The assessment of postural stability after ambulatory anesthesia: a comparison of desflurane with propofol

Effects of Progesterone on Isoflurane Requirement for Balance Disturbance and Loss of Righting Reflex in Male Mice

Introduction It has been known that progesterone has central effects, as measured by minimum alveolar concentration in various experimental settings. Previously, we showed that progesterone reduces the sevoflurane requirement for the loss of righting reflex (LORR) using male mice. However, the combination of progesterone and isoflurane has not been studied. Therefore, in this study, we compared the effect of progesterone on anesthetic requirements in a mouse model. Methods Male C57BL/6 mice were treated with either progesterone (75 mg/kg) + olive oil or only olive oil. Animals were studied in closed cylinders supplied with oxygen and isoflurane that rotated four times per minute. Balance disturbance and loss of the righting reflex were counted. The data were analyzed by using a multiple independent variable logistics regression model. Results The concentrations at the onset of balance disturbances, represented by the effective dose 50% (ED50) and effective dose 95% (ED95) of isoflurane, were 0.37% and 0.45% for the control group and 0.34% and 0.41% for the progesterone group, respectively. Similarly, the concentrations for loss of righting reflex (LORR), represented by ED50 and ED95, were 0.55% and 0.62% for the control group and 0.53% and 0.60% for the progesterone group, respectively. Subcutaneous injection of progesterone at a dose of 75 mg/kg significantly reduced the isoflurane requirement for both balance disturbance (p = 0.0022) and LORR (p = 0.0218). Conclusion We conclude that progesterone decreased isoflurane concentration for both balance disturbance and LORR.

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry

One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events. This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project. Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies. This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.