A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment.

The life cycle assessment (LCA) framework has established itself as the leading tool for the assessment of the environmental impact of products. Several works have established the need of integrating the LCA and risk analysis methodologies, due to the several common aspects. One of the ways to reach such integration is through guaranteeing that uncertainties in LCA modeling are carefully treated. It has been claimed that more attention should be paid to quantifying the uncertainties present in the various phases of LCA. Though the topic has been attracting increasing attention of practitioners and experts in LCA, there is still a lack of understanding and a limited use of the available statistical tools. In this work, we introduce a protocol to conduct global sensitivity analysis in LCA. The article focuses on the life cycle impact assessment (LCIA), and particularly on the relevance of global techniques for the development of trustable impact assessment models. We use a novel characterization model developed for the quantification of the impacts of noise on humans as a test case. We show that global SA is fundamental to guarantee that the modeler has a complete understanding of: (i) the structure of the model and (ii) the importance of uncertain model inputs and the interaction among them.

Cancer cell reprogramming: stem cell differentiation stage factors and an agent based model to optimize cancer treatment.

The recent tumor research has lead scientists to recognize the central role played by cancer stem cells in sustaining malignancy and chemo-resistance. A model of cancer presented by one of us describes the mechanisms that give rise to the different kinds of cancer stem-like cells and the role of these cells in cancer diseases. The model implies a shift in the conceptualization of the disease from reductionism to complexity theory. By exploiting the link between the agent-based simulation technique and the theory of complexity, the medical view is here translated into a corresponding computational model. Two main categories of agents characterize the model, 1) cancer stem-like cells and 2) stem cell differentiation stage factors. Cancer cells agents are then distinguished based on the differentiation stage associated with the malignancy. Differentiation factors interact with cancer cells and then, with varying degrees of fitness, induce differentiation or cause apoptosis. The model inputs are then fitted to experimental data and numerical simulations carried out. By performing virtual experiments on the model's choice variables a decision-maker (physician) can obtains insights on the progression of the disease and on the effects of a choice of administration frequency and or dose. The model also paves the way to future research, whose perspectives are discussed.

Decision making during nuclear power plant incidents: a new approach to the evaluation of precursor events.

Renewed interest in precursor analysis has shown that the evaluation of near misses is an interdisciplinary effort, fundamental within the life of an organization for reducing operational risks and enabling accident prevention. The practice of precursor analysis has been a part of nuclear power plant regulation in the United States for over 25 years. During this time, the models used in the analysis have evolved from simple risk equations to quite complex probabilistic risk assessments. But, one item that has remained constant over this time is that the focus of the analysis has been on modeling the scenario using the risk model (regardless of the model sophistication) and then using the results of the model to determine the severity of the precursor incident. We believe that evaluating precursors in this fashion could be a shortcoming since decision making during the incident is not formally investigated. Consequently, we present the idea for an evaluation procedure that enables one to integrate current practice with the evaluation of decisions made during the precursor event. The methodology borrows from technologies both in the risk analysis and the decision analysis realms. We demonstrate this new methodology via an evaluation of a U.S. precursor incident. Specifically, the course of the incident is represented by the integration of a probabilistic risk assessment model (i.e., the risk analysis tool) with an influence diagram and the corresponding decision tree (i.e., the decision analysis tools). The results and insights from the application of this new methodology are discussed.

Pharmacokinetics of a sustained release formulation of pyridoxal phosphate of buflomedil after single or repeated oral doses in healthy volunteers.

The pharmacokinetics of a sustained release (SR) formulation of pyridoxal phosphate of buflomedil (Pirxane retard) has been studied after oral administration to healthy volunteers using among else a gaschromatographic dosage method. After oral administration of 400 mg of the SR formulation, pyridoxal phosphate of buflomedil has a much slower kinetics compared to the normal formulation (tmax:approx. 1.5 h) reaching the maximum plasma concentration, which was about 467 ng/ml, in about 3 h. After 24 h the concentrations were still about 1/10 (48 ng/ml) the maximum value. 24-h urinary excretion was about 21% of the administered dose. Repeated administration of the SR formulation for 7 days in single daily doses of 400 mg gave steady state plasma levels (ca. 250 ng/ml) 12 h after the administration without statistically significant variations. The plasma concentrations of the drug measured daily after reaching the steady state were similar one to the other. The tolerability was very good and no local or systemic side effects of any kind were reported.