Desflurane consumption during automated closed-circuit delivery is higher than when a conventional anesthesia machine is used with a simple vaporizer-O2-N2O fresh gas flow sequence

The Zeus® (Dräger, Lübeck, Germany), an automated closed-circuit anesthesia machine, uses high fresh gas flows (FGF) to wash-in the circuit and the lungs, and intermittently flushes the system to remove unwanted N₂. We hypothesized this could increase desflurane consumption to such an extent that agent consumption might become higher than with a conventional anesthesia machine (Anesthesia Delivery Unit [ADU®], GE, Helsinki, Finland) used with a previously derived desflurane-O₂-N₂O administration schedule that allows early FGF reduction.Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Mathematical method to build an empirical model for inhaled anesthetic agent wash-in

<p>Abstract</p> <p>Background</p> <p>The wide range of fresh gas flow - vaporizer setting (FGF - F_D) combinations used by different anesthesiologists during the wash-in period of inhaled anesthetics indicates that the selection of FGF and F_Dis based on habit and personal experience. An empirical model could rationalize FGF - F_Dselection during wash-in.</p> <p>Methods</p> <p>During model derivation, 50 ASA PS I-II patients received desflurane in O₂with an ADU^®anesthesia machine with a random combination of a fixed FGF - F_Dsetting. The resulting course of the end-expired desflurane concentration (F_A) was modeled with Excel Solver, with patient age, height, and weight as covariates; NONMEM was used to check for parsimony. The resulting equation was solved for F_D, and prospectively tested by having the formula calculate F_Dto be used by the anesthesiologist after randomly selecting a FGF, a target F_A(F_At), and a specified time interval (1 - 5 min) after turning on the vaporizer after which F_Athad to be reached. The following targets were tested: desflurane F_At3.5% after 3.5 min (n = 40), 5% after 5 min (n = 37), and 6% after 4.5 min (n = 37).</p> <p>Results</p> <p>Solving the equation derived during model development for F_Dyields F_D=-(e^{(-FGF*-0.23+FGF*0.24)}*(e^(FGF*-0.23)*F_At*Ht*0.1-e^(FGF*-0.23)*FGF*2.55+40.46-e^(FGF*-0.23)*40.46+e^{(FGF*-0.23+Time/-4.08)}*40.46-e^(Time/-4.08)*40.46))/((-1+e^(FGF*0.24))*(-1+e^(Time/-4.08))*39.29). Only height (Ht) could be withheld as a significant covariate. Median performance error and median absolute performance error were -2.9 and 7.0% in the 3.5% after 3.5 min group, -3.4 and 11.4% in the 5% after 5 min group, and -16.2 and 16.2% in the 6% after 4.5 min groups, respectively.</p> <p>Conclusions</p> <p>An empirical model can be used to predict the FGF - F_Dcombinations that attain a target end-expired anesthetic agent concentration with clinically acceptable accuracy within the first 5 min of the start of administration. The sequences are easily calculated in an Excel file and simple to use (one fixed FGF - F_Dsetting), and will minimize agent consumption and reduce pollution by allowing to determine the lowest possible FGF that can be used. Different anesthesia machines will likely have different equations for different agents.</p

Neuronal Cultures and Nanomaterials

4noIn recent years, the scientific community has witnessed an exponential increase in the use of nanomaterials for biomedical applications. In particular, the interest of graphene and graphene-based materials has rapidly risen in the neuroscience field due to the properties of this material, such as high conductivity, transparency and flexibility. As for any new material that aims to play a role in the biomedical area, a fundamental aspect is the evaluation of its toxicity, which strongly depends on material composition, chemical functionalization and dimensions. Furthermore, a wide variety of three-dimensional scaffolds have also started to be exploited as a substrate for tissue engineering. In this application, the topography is probably the most relevant amongst the various properties of the different materials, as it may allow to instruct and interrogate neural networks, as well as to drive neural growth and differentiation. This chapter discusses the in vitro approaches, ranging from microscopy analysis to physiology measurements, to investigate the interaction of graphene with the central nervous system. Moreover, the in vitro use of three-dimensional scaffolds is described and commented.reservedmixedMattia Bramini, Anna Rocchi, Fabio Benfenati, Fabrizia CescaBramini, Mattia; Rocchi, Anna; Benfenati, Fabio; Cesca, Fabrizi