Clinicopathologic findings in thoroughbred horses during a high speed – short term competition

The aim of this study was to examine whether a real high speed-short term competition influences clinicopathological data focusing on muscle enzymes, iron profile and Acute Phase Proteins. 30 Thoroughbred racing horses (15 geldings and 15 females) aged between 4-12 years (mean 7 years), were used for the study. All the animals performed a high speed-short term competition for a total distance of 154 m in about 12 seconds, repeated 8 times, within approximately one hour (Niballo Horse Race). Blood samples were obtained 24 hours before and within 30 minutes after the end of the races. On all samples were performed a complete blood count (CBC), biochemical and haemostatic profiles. The post-race concentrations for the single parameter were corrected using an estimation of the plasma volume contraction according to the individual Alb concentration. Data were analysed with descriptive statistics and the percentage of variation from the baseline values were recorded. Pre- and post-race results were compared with non-parametric statistics (Mann Whitney U test). A difference was considered significant at p<0.05. A significant plasma volume contraction after the race was detected (Hct, Alb; p<0.01). Other relevant findings were increased concentrations of muscular enzymes (CK, LDH; p<0.01), Crt (p<0.01), significant increased uric acid (p<0.01), a significant decrease of haptoglobin (p<0.01) associated to an increase of ferritin concentrations (p<0.01), significant decrease of fibrinogen (p<0.05) accompanied by a non-significant increase of D-Dimers concentrations (p=0.08). This competition produced relevant abnormalities on clinical pathology in galloping horses. This study confirms a significant muscular damage, oxidative stress, intravascular haemolysis and subclinical hemostatic alterations. Further studies are needed to better understand the pathogenesis, the medical relevance and the impact on performance of these alterations in equine sport medicine.

Analysis of eruptive and seismic sequences to improve the short-and long-term eruption forecasting

Forecasting the time, location, nature, and scale of volcanic eruptions is one of the most urgent aspects of modern applied volcanology. The reliability of probabilistic forecasting procedures is strongly related to the reliability of the input information provided, implying objective criteria for interpreting the historical and monitoring data. For this reason both, detailed analysis of past data and more basic research into the processes of volcanism, are fundamental tasks of a continuous information-gain process; in this way the precursor events of eruptions can be better interpreted in terms of their physical meanings with correlated uncertainties. This should lead to better predictions of the nature of eruptive events. In this work we have studied different problems associated with the long- and short-term eruption forecasting assessment. First, we discuss different approaches for the analysis of the eruptive history of a volcano, most of them generally applied for long-term eruption forecasting purposes; furthermore, we present a model based on the characteristics of a Brownian passage-time process to describe recurrent eruptive activity, and apply it for long-term, time-dependent, eruption forecasting (Chapter 1). Conversely, in an effort to define further monitoring parameters as input data for short-term eruption forecasting in probabilistic models (as for example, the Bayesian Event Tree for eruption forecasting -BET_EF-), we analyze some characteristics of typical seismic activity recorded in active volcanoes; in particular, we use some methodologies that may be applied to analyze long-period (LP) events (Chapter 2) and volcano-tectonic (VT) seismic swarms (Chapter 3); our analysis in general are oriented toward the tracking of phenomena that can provide information about magmatic processes. Finally, we discuss some possible ways to integrate the results presented in Chapters 1 (for long-term EF), 2 and 3 (for short-term EF) in the BET_EF model (Chapter 4).