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BackgroundThe coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with [~]80% asymptomatic or mild cases and [~]5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. MethodsWe implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. ResultsWe discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8+ T (TRM) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in TRM cells trajectory from moderate to severe patients. ConclusionsCXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and TRM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.
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Objective To explore the risk factors of nosocomial infections to provide a reference for prevention and control of nosocomial infection.Methods The data of 680 patients with nosocomial infection in this hospital from January 2015 to December 2016 were retrospectively collected.The characteristics of nosocomial infection were analyzed from the aspects of age,hospitalization department,infection site,etc.Results A total of 680 cases of nosocomial infection accounted for 1.67％(680/40 657) of all hospitalization patients,including 316 cases(nosocomial infection rate was 2.47％) of 61-80 years old.The infection rate was the highest in ICU(24.90％),followed by the nephrology department(4.99％).For infection sites,the maximal cases-times in infection site was lower respiratory tract(413 case-times),followed by urinary tract and superficial incision(each 68 case-times).The pathogenic detection rate was 45.03％,and 190 strains of pathogens were detected.The top five places were Escherichia coli(47 strains),Klebsiella pneumoniae(28 strains),Pseudomonas aeruginosa (23 strains),Staphylococcus aureus (22 strains) and Bauman Acinetobacter (16 strains).Logistic regression analysis showed that age larger than 60 years old,hospitalization time longer than 60 d,indwelling urinary catheter,peripherally inserted central catheter(PICC),tracheotomy and enteral nutrition were the risk factors for nosocomial infection occurrence.Conclusion Hospital should put the emphasis on preventing nosocomial infection of lower respiratory tract and strengthen the nosocomial infection management of key departments like ICU.
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In this study, the rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry ( RRLC-QTOF/MS ) was used to profile the metabolites of urine samples from Childhood Pneumonia ( CP) patients and healthy controls and find the potential biomarkers which can support evidence to early diagnose and cure the disease. Choose 10 CP patients ( age 47. 72 ± 2. 35 months) and 10 healthy controls ( age 46 . 65 ± 1 . 97 months ) . The urine samples were analyzed by RRLC-QTOF/MS and then the resulting data matrices were analyzed by principal components analysis ( PCA ) to find the potential biomarkers. Urine samples of CP patients were successfully distinguished from those of healthy controls. A total of two significantly changed metabolites have been found and identified as potential biomarkers. It is suggested that the disorder of purine metabolism and amino acid metabolism may play an important role in the mechanism of CP.
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A rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometric ( RRLC-QTOF/MS) method was used to profile the metabolites of urine samples from atherosclerosis ( AS) patients and healthy controls and find the differential metabolites which could provide the scientific evidence to explain the pathogenesis and early disease diagnose. In the study, 15 AS patients ( age46. 84±2. 41 years) and 15 healthy controls ( age45 . 72±1 . 93 years ) was screened out by VaSera VS-1000 . The urine samples were analyzed by RRLC-QTOF/MS and the resulting data matrices were analyzed by multivariate statistical analysis ( Principal Component Analysis, PCA ) to find the potential biomarkers. The results showed that the urine samples of AS patients were successfully distinguished from those of healthy controls. Besides, a total of two significantly changed metabolites, uric acid and Guanidineacetic acid, had been found and identified as potential biomarkers, which suggested that the disorder of purine metabolism and amino acid metabolism played an important role in the mechanism of AS.
