Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with a high mortality rate, though outcomes of the different lung compliance phenotypes are unclear. We aimed to measure lung compliance and examine other factors associated with mortality in COVID-19 patients with ARDS. METHODS: Adult patients with COVID-19 ARDS who required invasive mechanical ventilation at 8 hospitals in Singapore were prospectively enrolled. Factors associated with both mortality and differences between high (<40mL/cm H RESULTS: A total of 102 patients with COVID-19 who required invasive mechanical ventilation were analysed; 15 (14.7%) did not survive. Non-survivors were older (median 70 years, interquartile range [IQR] 67-75 versus median 61 years, IQR 52-66; CONCLUSION COVID-19 ARDS patients with higher compliance on the day of intubation and a longitudinal decrease over time had a higher risk of death.
COVID-19 ; Humans ; Lung Compliance ; Phenotype ; Respiratory Distress Syndrome/therapy* ; SARS-CoV-2

Autoimmune blistering diseases (AIBD) represent a group of rare and chronic disorders with significant impact on quality of life (QoL). The aim of this study was to assess the validity and reliability of the Malay translation of the autoimmune bullous disease quality of life (ABQOL) questionnaire.

Objectives: Type 2 diabetes mellitus (T2DM) shares a complex relationship with bone metabolism and few studies investigated the effect of impaired bone health on the risk of T2DM. This study was conducted to investigate the association between hip fractures and the risk of incident T2DM. Methods: This is a retrospective cohort study using data from the real-world hip fracture cohort. Hong Kong Chinese patients aged ≥ 65 years without T2DM who were admitted to public hospitals due to a fall between 2008 and 2015 were included in the study. Patients who sustained falls with and without hip fractures were matched by propensity score (PS) at a 1:1 ratio. Competing risk regression was used to evaluate the association between hip fracture and incident T2DM, with death being the competing event. Results: A total of 23,314 hip fracture cases were matched to 23,314 controls. The median follow-up time was 5.09 years. The incidence rate of T2DM was 11.947 and 14.505 per 1000 person-years for the hip fracture and control group respectively. After accounting for the competing risk of death, the hip fracture group had a significantly lower risk of developing T2DM (HR: 0.771, 95% CI: 0.719–0.827). Similar results were observed in all subgroups after stratification by age and sex. Conclusions Hip fracture was found to be associated with a reduced risk of T2DM. These findings provide insight into the topic of bone and glucose metabolism and prompt further research in evaluating the role of bone health in the management of T2DM.