Different Apoptotic Pathways Activated by Oxaliplatin in Primary Astrocytes vs. Colo-Rectal Cancer Cells

Oxaliplatin-based chemotherapy improves the outcomes of metastatic colorectal cancer patients. Its most significant and dose-limiting side effect is the development of a neuropathic syndrome. The mechanism of the neurotoxicity is unclear. The limited knowledge about differences existing between neurotoxic and antitumor effects hinders the discovery of effective and safe adjuvant therapies. In vitro, we suggested cell-specific activation apoptotic pathways in normal nervous cells (astrocytes) vs. colon-cancer cells (HT-29). In the present research we compared the apoptotic signals evoked by oxaliplatin in astrocytes and HT-29 analyzing the intrinsic and extrinsic apoptotic pathways. In astrocytes, oxaliplatin induced a mitochondrial derangement measured as cytosolic release of cytochrome C, increase in superoxide anion levels and decreased expression of the antiapoptotic protein Bcl-2. Caspase-8, a main initiator of the extrinsic process remained unaltered. On the contrary, in HT-29 oxaliplatin increased caspase-8 activity and Bid expression, thus activating the extrinsic apoptosis, while the Bcl-2 increased expression blocked the mitochondrial damage. Data suggest the preferred activation of the intrinsic apoptosis as oxaliplatin damage signaling in normal nervous cells. The extrinsic pathway prevails in tumor cells indicating a possible strategy for planning new molecules to treat oxaliplatin-dependent neurotoxicity without negatively influence chemotherapy.     

Modelling habitat preferences for fin whales and striped dolphins in the Pelagos Sanctuary (Western Mediterranean Sea) with physiographic and remote sensing variables

One of the needs of the Pelagos Sanctuary for the Conservation of Mediterranean Marine Mammals is information on critical habitats for cetaceans. This study modelled habitat use and preferences of fin whales and striped dolphins (the two most abundant species in the area) with the aim of providing this information, using sighting data collected between 1993 and 1999. The study area was divided into a 2′ latitude by 2′ longitude grid. The explanatory variables considered in the models were physiographic variables (mean, range and standard deviation of depth and slope, and distance from the nearest coastline) and remotely-sensed data (Sea Surface Temperature and Chlorophyll-a concentration). The former were calculated for each cell using GIS tools, while the latter were obtained from AVHRR and SeaWiFS sensors. Generalized Additive Models (GAMs) with multidimensional smoothers were used to model the distribution of fin whales and striped dolphins in relation to these variables, and Classification And Regression Trees were used for habitat characterization and predictive models. The GAMs were coupled with Generalized Estimating Equations (GEEs) to account for temporal autocorrelation in the errors and to help ensure model selection was reliable; the QIC statistic was used alongside GEE-based p-values. Bathymetric features were the most valuable predictors in the Pelagos Sanctuary area for both species. Sea Surface Temperature values were indicators of striped dolphin and fin whale presence, with both species showing a tendency to prefer colder waters (21–24 °C). Chl-a levels were selected by the GAM models only for striped dolphins, and with large associated uncertainty; this may be related to the relatively brief period examined (only 2 years) and/or to any functional relationship operating at a different geographical or temporal scale. The boosted classification trees however indicated an importance of Chl-a for both species. The techniques applied to this dataset proved to be valuable tools to describe habitat use and preferences of cetaceans, and the use of the remotely-sensed data can substantially improve the predictions. The results of this study will be used for assessing critical habitats within the Pelagos Sanctuary and will provide information for conservation and management in the Sanctuary.     

Piezoresistif microsensors for an instrumented medical needle for its real time monitoring in a microlocalization tool

Microsystem Technologies - Because today a physician has no reliable tool to monitor a medical needle, when he inserted it into the human body, this paper deals with an instrumented medical needle,...     

Effect of a single dose of ibuprofen lysinate before embryo transfer on pregnancy rates in cows

Embryo implantation is a critical step in both cows and humans. The use of ibuprofen lysinate to enhance implantation has been investigated in cattle with the specific aim of improving pregnancy rates after embryo transfer. In this study, heifers (n = 100) were assigned randomly to one of two groups: one group was treated i.m. with 5 mg ibuprofen lysinate kg(-1) body weight 1 h before embryo transfer and a control group received vehicle only. A single embryo was transferred into each recipient cow. There was a significant difference in the number of pregnancies after embryo transfer between cows in the treated (41 of 50; 82%) and control (28 of 50; 56%) groups (P < 0.05). These data indicate that ibuprofen lysinate may be an effective adjunctive treatment for assisted reproduction in cattle. Further studies are needed to clarify whether this effect is associated with the reduction of cyclooxygenase enzyme isoforms during embryo transfer or whether other mechanisms are involved.     

Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time–Frequency Analysis

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, methods are presented to identify developing electrical and mechanical faults based on both the short-time Fourier transform and wavelet analysis of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified by developing a linear discriminant classifier based on the transform coefficients.