No effect of the angiotensin receptor blocker candesartan on cerebrovascular autoregulation in rats during very high and low sodium intake

Autoregulation of cerebral blood flow (CBF) denotes that CBF is constant despite fluctuation of blood pressure within wide limits. Inhibition of the renin–angiotensin system (RAS) is known to decrease the lower and upper limits of CBF autoregulation. We have previously shown that this includes inhibition by the angiotensin receptor blocker (ARB) candesartan. In the present study we investigated the influence of the ARB candesartan on the lower limit of CBF autoregulation in two groups of Sprague-Dawley rats, on high (4.0% Na+) and low (0.004% Na+) sodium diet, respectively. Control animals were given the same diet, but no ARB. CBF was studied with the laser Doppler method. Blood pressure was lowered by controlled bleeding. Results revealed that both high and low sodium diet with low and high renin levels respectively block the influence of candesartan on CBF autoregulation. This was expected in rats on a high salt diet with a low renin level, but unexpected in rats with a low salt intake with a high renin level.</p

Drive counts as a method of estimating ungulate density in forests: mission impossible?

Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of ‘virtual experiments’ in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size

Change in albuminuria as a surrogate endpoint for progression of kidney disease: a meta-analysis of treatment effects in randomised clinical trials

Background Change in albuminuria has strong biological plausibility as a surrogate endpoint for progression of chronic kidney disease, but empirical evidence to support its validity is lacking. We aimed to determine the association between treatment effects on early changes in albuminuria and treatment effects on clinical endpoints and surrograte endpoints, to inform the use of albuminuria as a surrogate endpoint in future randomised controlled trials. Methods In this meta-analysis, we searched PubMed for publications in English from Jan 1, 1946, to Dec 15, 2016, using search terms including “chronic kidney disease”, “chronic renal insufficiency”, “albuminuria”, “proteinuria”, and “randomized controlled trial”; key inclusion criteria were quantifiable measurements of albuminuria or proteinuria at baseline and within 12 months of follow-up and information on the incidence of end-stage kidney disease. We requested use of individual patient data from the authors of eligible studies. For all studies that the authors agreed to participate and that had sufficient data, we estimated treatment effects on 6-month change in albuminuria and the composite clinical endpoint of treated end-stage kidney disease, estimated glomerular filtration rate of less than 15 mL/min per 1·73 m2, or doubling of serum creatinine. We used a Bayesian mixed-effects meta-regression analysis to relate the treatment effects on albuminuria to those on the clinical endpoint across studies and developed a prediction model for the treatment effect on the clinical endpoint on the basis of the treatment effect on albuminuria. Findings We identified 41 eligible treatment comparisons from randomised trials (referred to as studies) that provided sufficient patient-level data on 29 979 participants (21 206 [71%] with diabetes). Over a median follow-up of 3·4 years (IQR 2·3–4·2), 3935 (13%) participants reached the composite clinical endpoint. Across all studies, with a meta-regression slope of 0·89 (95% Bayesian credible interval [BCI] 0·13–1·70), each 30% decrease in geometric mean albuminuria by the treatment relative to the control was associated with an average 27% lower hazard for the clinical endpoint (95% BCI 5–45%; median R2 0·47, 95% BCI 0·02–0·96). The association strengthened after restricting analyses to patients with baseline albuminuria of more than 30 mg/g (ie, 3·4 mg/mmol; R2 0·72, 0·05–0·99]). For future trials, the model predicts that treatments that decrease the geometric mean albuminuria to 0·7 (ie, 30% decrease in albuminuria) relative to the control will provide an average hazard ratio (HR) for the clinical endpoint of 0·68, and 95% of sufficiently large studies would have HRs between 0·47 and 0·95. Interpretation Our results support a role for change in albuminuria as a surrogate endpoint for the progression of chronic kidney disease, particularly in patients with high baseline albuminuria; for patients with low baseline levels of albuminuria this association is less certain

DNA-Sequence Variation Among Schistosoma mekongi Populations and Related Taxa; Phylogeography and the Current Distribution of Asian Schistosomiasis

Schistosomiasis is a disease caused by parasitic worms of the genus Schistosoma. In the lower Mekong river, schistosomiasis in humans is called Mekong schistosomiasis and is caused by Schistosoma mekongi. In the past, Mekong schistosomiasis was known only from the lower Mekong river. Here DNA-sequence variation is used to study the relationships and history of populations of S. mekongi. Populations from other rivers are compared and shown to be S. mekongi, thus confirming that this species is not restricted to only a small section of one river. The dates of divergence among populations are also estimated. Prior to this study it was assumed that S. mekongi originated in Yunnan, China, migrated southwards across Laos and into Cambodia, later becoming extinct in Laos (due to conditions unsuitable for transmission). In contrast, the dates estimated here indicate that S. mekongi entered Cambodia from Vietnam, 2.5–1 Ma. The pattern of genetic variation fits better with a more recent, and ongoing, northwards migration from Cambodia into Laos. The implications are that Mekong schistosomiasis is more widespread than once thought and that the human population at risk is up to 10 times greater than originally estimated. There is also an increased possibility of the spread of Mekong schistosomiasis across Laos

Pathogenetic mechanisms in vascular dementia

Vascular dementia accounts for approximately 20% of all cases of dementia and for about 50% in subjects over 80 years. Thromboembolism with multiple cerebral infarcts was considered to be almost the only pathogenetic pathway of vascular dementia, with multi-infarct dementia as its clinical manifestation. However, there is a great heterogeneity of vascular dementia syndromes and pathological subtypes, as documented by the number of pathogenetic mechanisms now known to underlie the clinical picture. They include thromboembolism and extracerebral and cerebral factors. Among the extracerebral factors are ischemic hypoxic dementia (i.e., dementia due to hypoperfusion), vasculitis, hyperviscosity and abnormalities of hemostasis. Among the cerebral factors are lipohyalinosis, cerebral amyloid angiopathy, disruption of the blood-brain barrier and altered regulation of cerebral blood flow. Therefore, the approach to vascular dementia should take the heterogeneity into account. In this context, the importance of non-infarct type should be considered; subcortical white matter disorder seems to be a noteworthy common pathway of vascular dementia produced by various vascular mechanisms. Finally, the heterogeneity of the vascular mechanisms involved in vascular dementia-namely hypoperfusion-might be a factor that can be positively influenced by targeted therapeutic intervention

Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms

Computational discovery of organometallic catalysts that effectively catalyze nitrogen fixation is a difficult task. The complexity of the chemical reactions involved and the lack of understanding of natures enzyme catalysts raises the need for intricate computational models. In this study, we use a dataset of 91 experimentally verified ligands as starting population for a Genetic Algorithm (GA) and use this to discover molybdenum based nitrogen fixation catalyst in trigonal bipyramidal and octahedral configurations. Through evolutionary discovery with a semi-empirical quantum method driven GA and a density functional theory (DFT) based screening process, we find 3 promising catalyst candidates that are shown to effectively catalyze the first protonation step of the Schrock cycle. Synthetic accessibility (SA) scores are used to guide the GA towards reasonable ligands and the work features a description of the GA framework, including pre-screening of catalyst candidates that involves assignment of metal coordination atoms and catalyst stereoisomers. This research thus not only offers insights into the specific field of molybdenum-based catalysts for nitrogen fixation but also demonstrates the broader applicability and potential of genetic algorithms in the field of catalyst discovery and materials science.</p