The effect of phosphodiesterase-5 inhibitors on cerebral blood flow in humans: A systematic review.

Agents that augment cerebral blood flow (CBF) could be potential treatments for vascular cognitive impairment. Phosphodiesterase-5 inhibitors are vasodilating drugs established in the treatment of erectile dysfunction (ED) and pulmonary hypertension. We reviewed published data on the effects of phosphodiesterase-5 inhibitors on CBF in adult humans. A systematic review according to PRISMA guidelines was performed. Embase, Medline and Cochrane Library Trials databases were searched. Sixteen studies with 353 participants in total were retrieved. Studies included healthy volunteers and patients with migraine, ED, type 2 diabetes, stroke, pulmonary hypertension, Becker muscular dystrophy and subarachnoid haemorrhage. Most studies used middle cerebral artery flow velocity to estimate CBF. Few studies employed direct measurements of tissue perfusion. Resting CBF velocity was unaffected by phosphodiesterase-5 inhibitors, but cerebrovascular regulation was improved in ED, pulmonary hypertension, diabetes, Becker's and a group of healthy volunteers. This evidence suggests that phosphodiesterase-5 inhibitors improve responsiveness of the cerebral vasculature, particularly in disease states associated with an impaired endothelial dilatory response. This supports the potential therapeutic use of phosphodiesterase-5 inhibitors in vascular cognitive impairment where CBF is reduced. Further studies with better resolution of deep CBF are warranted. The review is registered on the PROSPERO database (registration number CRD42016029668)

Q methodology and a Delphi poll: a useful approach to researching a narrative approach to therapy

Q methodology and a Delphi poll combined qualitative and quantitative methods to explore definitions of White and Epston's (1990) narrative approach to therapy among a group of UK practitioners. A Delphi poll was used to generate statements about narrative therapy. The piloting of statements by the Delphi panel identified agreement about theoretical ideas underpinning narrative therapy and certain key practices. A wider group of practitioners ranked the statements in a Q sort and made qualitative comments about their sorting. Quantitative methods (principal components analysis) were used to extract eight accounts of narrative therapy, five of which are qualitatively analysed in this paper. Agreement and differences were identified across a range of issues, including the social construction of narratives, privileging a political stance or narrative techniques and the relationship with other therapies, specifically systemic psychotherapy. Q methodology, combined with the Delphi poll, was a unique and innovative feature of this study

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Protective role of vitamin B6 (PLP) against DNA damage in Drosophila models of type 2 diabetes

Growing evidence shows that improper intake of vitamin B6 increases cancer risk and several studies indicate that diabetic patients have a higher risk of developing tumors. We previously demonstrated that in Drosophila the deficiency of Pyridoxal 5' phosphate (PLP), the active form of vitamin B6, causes chromosome aberrations (CABs), one of cancer prerequisites, and increases hemolymph glucose content. Starting from these data we asked if it was possible to provide a link between the aforementioned studies. Thus, we tested the effect of low PLP levels on DNA integrity in diabetic cells. To this aim we generated two Drosophila models of type 2 diabetes, the first by impairing insulin signaling and the second by rearing flies in high sugar diet. We showed that glucose treatment induced CABs in diabetic individuals but not in controls. More interestingly, PLP deficiency caused high frequencies of CABs in both diabetic models demonstrating that hyperglycemia, combined to reduced PLP level, impairs DNA integrity. PLP-depleted diabetic cells accumulated Advanced Glycation End products (AGEs) that largely contribute to CABs as α-lipoic acid, an AGE inhibitor, rescued not only AGEs but also CABs. These data, extrapolated to humans, indicate that low PLP levels, impacting on DNA integrity, may be considered one of the possible links between diabetes and cancer

Interaction between alcohol dehydrogenase II gene, alcohol consumption, and risk for breast cancer

MaeIII Restriction Fragment Length Polymorphism in exon 3 of the alcohol dehydrogenase II was assessed in serum from 467 randomly selected German women and 278 women with invasive breast cancer to evaluate the interaction between a polymorphism of the alcohol dehydrogenase II gene, alcohol consumption and risk for breast cancer. In both groups, usual consumption of different alcoholic beverages was asked for using semiquantitative food frequency questionnaires. We used multivariable logistic regression to separately estimate the association between alcohol consumption and alcohol dehydrogenase II polymorphism in the population sample and women with breast cancer. The alcohol dehydrogenase II polymorphism was detected in 14 women from the population sample (3.0%) and in 27 women with invasive breast cancer (9.7%). Frequency of alcohol consumption was independent of the genotype in the population sample. In women with breast cancer, there was a significant inverse association between the alcohol dehydrogenase II polymorphism and frequency of alcohol consumption (adjusted case-only odds ratio over increasing frequency of alcohol consumption=0.5; P for interaction=0.02). We observed a gene-environment interaction between the alcohol dehydrogenase II polymorphism, alcohol consumption, and risk for breast cancer. Breast cancer risk associated with alcohol consumption may vary according to the alcohol dehydrogenase II polymorphism, probably due to differences in alcohol metabolism

