DNA multigene characterization of Fasciola hepatica and Lymnaea neotropica and its fascioliasis transmission capacity in Uruguay, with historical correlation, human report review and infection risk analysis

Fascioliasis is a highly pathogenic zoonotic disease emerging in recent decades, in part due to the effects of climate and global changes. South America is the continent presenting more numerous human fascioliasis endemic areas and the highest Fasciola hepatica infection prevalences and intensities known in humans. These serious public health scenarios appear mainly linked to altitude areas in Andean countries, whereas lowland areas of non-Andean countries, such as Uruguay, only show sporadic human cases or outbreaks. To understand this difference, we characterized F. hepatica from cattle and horses and lymnaeids of Uruguay by sequencing of ribosomal DNA ITS-2 and ITS-1 spacers and mitochondrial DNA cox1, nad1 and 16S genes. Results indicate that vectors belong to Lymnaea neotropica instead of to Lymnaea viator, as always reported from Uruguay. Our correlation of fasciolid and lymnaeid haplotypes with historical data on the introduction and spread of livestock species into Uruguay allow to understand the molecular diversity detected. We study the life cycle and transmission features of F. hepatica by L. neotropica of Uruguay under standardized experimental conditions to enable a comparison with the transmission capacity of F. hepatica by Galba truncatula at very high altitude in Bolivia. Results demonstrate that although L. neotropica is a highly efficient vector in the lowlands, its transmission capacity is markedly lower than that of G. truncatula in the highlands. On this baseline, we review the human fascioliasis cases reported in Uruguay and analyze the present and future risk of human infection in front of future climate change estimations

The role of hypothalamic H1 receptor antagonism in antipsychotic-induced weight gain

Treatment with second generation antipsychotics (SGAs), notably olanzapine and clozapine, causes severe obesity side effects. Antagonism of histamine H1 receptors has been identified as a main cause of SGA-induced obesity, but the molecular mechanisms associated with this antagonism in different stages of SGA-induced weight gain remain unclear. This review aims to explore the potential role of hypothalamic histamine H1 receptors in different stages of SGA-induced weight gain/obesity and the molecular pathways related to SGA-induced antagonism of these receptors. Initial data have demonstrated the importance of hypothalamic H1 receptors in both short- and long-term SGA-induced obesity. Blocking hypothalamic H1 receptors by SGAs activates AMP-activated protein kinase (AMPK), a well-known feeding regulator. During short-term treatment, hypothalamic H1 receptor antagonism by SGAs may activate the AMPK—carnitine palmitoyltransferase 1 signaling to rapidly increase caloric intake and result in weight gain. During long-term SGA treatment, hypothalamic H1 receptor antagonism can reduce thermogenesis, possibly by inhibiting the sympathetic outflows to the brainstem rostral raphe pallidus and rostral ventrolateral medulla, therefore decreasing brown adipose tissue thermogenesis. Additionally, blocking of hypothalamic H1 receptors by SGAs may also contribute to fat accumulation by decreasing lipolysis but increasing lipogenesis in white adipose tissue. In summary, antagonism of hypothalamic H1 receptors by SGAs may time-dependently affect the hypothalamus-brainstem circuits to cause weight gain by stimulating appetite and fat accumulation but reducing energy expenditure. The H1 receptor and its downstream signaling molecules could be valuable targets for the design of new compounds for treating SGA-induced weight gain/obesity

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Search for B-s(0) - GT mu(+)mu(-) and B-0 - GT mu(+)mu(-) Decays in pp Collisions at root s=7 TeV

A search for the rare decays B-s(0) -- GT mu(+)mu(-) and B-0 -- GT mu(+)mu(-) is performed in pp collisions at root s = 7 TeV, with a data sample corresponding to an integrated luminosity of 1.14 fb(-1), collected by the CMS experiment at the LHC. In both cases, the number of events observed after all selection requirements is consistent with expectations from background and standard-model signal predictions. The resulting upper limits on the branching fractions are B(B-s(0) -- GT mu(+)mu(-)) LT 1.9 x 10(-8) and B(B-0 -- GT mu(+)mu(-)) LT 4.6 x 10(-9), at 95% confidence level