Biology subjects

Silvestri, S.

Publications and source records attributed to Silvestri, S..

4 recordsLinked to original sources

bioRxiv2024

Generation length of the world's amphibians and reptiles

Variation in life histories influences demographic processes from adaptive changes to population declines leading to extinction. Among life history traits, generation length offers a critical feature to forecast species demographic trajectories such as population declines (widely used by the IUCN Red List of Threatened Species) and adaptability to environmental change over time. Therefore, estimates of generation length are crucial to monitor demographic stability or future change in highly threatened organisms, particularly ectothermic tetrapods (amphibians and reptiles) - which rank among the most threatened groups - but for which uncertainty in future impacts remains high. Despite its importance, generation length for amphibians and reptiles is largely missing. Here, we aimed to fill-in this gap by modeling generation lengths for amphibians, squamates and testudines as a function of species size, climate, life history, and phylogeny using generalized additive models and phylogenetic generalized least squares. We obtained estimates of generation lengths for 4,543 (52%) amphibians, 8,464 (72%) squamates and 118 (32%) testudines. Our models performed well for most families, for example Bufonidae in amphibians, Lacertidae and Colubridae in squamates and Geoemydidae in testudines, while we found high uncertainty around the prediction of a few families, notably Chamaeleonidae. Species body size and mean temperature were the main predictors of generation length in all groups. Although our estimates are not meant to substitute robust and validated measurements from field studies or natural history museums, they can help reduce existing biases in conservation assessments until field data will be comprehensively available.

zoology↗

bioRxiv2022

SiCoDEA: a simple, fast and complete app for analyzing the effect of individual drugs and their combinations

The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of the single drugs. For these reasons, it is of interest to study combinations of drugs and in particular to determine whether a specific combination has a synergistic, antagonistic or additive effect, i.e greater, less than or equal to the effect expected by the sum of the individual drugs. For this purpose, various mathematical models have been developed, which use different methods to evaluate the synergy of a combination of drugs. Most of these methods are based on the Loewe Additivity Principle (Loewe et al., 1953), which has its key step in choosing the model used for predicting the effect of individual drugs. Creating a model for this purpose and calculating its parameters, however, requires a certain level of mathematical and programming knowledge or the use of commercial software. For this purpose, therefore, we have developed an open access and easy to use app that allows to explore different models and to choose the most fitting for the specific experimental data: SiCoDEA (Single and Combined Drug Effect Analysis, https://sicodea.shinyapps.io/shiny/). The data used to test SiCoDEA comes from cell line samples treated with different drug combinations and analyzed through metabolic or viability assays. SiCoDEA is developed through a Shiny interactive and easy-to-use interface (R based). There are five models taken into consideration for the analysis of single drugs and the calculation of combination index. The first is one of the most used, that of the median-effect; while the others are different forms of the log-logistic equation, with two, three and four parameters. The purpose of SiCoDEA is, on the one hand, to provide an easy-to-use tool for analyzing drug combination data and, on the other hand, also to have a view of the various steps and to offer different results based on the model chosen. An important prerequisite in analyzing drug combinations is in fact the dose-response curve calculated for individual drugs. For this purpose, SiCoDEA allows you to view the plots of the individual drugs both to evaluate the distribution of the calculated points and therefore identify any outliers, and to view the curve of the different models taken into consideration and evaluate which one best fits the data. A table showing all the R2 values for the five different models is created with the curve plot. In addition to the type of model, it is also possible to choose between two different normalization methods, one based on the maximum or minimum value and the other on the value calculated at drug concentrations equal to zero. For the chosen options, a plot is then created that shows the trend of the combination index for the different drug combinations and consequently whether it is synergy, antagonism or additivity. Finally, it is possible to export the results in single png files or in a summary report in pdf. SiCoDEA is an open-source app among the most complete and offers more functions even than the famous CompuSyn (Chou et al., 2010), as it allows you to analyze drug curves with different models, rather than just one, and it also allows the analysis of single drug curves.

bioinformatics↗

bioRxiv2021

rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

O_LIEcosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. C_LIO_LIIn this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. C_LIO_LIThe rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms. C_LI

ecology↗

bioRxiv2019

Palmitate-induced toxicity is associated with impaired mitochondrial respiration and accelerated oxidative stress in cultured cardiomyocytes: the critical role of Coenzyme Q9/10

Impaired mitochondrial function concomitant to enhanced oxidative stress-induced damage are well established mechanisms involved in hyperlipidemia-induced cardiotoxicity. Coenzyme Q9/10 (CoQ) is known to be a critical component of the mitochondrial electron transport chain that efficiently supports the process of bioenergetics in addition to its antioxidant activities. However, there is very limited information on the direct effect of myocardial lipid overload on endogenous CoQ levels in association with mitochondrial respiration and oxidative stress status. Here, such effects were explored by exposing H9c2 cardiomyocytes to various doses (0.15 to 1 mM) of palmitate for 24 hours. The results demonstrated that palmitate doses [≥] 0.25 mM are enough to impair mitochondrial respiration and cause oxidative stress. Although endogenous CoQ levels are enhanced by palmitate doses [≤] 5 mM, this is not enough to counteract oxidative stress, but is sufficient to maintain cell viability of cardiomyocytes, suggesting a compensation mechanism. Palmitate doses > 5 mM caused severe mitochondrial toxicity, including reduction of cell viability. Interestingly, enhancement of CoQ levels with the lowest dose of palmitate (0.15 mM) was accompanied by a significantly reduction of CoQ oxidation status, as well as low cytosolic production of reactive oxygen species. From the overall findings, it appears that CoQ response may be crucial to improve mitochondrial function and thus protect against hyperlipidemia-induced insult. These results further suggest that therapeutic agents that can stimulate endogenous levels of CoQ may be beneficial in protecting the myocardium against diabetes associated complications.

biochemistry↗