Search

Remote ischemic conditioning (RIC) is a promising therapeutic approach for ischemic stroke patients. It has been proven that RIC reduces infarct size and improves functional outcomes. RIC can be applied either before ischemia (pre-conditioning; RIPreC), during ischemia (per-conditioning; RIPerC) or after ischemia (post-conditioning; RIPostC). Our aim was to systematically determine the efficacy of RIC in reducing infarct volumes and define the cellular pathways involved in preclinical animal models of ischemic stroke. A systematic search in three databases yielded 50 peer-review articles. Data were analyzed using random effects models and results expressed as percentage of reduction in infarct size (95% CI). A meta-regression was also performed to evaluate the effects of covariates on the pooled effect-size. 95.3% of analyzed experiments were carried out in rodents. Thirty-nine out of the 64 experiments studied RIPostC (61%), sixteen examined RIPreC (25%) and nine tested RIPerC (14%). In all studies, RIC was shown to reduce infarct volume (− 38.36%; CI − 42.09 to − 34.62%) when compared to controls. There was a significant interaction caused by species. Short cycles in mice significantly reduces infarct volume while in rats the opposite occurs. RIPreC was shown to be the most effective strategy in mice. The present meta-analysis suggests that RIC is more efficient in transient ischemia, using a smaller number of RIC cycles, applying larger length of limb occlusion, and employing barbiturates anesthetics. There is a preclinical evidence for RIC, it is safe and effective. However, the exact cellular pathways and underlying mechanisms are still not fully determined, and its definition will be crucial for the understanding of RIC mechanism of action. [ABSTRACT FROM AUTHOR]

Introduction: Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered.Objective: To develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease.Methods: We explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer.Results: The evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay.Conclusion: Problems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface. [ABSTRACT FROM AUTHOR]

In this study, we studied the genetic diversity and population genetic structure of the endangered endemic Crepis granatensis, using amplified fragments length polymorphism (AFLP) and plastid DNA (cpDNA). No genetic divergences were obtained using cpDNA markers. Three primers combinations selected from a total of 12 produced a total of 421 fragments, of which 418 (99.3%) were polymorphic. The total genetic diversity of C. granatensis was moderate (Ht = 0.260). Neís gene diversity ranged from 0.202 to 0.258. The fixation index (Fst) was 0.137, suggesting low to moderate genetic differentiation among populations. The AMOVA analysis revealed that genetic diversity was mainly concentrated among individuals within populations (74%), while 8% was found among populations and 18% among regions. The Bayesian analysis and PCoA identified two genetic clusters: one corresponded to La Sagra population and the other corresponded to the Mágina populations. Based on our genetic results, it is necessary to preserve the evolutionary potential of C. granatensis by protecting all extant populations. Both in situ and ex-situ conservation measures should be considered. Reinforcement, reintroduction, and translocation programmes could be performed if necessary. Finally, such conservation strategies should be considered both in the current recovery plan and management actions for the species. [ABSTRACT FROM AUTHOR]

ITGAM–ITGAX (rs11150612, rs11574637), VAV3 rs17019602, CARD9 rs4077515, DEFA (rs2738048, rs10086568), and HORMAD2 rs2412971 are mucosal immune defence polymorphisms, that have an impact on IgA production, described as risk loci for IgA nephropathy (IgAN). Since IgAN and Immunoglobulin-A vasculitis (IgAV) share molecular mechanisms, with the aberrant deposit of IgA1 being the main pathophysiologic feature of both entities, we assessed the potential influence of the seven abovementioned polymorphisms on IgAV pathogenesis. These seven variants were genotyped in 381 Caucasian IgAV patients and 997 matched healthy controls. No statistically significant differences were observed in the genotype and allele frequencies of these seven polymorphisms when the whole cohort of IgAV patients and those with nephritis were compared to controls. Similar genotype and allele frequencies of all polymorphisms were disclosed when IgAV patients were stratified according to the age at disease onset or the presence/absence of gastrointestinal or renal manifestations. Likewise, no ITGAM–ITGAX and DEFA haplotype differences were observed when the whole cohort of IgAV patients, along with those with nephritis and controls, as well as IgAV patients, stratified according to the abovementioned clinical characteristics, were compared. Our results suggest that mucosal immune defence polymorphisms do not represent novel genetic risk factors for IgAV pathogenesis. [ABSTRACT FROM AUTHOR]

OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC. [ABSTRACT FROM AUTHOR]