The characteristics of laboratory tests at admission and the risk factors for adverse clinical outcomes of severe and critical COVID-19 patients.

BACKGROUND: The current coronavirus disease 2019 (COVID-19) is a public health emergency. In this study, we aimed to evaluate the risk factors for mortality in severe and critical COVID-19 patients. METHODS: We performed a retrospective study of patients diagnosed with severe and critical COVID-19 from four hospitals in Wuhan, China, by evaluating the clinical characteristics and laboratory results, and using Cox proportional hazards model to assess the risk factors involved in disease progression. RESULTS: In total, 446 patients with COVID-19 were enrolled. The study indicated a high mortality rate (20.2%) in severe and critical COVID-19 patients. At the time of admission, all patients required oxygen therapy, and 52 (12%) required invasive mechanical ventilation, of which 50 (96%) died. The univariate Cox proportional hazards model showed a white blood cell count of more than 10 × 109/L (HR 3.993,95%CI 2.469 to 6.459) that correlated with an increased mortality rate. The multivariable Cox proportional hazards model demonstrated that older age (HR 1.066, 95% CI 1.043 to 1.089) and higher white blood cell count (HR 1.135, 95% CI 1.080 to 1.192) were independent risk factors for determining COVID-19 associated mortality. CONCLUSIONS: COVID-19 is associated with a significant risk of morbidity and mortality in the population. Older age and higher white blood cell count were found to be independent risk factors for mortality.

Landscape of co-expressed genes between the myocardium and blood in sepsis and ceRNA network construction: a bioinformatic approach.

Septic cardiomyopathy is a serious complication of sepsis. The mechanism of disease pathogenesis, which is caused by infection, is well researched. Despite ongoing efforts, there are no viable biological markers in the peripheral blood for early detection and diagnosis of septic cardiomyopathy. We aimed to uncover potential biomarkers of septic cardiomyopathy by comparing the covaried genes and pathways in the blood and myocardium of sepsis patients. Gene expression profiling of GSE79962, GSE65682, GSE54514, and GSE134364 was retrieved from the GEO database. Student's t-test was used for differential expression analysis. K-means clustering analysis was applied for subgroup identification. Least absolute shrinkage and selection operator (LASSO) and logistic regression were utilized for screening characteristic genes and model construction. Receiver operating characteristic (ROC) curves were generated for estimating the diagnostic efficacy. For ceRNA information prediction, miWalk and lncBase were applied. Cytoscape was used for ceRNA network construction. Inflammation-associated genes were upregulated, while genes related to mitochondria and aerobic metabolism were downregulated in both blood and the myocardium. Three groups with a significantly different mortality were identified by these covaried genes, using clustering analysis. Five characteristic genes-BCL2A1, CD44, ADGRG1, TGIF1, and ING3-were identified, which enabled the prediction of mortality of sepsis. The pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis. Thus, the genes we identified have the potential to become biomarkers for septic cardiomyopathy.

Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics Analysis.

Background: Septic cardiomyopathy is widespread during sepsis and has adverse effects on mortality. Diagnosis of septic cardiomyopathy now mainly depends on transthoracic echocardiogram. Although some laboratory tests such as troponin T and atrial brain natriuretic peptide play a role in the diagnosis, specific blood biochemistry biomarkers are still lacking. Objective and Methods. In our study, we sought to find potential biological markers from genes and pathways that are covariant in the blood and myocardium of septic patients. Bioinformatics and machine learning methods were applied to achieve our goal. Datasets of myocardium and peripheral blood of patients with sepsis were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected and received functional enrichment analysis. Unsupervised hierarchical clustering analysis was performed to identify the subtypes of sepsis. Random forest, lasso regression, and logistic regression were used for variable screening and model construction. Internal and external validation sets were applied to verify the efficiency of the model in classifying disease and predicting mortality. Results: By defining significance for genes using Student's t-test, we obtained 1,049 genes commonly changed in both myocardium and blood of patients with sepsis. The upregulated genes (LogFC >0) were related to inflammation pathways, and downregulated (LogFC <0) genes were related to mitochondrial and aerobic metabolism. We divided 468 sepsis patients into two groups with different clinical result based on the mortality-related commonly changed genes (104 genes), using unsupervised hierarchical clustering analysis. In our validation datasets, a six-gene model (SMU1, CLIC3, SP100, ARHGAP25, DECR1, and TNS3) was obtained and proven to perform well in classifying groups and predicting mortality. Conclusion: We have identified genes that have the potential to become biomarkers for septic cardiomyopathy. Additionally, the pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis.

