for the ACME Study Investigators and the Asian Critical Care Clinical Trials Group IMPORTANCE Little data exist on end-of-life care practices in intensive care units (ICUs) in Asia. OBJECTIVE To describe physicians' attitudes toward withholding and withdrawal of life-sustaining treatments in end-of-life care and to evaluate factors associated with observed attitudes. DESIGN, SETTING, AND PARTICIPANTS Self-administered structured and scenario-based survey conducted among 1465 physicians (response rate, 59.6%) who manage patients in ICUs (May-December 2012) at 466 ICUs (response rate, 59.4%) in 16 Asian countries and regions.RESULTS For patients with no real chance of recovering a meaningful life, 1029 respondents (70.2%) reported almost always or often withholding whereas 303 (20.7%) reported almost always or often withdrawing life-sustaining treatments; 1092 respondents (74.5%) deemed withholding and withdrawal ethically different. The majority of respondents reported that vasopressors, hemodialysis, and antibiotics could usually be withheld or withdrawn in end-of-life care, but not enteral feeding, intravenous fluids, and oral suctioning. For severe hypoxic-ischemic encephalopathy after cardiac arrest, 1201 respondents (82.0% [range between countries, 48.4%-100%]) would implement do-not-resuscitate orders, but 788 (53.8% [range, 6.1%-87.2%]) would maintain mechanical ventilation and start antibiotics and vasopressors if indicated. On multivariable analysis, refusal to implement do-not-resuscitate orders was more likely with physicians who did not value families' or surrogates' requests (adjusted odds ratio [AOR], 1.67 [95% CI, 1.16-2.40]; P = .006), who were uncomfortable discussing end-of-life care (AOR, 2.38 [95% CI, 1.62-3.51]; P < .001), who perceived greater legal risk (AOR, 1.92 [95% CI, 1.26-2.94]; P = .002), and in low-to middle-income economies (AOR, 2.73 [95% CI, 1.56-4.76]; P < .001). Nonimplementation was less likely with physicians of Protestant (AOR, 0.36 [95% CI, 0.16-0.80]; P = .01) and Catholic (AOR, 0.22 [95% CI, 0.09-0.58]; P = .002) faiths, and when out-of-pocket health care expenditure increased (AOR, 0.98 per percentage of total health care expenditure [95% CI, 0.97-0.99]; P = .02). CONCLUSIONS AND RELEVANCEWhereas physicians in ICUs in Asia reported that they often withheld but seldom withdrew life-sustaining treatments at the end of life, attitudes and practice varied widely across countries and regions. Multiple factors related to country or region, including economic, cultural, religious, and legal differences, as well as personal attitudes, were associated with these variations. Initiatives to improve end-of-life care in Asia must begin with a thorough understanding of these factors.

Significant differences in ICU physicians' self-reported practice of limiting life-sustaining treatments, the role of families and surrogates, perception of legal risks and financial considerations exist between low-middle-income and high-income Asian countries and regions.

No abstract

Despite similarities in cultures and a common emphasis on the role of family, differences exist in physician perceptions and practices of end-of-life ICU care in China, Korea, and Japan. These findings may be due to differences in the degree of Westernization, national healthcare systems, economic status, and legal climate.

Rhubarb might maintain the intestinal mucosal barrier through regulating intestinal flora and inhibiting intestinal inflammatory response in rats with SAP.

No abstract

Circular RNAs (circRNAs) are widely expressed in human cells and tissues and can form a covalently closed exon circularization, which have stable patterns and play important regulatory roles in physiological or pathological process. There is still lack of a comprehensively disease-related knowledge base for in-depth analysis of circRNAs. In this paper, a cancer circRNAs-related database (CCRDB) was established. The CCRDB’s initial circRNAs data were collected by sequencing experimental data of 10 samples from 5 patients with hepatocellular carcinoma (HCC), where a total of 11 501 circRNAs were found and can easily be expanded by collecting and analyzing external data sources such as circBASE ( 1 ). Using CCRDB, we have further studied the relationships between circRNAs and HCC and found that circRNAs (hsa_circ_ 0002130, hsa_circ_0084615, hsa_circ_0001445, hsa_circ_0001727 and hsa_circ_0001361) and the corresponding genes ID [C3 ( 2 , 3 ), ASPH ( 4 ), SMARCA5 ( 5 ), ZKSCAN1 ( 6 ) and FNDC3B ( 7 )], respectively, might be the potential biomarker targets for HCC. Furthermore, our experiment also found that some new circRNAs chromosome sites chr12:23998917 24048958 and chr16:72090429 72093087 and the corresponding genes ID (SOX5 ( 8 ) and HP ( 9 ), respectively), might be the potential biomarker targets for HCC. These results indicate that CCRDB can effectively reveal the relationships between circRNAs and HCC. As the first circRNAs database to provide analysis and comparison functions, it is of great significance for researchers to further study the rules of circRNAs, to understand the causes of circRNAs in disease discovery and to find target genes for therapeutic approaches.

Circular RNA (circRNA) is a closed long non-coding RNA (lncRNA) formed by covalently closed loops through back-splicing. Emerging evidence indicates that circRNA can influence cellular physiology through various molecular mechanisms. Thus, accurate circRNA identification and prediction of its regulatory information are critical for understanding its biogenesis. Although several computational tools based on machine learning have been proposed for circRNA identification, the prediction accuracy remains to be improved. Here, first we present circLGB, a machine learning-based framework to discriminate circRNA from other lncRNAs. circLGB integrates commonly used sequence-derived features and three new features containing adenosine to inosine (A-to-I) deamination, A-to-I density and the internal ribosome entry site. circLGB categorizes circRNAs by utilizing a LightGBM classifier with feature selection. Second, we introduce circMRT, an ensemble machine learning framework to systematically predict the regulatory information for circRNA, including their interactions with microRNA, the RNA binding protein, and transcriptional regulation. Feature sets including sequence-based features, graph features, genome context, and regulatory information features were modeled in circMRT. Experiments on public and our constructed datasets show that the proposed algorithms outperform the available state-of-the-art methods.