Psychophysics with children: Investigating the effects of attentional lapses on threshold estimates

When assessing the perceptual abilities of children, researchers tend to use psychophysical techniques designed for use with adults. However, children’s poorer attentiveness might bias the threshold estimates obtained by these methods. Here, we obtained speed discrimination threshold estimates in 6- to 7-year-old children in UK Key Stage 1 (KS1), 7- to 9-year-old children in Key Stage 2 (KS2), and adults using three psychophysical procedures: QUEST, a 1-up 2-down Levitt staircase, and Method of Constant Stimuli (MCS). We estimated inattentiveness using responses to “easy” catch trials. As expected, children had higher threshold estimates and made more errors on catch trials than adults. Lower threshold estimates were obtained from psychometric functions fit to the data in the QUEST condition than the MCS and Levitt staircases, and the threshold estimates obtained when fitting a psychometric function to the QUEST data were also lower than when using the QUEST mode. This suggests that threshold estimates cannot be compared directly across methods. Differences between the procedures did not vary significantly with age group. Simulations indicated that inattentiveness biased threshold estimates particularly when threshold estimates were computed as the QUEST mode or the average of staircase reversals. In contrast, thresholds estimated by post-hoc psychometric function fitting were less biased by attentional lapses. Our results suggest that some psychophysical methods are more robust to attentiveness, which has important implications for assessing the perception of children and clinical groups

Towards an integrated model for breast cancer etiology: The lifelong interplay of genes, lifestyle, and hormones

While the association of a number of risk factors, such as family history and reproductive patterns, with breast cancer has been well established for many years, work in the past 10–15 years also has added substantially to our understanding of disease etiology. Contributions of particular note include the delineation of the role of endogenous and exogenous estrogens to breast cancer risk, and the discovery and quantification of risk associated with several gene mutations (e.g. BRCA1). Although it is difficult to integrate all epidemiologic data into a single biologic model, it is clear that several important components or pathways exist. Early life events probably determine both the number of susceptible breast cells at risk and whether mutations occur in these cells. High endogenous estrogens are well established as an important cause of breast cancer, and many known risk factors appear to operate through this pathway. Estrogens (and probably other growth factors) appear to accelerate the development of breast cancer at many points along the progression from early mutation to tumor metastasis, and appear to be influential at many points in a woman's life. These data now provide a basis for a number of strategies that can reduce risk of breast cancer, although some strategies represent complex decision-making. Together, the modification of nutritional and lifestyle risk factors and the judicious use of chemopreventive agents could have a major impact on breast cancer incidence. Further research is needed in many areas, but a few specific arenas are given particular mention

Korarchaeota Diversity, Biogeography, and Abundance in Yellowstone and Great Basin Hot Springs and Ecological Niche Modeling Based on Machine Learning

Over 100 hot spring sediment samples were collected from 28 sites in 12 areas/regions, while recording as many coincident geochemical properties as feasible (>60 analytes). PCR was used to screen samples for Korarchaeota 16S rRNA genes. Over 500 Korarchaeota 16S rRNA genes were screened by RFLP analysis and 90 were sequenced, resulting in identification of novel Korarchaeota phylotypes and exclusive geographical variants. Korarchaeota diversity was low, as in other terrestrial geothermal systems, suggesting a marine origin for Korarchaeota with subsequent niche-invasion into terrestrial systems. Korarchaeota endemism is consistent with endemism of other terrestrial thermophiles and supports the existence of dispersal barriers. Korarchaeota were found predominantly in >55°C springs at pH 4.7–8.5 at concentrations up to 6.6×106 16S rRNA gene copies g−1 wet sediment. In Yellowstone National Park (YNP), Korarchaeota were most abundant in springs with a pH range of 5.7 to 7.0. High sulfate concentrations suggest these fluids are influenced by contributions from hydrothermal vapors that may be neutralized to some extent by mixing with water from deep geothermal sources or meteoric water. In the Great Basin (GB), Korarchaeota were most abundant at spring sources of pH<7.2 with high particulate C content and high alkalinity, which are likely to be buffered by the carbonic acid system. It is therefore likely that at least two different geological mechanisms in YNP and GB springs create the neutral to mildly acidic pH that is optimal for Korarchaeota. A classification support vector machine (C-SVM) trained on single analytes, two analyte combinations, or vectors from non-metric multidimensional scaling models was able to predict springs as Korarchaeota-optimal or sub-optimal habitats with accuracies up to 95%. To our knowledge, this is the most extensive analysis of the geochemical habitat of any high-level microbial taxon and the first application of a C-SVM to microbial ecology