Modification of bacterial cell membrane to accelerate decolorization of textile wastewater effluent using microbial fuel cells: role of gamma radiation, salinity and endogenous biosurfactant induction

A combined approach was investigated to accelerate Microbial Fuel Cell (MFC) performance and textile wastewater decolorization through modifying bacterial membrane. The aim was to increase both bacterial adhesion on anode and electron mediator release. Ten Gram-positive exoelectrogenic bacteria were isolated from the anodic biofilm after decolorization of real textile waste water in mediator-less MFC. The isolates were identified and characterized, to understand the nature of the bacteria involved. According to the battery of tests performed, three factors gamma radiation, salinity and induction of endogenous biosurfactant were involved membrane modification. Dielectric measurement, a non-invasive technique, was used to measure the cell membrane permeability and cell surface charge. Plackett-Burman experimental design was carried out to determine the key contributor among the three studied factors. Exposing the cells to 1 KGy gamma radiation led to 7.84- and 1.71- fold increase in total surface-charge and cell-permeability, respectively. Scanning Electron Microscope (SEM) images and surface-bound protein concentrations for the samples indicated that biofilm formation increased under the same conditions. These results have been reflected on the power density profiles and decolorization of textile wastewater. Modification of bacterial membrane prior to MFC operation can be considered highly effective as a pre-treatment tool that accelerates MFC performance

Prediction of survival among patients receiving transarterial chemoembolization for hepatocellular carcinoma: A response-based approach

Background and aims: The heterogeneity of intermediate-stage hepatocellular carcinoma (HCC) and the widespread use of transarterial chemoembolization (TACE) outside recommended guidelines have encouraged the development of scoring systems that predict patient survival. The aim of this study was to build and validate statistical models that offer individualized patient survival prediction using response to TACE as a variable. Approach and results: Clinically relevant baseline parameters were collected for 4,621 patients with HCC treated with TACE at 19 centers in 11 countries. In some of the centers, radiological responses (as assessed by modified Response Evaluation Criteria in Solid Tumors [mRECIST]) were also accrued. The data set was divided into a training set, an internal validation set, and two external validation sets. A pre-TACE model ("Pre-TACE-Predict") and a post-TACE model ("Post-TACE-Predict") that included response were built. The performance of the models in predicting overall survival (OS) was compared with existing ones. The median OS was 19.9 months. The factors influencing survival were tumor number and size, alpha-fetoprotein, albumin, bilirubin, vascular invasion, cause, and response as assessed by mRECIST. The proposed models showed superior predictive accuracy compared with existing models (the hepatoma arterial embolization prognostic score and its various modifications) and allowed for patient stratification into four distinct risk categories whose median OS ranged from 7 months to more than 4 years. Conclusions: A TACE-specific and extensively validated model based on routinely available clinical features and response after first TACE permitted patient-level prognosticatio

Towards Information Agent Interoperability

Currently, many kinds of information agents for different purposes exist. However, agents from different systems are still unable to cooperate, even if they accurately follow a common standard like FIPA, KIF or KQML. Being able to plug agents together with little effort and exchange information easily, would be of a great use for several reasons. Among others, the agents could profit from each others' services. In addition, certain aspects of multi-agent systems could be evaluated without needing to build a complete system. Testing agent systems with standard components would allow simpler comparison. Furthermore, building different agent-based applications would be simplified by combining new software with of the shelf- components. In this paper, we explore the feasibility of practical software development and integration of existing systems, without developing yet another abstract agent architecture

Prediction of Survival Among Patients Receiving Transarterial Chemoembolization for Hepatocellular Carcinoma: A Response-Based Approach

Background and Aims: The heterogeneity of intermediate-stage hepatocellular carcinoma (HCC) and the widespread use of transarterial chemoembolization (TACE) outside recommended guidelines have encouraged the development of scoring systems that predict patient survival. The aim of this study was to build and validate statistical models that offer individualized patient survival prediction using response to TACE as a variable. Approach and Results: Clinically relevant baseline parameters were collected for 4,621 patients with HCC treated with TACE at 19 centers in 11 countries. In some of the centers, radiological responses (as assessed by modified Response Evaluation Criteria in Solid Tumors [mRECIST]) were also accrued. The data set was divided into a training set, an internal validation set, and two external validation sets. A pre-TACE model (\u201cPre-TACE-Predict\u201d) and a post-TACE model (\u201cPost-TACE-Predict\u201d) that included response were built. The performance of the models in predicting overall survival (OS) was compared with existing ones. The median OS was 19.9 months. The factors influencing survival were tumor number and size, alpha-fetoprotein, albumin, bilirubin, vascular invasion, cause, and response as assessed by mRECIST. The proposed models showed superior predictive accuracy compared with existing models (the hepatoma arterial embolization prognostic score and its various modifications) and allowed for patient stratification into four distinct risk categories whose median OS ranged from 7 months to more than 4 years. Conclusions: A TACE-specific and extensively validated model based on routinely available clinical features and response after first TACE permitted patient-level prognostication

Prediction of survival among patients receiving transarterial chemoembolization for hepatocellular carcinoma: A response-based approach

Background and aims: The heterogeneity of intermediate-stage hepatocellular carcinoma (HCC) and the widespread use of transarterial chemoembolization (TACE) outside recommended guidelines have encouraged the development of scoring systems that predict patient survival. The aim of this study was to build and validate statistical models that offer individualized patient survival prediction using response to TACE as a variable. Approach and results: Clinically relevant baseline parameters were collected for 4,621 patients with HCC treated with TACE at 19 centers in 11 countries. In some of the centers, radiological responses (as assessed by modified Response Evaluation Criteria in Solid Tumors [mRECIST]) were also accrued. The data set was divided into a training set, an internal validation set, and two external validation sets. A pre-TACE model ("Pre-TACE-Predict") and a post-TACE model ("Post-TACE-Predict") that included response were built. The performance of the models in predicting overall survival (OS) was compared with existing ones. The median OS was 19.9 months. The factors influencing survival were tumor number and size, alpha-fetoprotein, albumin, bilirubin, vascular invasion, cause, and response as assessed by mRECIST. The proposed models showed superior predictive accuracy compared with existing models (the hepatoma arterial embolization prognostic score and its various modifications) and allowed for patient stratification into four distinct risk categories whose median OS ranged from 7 months to more than 4 years. Conclusions: A TACE-specific and extensively validated model based on routinely available clinical features and response after first TACE permitted patient-level prognosticatio