224 research outputs found
Effects of terlipressin as early treatment for protection of brain in a model of haemorrhagic shock
Introduction:
We investigated whether treatment with terlipressin during recovery from hypotension due to haemorrhagic shock (HS) is effective in restoring cerebral perfusion pressure (CPP) and brain tissue markers of water balance, oxidative stress and apoptosis.
Methods:
In this randomised controlled study, animals undergoing HS (target mean arterial pressure (MAP) 40 mmHg for 30 minutes) were randomised to receive lactated Ringerâs solution (LR group; n =14; volume equal to three times the volume bled), terlipressin (TERLI group; n =14; 2-mg bolus), no treatment (HAEMO group; n =12) or sham (n =6). CPP, systemic haemodynamics (thermodilution technique) and blood gas analyses were registered at baseline, shock and 5, 30, 60 (T60), 90 and 120 minutes after treatment (T120). After the animals were killed, brain tissue samples were obtained to measure markers of water balance (aquaporin-4 (AQP4)), Na+-K+-2Clâ co-transporter (NKCC1)), oxidative stress (thiobarbituric acid reactive substances (TBARS) and manganese superoxide dismutase (MnSOD)) and apoptotic damage (Bcl-x and Bax).
Results:
Despite the HS-induced decrease in cardiac output (CO) and hyperlactataemia, resuscitation with terlipressin recovered MAP and resulted in restoration of CPP and in cerebral protection expressed by normalisation of AQP4, NKCC1, TBARS and MnSOD expression and Bcl-x/Bax ratio at T60 and T120 compared with sham animals. In the LR group, CO and blood lactate levels were recovered, but the CPP and MAP were significantly decreased and TBARS levels and AQP4, NKCC1 and MnSOD expression and Bcl-x/Bax ratio were significantly increased at T60 and T120 compared with the sham group.
Conclusions:
During recovery from HS-induced hypotension, terlipressin was effective in normalising CPP and cerebral markers of water balance, oxidative damage and apoptosis. The role of this pressor agent on brain perfusion in HS requires further investigation
Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
Background: Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease.
Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a
linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized.
Methods: We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow
including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed
independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or
asymmetrically distributed.
Results: In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of
formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be
used with caution in the case of independent variables with asymmetric distributions that significantly differ from
the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the
potential effects on neurodevelopment in children exposed to arsenic and manganese.
Conclusions: The procedure proposed has been shown to be valid and capable of expressing the effect size of a
linear regression model based on different change criteria in the variables. Homogenizing the results from different
studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations
had been performed on the dependent and/or independent variables
Astrobiological Complexity with Probabilistic Cellular Automata
Search for extraterrestrial life and intelligence constitutes one of the
major endeavors in science, but has yet been quantitatively modeled only rarely
and in a cursory and superficial fashion. We argue that probabilistic cellular
automata (PCA) represent the best quantitative framework for modeling
astrobiological history of the Milky Way and its Galactic Habitable Zone. The
relevant astrobiological parameters are to be modeled as the elements of the
input probability matrix for the PCA kernel. With the underlying simplicity of
the cellular automata constructs, this approach enables a quick analysis of
large and ambiguous input parameters' space. We perform a simple clustering
analysis of typical astrobiological histories and discuss the relevant boundary
conditions of practical importance for planning and guiding actual empirical
astrobiological and SETI projects. In addition to showing how the present
framework is adaptable to more complex situations and updated observational
databases from current and near-future space missions, we demonstrate how
numerical results could offer a cautious rationale for continuation of
practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo
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