Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

An Efficient Timer and Sizer of Biomacromolecular Motions

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An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.     

Computational approaches from polymer physics to investigate chromatin folding

Microscopy and sequencing-based technologies are providing increasing insights into chromatin architecture. Nevertheless, a full comprehension of chromosome folding and its link with vital cell functions is far from accomplished at the molecular level. Recent theoretical and computational approaches are providing important support to experiments to dissect the three-dimensional structure of chromosomes and its organizational mechanisms. Here, we review, in particular, the String&Binders polymer model of chromatin that describes the textbook scenario where contacts between distal DNA sites are established by cognate binders. It has been shown to recapitulate key features of chromosome folding and to be able at predicting how phenotypes causing structural variants rewire the interactions between genes and regulators.     

An improved intrafemoral injection with minimized leakage as an orthotopic mouse model of osteosarcoma

Osteosarcoma, the most common type of primary bone cancer, is the second highest cause of cancer-related death in pediatric patients. To understand the mechanisms behind osteosarcoma progression and to discover novel therapeutic strategies for this disease, a reliable and appropriate mouse model is essential. For this purpose, osteosarcoma cells need to be injected into the bone marrow. Previously, the intratibial and intrafemoral injection methods were reported; however, the major drawback of these methods is the potential leakage of tumor cells from the injection site during or after these procedures. To overcome this, we have established an improved method to minimize leakage in an orthotopic mouse model of osteosarcoma. By taking advantage of the anatomical benefits of the femur with less bowing and larger medullary cavity than those of the tibia, osteosarcoma cells are injected directly into the femoral cavity following reaming of its intramedullary space. To prevent potential leakage of tumor cells during and after the surgery, the injection site is sealed with bone wax. This method requires a minor surgery of approximately 15 min under anesthesia. Our established orthotopic osteosarcoma model could serve as a valuable and reliable tool for examining progression of various types of bone tumors.     

Synthesis and biological characterization of new aryloxyindole-4,9-diones as potent trypanosomicidal agents

A new indole-4,9-dione and their phenoxy derivatives were synthesized and evaluated in vitro against the epimastigote form of Trypanosoma cruzi, Y strain. All of these novel compounds were found to be extremely potent and selective that the standard drug nifurtimox. Interestingly, phenoxyindole-4,9-dione 9d displayed excellent nanomolar inhibitory activity, IC₅₀ = 20 nM, and high selectivity index, SI = 625. In silico studies using MOE program were performed to generate a preliminary pharmacophore model.     

On the Estimation of Heritability from Unbalanced Data

The distribution and moments, of ANOVA estimator of heritability are given under unbalanced random model. These expressions are used to investigate the effect of unbalancedness on the bias and variance/MSE of the estimator and also the validity of certain approximations for its variance, numerically. The computed results reveal that the unbalancedness increases both the bias and variance/MSE of the estimator and the Smith-approximation for the variance of the estimator provides better accuracy.     

A mathematical model for the force and energetics in competitive running

A simple mathematical model for competitive running is developed. This model contains the force and energy reserves as key variables and it describes their relationship and dynamics. It is made up of three submodels for the biomechanics of running, the energetics and the optimization. The model for the energetics is an extension of the hydraulic model of Margaria and Morton. The key geometric parameters of this piecewise linear, three compartment model are determined on the basis of well known physiological facts and data.