Gaseous Mediators and Mitochondrial Function: The Future of Pharmacologically Induced Suspended Animation?

The role of nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S) as poisonous gases is well-established. However, they are not only endogenously produced but also, at low concentrations, exert beneficial effects, such as anti-inflammation, and cytoprotection. This knowledge initiated the ongoing debate, as to whether these molecules, also referred to as “gaseous mediators” or “gasotransmitters,” could serve as novel therapeutic agents. In this context, it is noteworthy, that all gasotransmitters specifically target the mitochondria, and that this interaction may modulate mitochondrial bioenergetics, thereby subsequently affecting metabolic function. This feature is of crucial interest for the possible induction of “suspended animation.” Suspended animation, similar to mammalian hibernation (and/or estivation), refers to an externally induced hypometabolic state, with the intention to preserve organ function in order to survive otherwise life-threatening conditions. This hypometabolic state is usually linked to therapeutic hypothermia, which, however, comes along with adverse effects (e.g., coagulopathy, impaired host defense). Therefore, inducing an on-demand hypometabolic state by directly lowering the energy metabolism would be an attractive alternative. Theoretically, gasotransmitters should reversibly interact and inhibit the mitochondrial respiratory chain during pharmacologically induced suspended animation. However, it has to be kept in mind that this effect also bears the risk of cytotoxicity resulting from the blockade of the mitochondrial respiratory chain. Therefore, this review summarizes the current knowledge of the impact of gasotransmitters on modulating mitochondrial function. Further, we will discuss their role as potential candidates in inducing a suspended animation

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the crossvalidation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as knowledge engines'' in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality-especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.</p

Noninvasive Ventilation in Preterm Infants: Factors Influencing Weaning Decisions and the Role of the Silverman-Andersen Score

The factors influencing weaning of preterm infants from noninvasive ventilation (NIV) are poorly defined and the weaning decisions are often driven by subjective judgement rather than objective measures. To standardize quantification of respiratory effort, the Silverman-Andersen Score (SAS) was included in our nursing routine. We investigated the factors that steer the weaning process and whether the inclusion of the SAS would lead to more stringent weaning. Following SAS implementation, we prospectively evaluated 33 neonates born &le; 32 + 0 weeks gestational age. Age-, weight- and sex-matched infants born before routine SAS evaluation served as historic control. In 173 of 575 patient days, NIV was not weaned despite little respiratory distress (SAS &le; 2), mainly due to bradycardias (60% of days without weaning), occurring alone (40%) or in combination with other factors such as apnea/desaturations. In addition, &ldquo;soft factors&rdquo; that are harder to grasp impact on weaning decisions, whereas the SAS overall played a minor role. Consequently, ventilation times did not differ between the groups. In conclusion, NIV weaning is influenced by various factors that override the absence of respiratory distress limiting the predictive value of the SAS. An awareness of the factors that influence weaning decisions is important as prolonged use of NIV has been associated with adverse outcome. Guidelines are necessary to standardize NIV weaning practice