Although type 2 diabetes (T2D) is a major comorbidity of novel coronavirus disease 2019 (COVID-19), the impact of blood glucose control on the degree of medical interventions required and on all-cause mortality of patients with COVID-19 and pre-existing T2D remains unclear. Here, Zhu et al. report that among $7,300 individuals with COVID-19 (among which nearly 1,000 had T2D) in Hubei Province, China, those with T2D had significantly increased medical interventions and mortality risk. But among the patients with T2D, those with well-controlled blood glucose regulation (upper limit % 10 mmol/L) fared much better than those with poorly controlled blood glucose (upper limit > 10 mmol/L). These findings provide clinical evidence correlating more proper blood glucose control with improved outcomes in patients with COVID-19.

Abstract:The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

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Cardamine enshiensis is a well-known selenium (Se)-hyperaccumulating plant. Se is an essential trace element associated with many health benefits. Despite its critical importance, genomic information of this species is limited. Here, we report a chromosome-level genome assembly of C. enshiensis, which consists of 443.4 Mb in 16 chromosomes with a scaffold N50 of 24 Mb. To elucidate the mechanism of Se tolerance and hyperaccumulation in C. enshiensis, we generated and analyzed a dataset encompassing genomes, transcriptomes, and metabolomes. The results reveal that flavonoid, glutathione, and lignin biosynthetic pathways may play important roles in protecting C. enshiensis from stress induced by Se. Hi-C analysis of chromatin interaction patterns showed that the chromatin of C. enshiensis is partitioned into A and B compartments, and strong interactions between the two telomeres of each chromosome were correlated with histone modifications, epigenetic markers, DNA methylation, and RNA abundance. Se supplementation could affect the 3D chromatin architecture of C. enshiensis at the compartment level. Genes with compartment changes after Se treatment were involved in selenocompound metabolism, and genes in regions with topologically associated domain insulation participated in cellular responses to Se, Se binding, and flavonoid biosynthesis. This multiomics research provides molecular insight into the mechanism underlying Se tolerance and hyperaccumulation in C. enshiensis.

Background To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. Methods 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. Results Eight factors, namely, O xygen saturation, blood U rea nitrogen, R espiratory rate, admission before the date the national M aximum number of daily new cases was reached, A ge, P rocalcitonin, C -reactive protein (CRP), and absolute N eutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90–0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93–23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. Conclusions The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.