID-19 from 16 January 2020 to 25 February 2020. We analyzed the clinical and CT characteristics of the patients between the moderate group and the severe and critical group, and the dynamic changes of severity with the CT follow-up time.ResultsThere were no differences in the occurrence rate of CT characteristics between the moderate group (n=34) and the severe and critical group (n=16) in the initial CT (all p >0.05). There were differences in the CT score of right lung and total CT score at the initial CT between the two groups (all p <0.05). There was a quadratic relationship between total CT score and CT follow-up time in the severe and critical group (r2=0.137, p=0.008), the total CT severity score peaked at the second follow-up CT. There was no correlation between total CT score and CT follow-up time in the moderate group (p >0.05). The total CT score of the severe and critical group was different between the initial and first follow-up, the second and third follow-ups, the third and fourth follow-ups, and the fourth and fifth follow-ups CT (all p<0.05). The total CT score of the moderate group was different between the second and third follow-ups CT (p<0.05).ConclusionsCOVID-19 pneumonia with the severe and critical types progressed rapidly with the greatest severity at the second follow-up CT, and the moderate type was relatively stable.