A novel prognostic model to predict mortality in patients with acute-on-chronic liver failure in intensive care unit.

Acute-on-chronic liver failure (ACLF) implies high short-term mortality rates and usually requires intensive care unit (ICU) admission. Proper prognosis for these patients is crucial for early referral for liver transplantation. The superiority of CLIF-C ACLF score in Asian patients with ACLF admitted to an ICU remains inconclusive when compared to other scoring systems. The purpose of the study is (i) to compare the predictive performance of original MELD, MELD-Lactate, CLIF-C ACLF, CLIF-C ACLF-Lactate, and APACHE-II scores for short-term mortality assessment. (ii) to build and validate a novel scoring system and to compare its predictive performance to that of the original five scores. Two hundred sixty-five consecutive cirrhotic patients with ACLF who were admitted to our ICU were enrolled. The prognostic values for mortality were assessed by ROC analysis. A novel model was developed and internally validated using fivefold cross-validation. Alcohol abuse was identified as the primary etiology of cirrhosis. The AUROC of the five prognostic scores were not significantly superior to each other in predicting 1-month and 3-month mortality. The newly developed prognostic model, incorporating age, alveolar-arterial gradient (A-a gradient), BUN, total bilirubin level, INR, and HE grades, exhibited significantly improved performance in predicting 1-month and 3-month mortality with AUROC of 0.863 and 0.829, respectively, as compared to the original five prognostic scores. The novel ACLF model seems to be superior to the original five scores in predicting short-term mortality in ACLF patients admitted to an ICU. Further rigorous validation is required.

Comparing Three Profoundly Influential Prognostic Scores in Cirrhotic Patients with Acute-on-Chronic-Liver Failure Admitted to the ICU: Prediction of One-Month Mortality-A Retrospective Cohort Study.

BACKGROUND: Acute-on-chronic-liver failure (ACLF) demonstrates high short-term mortality rates and usually requires intensive care unit (ICU) admission. Accurate prognostication of these patients is pivotal for timely referral for liver transplantation. The superiority of CLIF-C ACLF, CLIF-C ACLF lactate, and NACSELD-ACLF scores in Asian patients with ACLF admitted to an ICU remains inconclusive. AIMS: To compare the predictive performance of CLIF-C ACLF, CLIF-C ACLF lactate, and NACSELD-ACLF scores for one-month mortality. METHODS: 276 consecutive cirrhotic patients with ACLF admitted to ICU were enrolled. The prognostic values for one-month mortality were assessed by AUROC analysis. RESULTS: The primary cause of cirrhosis in this cohort was alcohol abuse (56.5%). AUROC analysis (95% confidence intervals) demonstrated that CLIF-C ACLF lactate [0.802 (0.747-0.856)] outperformed both CLIF-C ACLF [0.791 (0.733-0.848)] and NACSELD-ACLF [0.673 (0.606-0.740)] in predicting one-month mortality. However, no statistically significant difference was observed between the predictive abilities of CLIF-C ACLF and CLIF-C ACLF lactate. CONCLUSIONS: In critically ill cirrhotic patients with ACLF admitted to the hepatology ICU, CLIF ACLF-lactate outperformed CLIF-C ACLF and NACSELD-ACLF in predicting one-month mortality. Nevertheless, no statistically significant difference was observed between CLIF-C ACLF and CLIF-C ACLF lactate. Larger-scale multi-center prospective studies are warranted to validate these results.

Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans.

Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening and genetic screening studies. The effects of various antiaging interventions on the rate of aging in the early stage of the aging process can be determined based on the quantification of physiological age. However, in general, the age of C. elegans is evaluated via human visual inspection of morphological changes based on personal experience and subjective judgment. For example, the rate of motor activity decay has been used to predict lifespan in early- to mid-stage aging. Using image processing, the physiological age of C. elegans can be measured and then classified into periods or classes from childhood to elderhood (e.g., 3 periods comprising days 0-2, 4-6 and 10-12) by using texture entropy (Shamir, L. et al., 2009). Our dataset consists of 913 microscopic images of C. elegans, with approximately 60 images per day from day 1 to day 14 of adulthood. We present quantitative methods to measure the physiological age of C. elegans with convolution neural networks (CNNs), which can measure age with a granularity of days rather than periods. The methods achieved a mean absolute error (MAE) of less than 1 day for the measured age of C. elegans. In our experiments, we found that after training and testing our dataset, 5 popular CNN models, 50-layer residual network (ResNet50), InceptionV3, InceptionResNetV2, 16-layer Visual Geometry Group network (VGG16) and MobileNet, measured the physiological age of C. elegans with an average testing MAE of 1.58 days. Furthermore, based on the results, we propose two models, one model for linear regression analysis and the other model for logistic regression, that combine a CNN model and a new attribute: curved_or_straight. The linear regression analysis model achieved a test MAE of 0.94 days; the logistic regression model achieved an accuracy of 84.78 percent with an error tolerance of 1 day.

Functional Genomics Identifies Hepatitis-Induced STAT3-TYRO3-STAT3 Signaling as a Potential Therapeutic Target of Hepatoma.

PURPOSE: Hepatitis promotes the development and recurrence of hepatocellular carcinoma (HCC). Receptor tyrosine kinases (RTK) play critical roles in the development of many cancers. We explored the potential roles of RTKs in hepatitis-related liver cancers. EXPERIMENTAL DESIGN: We conducted loss-of-function screening to elucidate the roles of RTKs in the development of HCC in vitro and in vivo. RESULTS: Many RTKs were coexpressed in HCC and were involved in tumor development and growth. Of these, TYRO3 promoted tumor growth and was clinically associated with hepatitis activity and poor prognosis. In mice, chemical-induced hepatitis transcriptionally activated Tyro3 expression via IL-6/IL6R-STAT3 signaling. Moreover, hepatitis-associated apoptotic cells facilitated the presentation of GAS6, a TYRO3 ligand, to further activate TYRO3-mediated signaling. Furthermore, TYRO3 activation elicited intracellular SRC- and STAT3 signaling. In mice, hepatitis and Tyro3 synergistically promoted HCC development. Silencing TYRO3 expression or inhibiting its kinase activity suppressed xenograft HCC growth in nude mice. CONCLUSIONS: Many RTKs are simultaneously involved in HCC development. Hepatitis exerts dual effects on the activation of TYRO3-mediated signaling in HCC cells, which further elicits the "TYRO3-STAT3-TYRO3" signaling loop to facilitate tumor growth. Our findings unveil a previously unrecognized link between RTKs and hepatitis-associated HCC and suggest TYRO3 as a marker and therapeutic target for the HCCs with higher hepatitis activity.

Denitrifying characteristics of the multiple stages enhanced biological nutrient removal process with external carbon sources.

This research investigated denitrifying activity of activated sludge with three external carbon sources (sodium acetate, methanol and glucose) via a series of batch experiments. Activated sludge used was cultivated in a multiple stages enhanced biological nutrient removal (EBNR) process that exhibited high removal efficiency of effective carbon, nitrogen, and phosphorus. Results showed type of external carbon source had a significant influence on specific nitrate utilization rate, nitrite accumulation, adaptive time of microorganisms, and nitrate removal efficiency. Sodium acetate addition resulted in high phosphate concentration in effluent; meanwhile methanol caused increasing turbidity and carbon breakthrough problem. When glucose was fed to be the external carbon source, accumulative nitrite concentration was higher than that with sodium acetate or methanol addition. When sodium acetate, methanol and glucose were used to be the electron donor, average dosages for nitrate elimination were 6.97, 5.85, and 5.65 mg-COD/mg-N, respectively. Because the final polyhydroxyalkanoates (PHAs) concentrations contained within the biomass were more than the original level and no phosphate re-release was observed, glycogen-accumulating organisms (GAOs) might exist in the multiple stages EBNR process and increased carbon dosage for further nitrate removal.