Nutritional risk and therapy for severe and critical COVID-19 patients: A multicenter retrospective observational study.

OBJECTIVE: To evaluate the nutritional risk and therapy in severe and critical patients with COVID-19. METHODS: A total of 523 patients enrolled from four hospitals in Wuhan, China. The inclusion time was from January 2, 2020 to February 15. Clinical characteristics and laboratory values were obtained from electronic medical records, nursing records, and related examinations. RESULTS: Of these patients, 211 (40.3%) were admitted to the ICU and 115 deaths (22.0%). Patients admitted to the ICU had lower BMI and plasma protein levels. The median Nutrition risk in critically ill (NUTRIC) score of 211 patients in the ICU was 5 (4, 6) and Nutritional Risk Screening (NRS) score was 5 (3, 6). The ratio of parenteral nutrition (PN) therapy in non-survivors was greater than that in survivors, and the time to start nutrition therapy was later than that in survivors. The NUTRIC score can independently predict the risk of death in the hospital (OR = 1.197, 95%CI: 1.091-1.445, p = 0.006) and high NRS score patients have a higher risk of poor outcome in the ICU (OR = 1.880, 95%CI: 1.151-3.070, p = 0.012). After adjusted age and sex, for each standard deviation increase in BMI, the risk of in-hospital death was reduced by 13% (HR = 0.871, 95%CI: 0.795-0.955, p = 0.003), and the risk of ICU transfer was reduced by 7% (HR = 0.932, 95%CI:0.885-0.981, p = 0.007). The in-hospital survival time of patients with albumin level ≤35 g/L was significantly decreased (15.9 d, 95% CI: 13.7-16.3, vs 24.2 d, 95% CI: 22.3-29.7, p < 0.001). CONCLUSION: Severe and critical patients with COVID-19 have a high risk of malnutrition. Low BMI and protein levels were significantly associated with adverse events. Early nutritional risk screening and therapy for patients with COVID-19 are necessary.

Uric acid as a prognostic factor and critical marker of COVID-19.

The purpose of this study is to explore whether uric acid (UA) can independently act as a prognostic factor and critical marker of the 2019 novel corona virus disease (COVID-19). A multicenter, retrospective, and observational study including 540 patients with confirmed COVID-19 was carried out at four designated hospitals in Wuhan. Demographic, clinical, laboratory data were collected and analyzed. The primary end point was in-hospital death of patients with COVID-19. The concentration of admission UA (adUA) and the lowest concentration of uric acid during hospitalization (lowUA) in the dead patients were significantly lower than those in the survivors. Multivariate logistic regression analysis showed the concentration of lowUA (OR 0.986, 95% CI 0.980-0.992, p < 0.001) was able to independently predict the risk of in-hospital death. The mean survival time in the low-level group of lowUA was significantly lower than other groups. When lowUA was ≤ 166 µmol/L, the sensitivity and specificity in predicting hospital short-term mortality were 76.9%, (95% CI 68.5-85.1%) and 74.9% (95% CI 70.3-78.9%). This retrospective study determined that the lowest concentration of UA during hospitalization can be used as a prognostic indicator and a marker of disease severity in severe patients with COVID-19.

[Microsatellite analysis of genetic diversity and phylogenetic relationship of nine species of grouper in genus Epinephelus].

Thirteen microsatellite markers of Epinephelus awoara previously discovered by our lab were selected to analyze the genetic diversity and phylogenetic relationship of nine species of grouper (E. awoara, E. merra, E. fario, E. fasciatus, E. lanceolatus, E. akaara, E. septemfasciatus, E. coioides and E. fuscoguttatus) from South China Sea. The results showed that the number of total alleles of these 13 microsatellite loci was 84 in these fishes, the mean number of alleles ranged from 2.69 to 5.38, mean polymorphism information content (PIC) ranged from 0.1976 to 0.4267, mean observed heterozygosity (Ho) from 0.4615 to 0.6239, mean expected heterozygosity (He) from 0.3510 to 0.4754 and mean Hardy-Weinberg departure value (D) from 0.1097 to 0.2836, respectively. All of these indicated that genetic diversity of the nine species of grouper was at a medium level. Two NJ dendrograms showed that E. coioides, E. fuscoguttatus and E. lanceolatus were grouped together, while E. awoara, E. akaara and E. septemfasciatus were in a second group, and E. merra, E. fasciatus and E. fario were in a third group which had a relatively closed relationship with the second group. The dendrograms could also support a conclusion that Promicrops lanceolatus (E. lanceolatus) should be included in genus Epinephelus.