State of the Art: Why do the lungs of patients with cystic fibrosis become infected and why can't they clear the infection?

Cystic Fibrosis (CF) lung disease, which is characterized by airway obstruction, chronic bacterial infection, and an excessive inflammatory response, is responsible for most of the morbidity and mortality. Early in life, CF patients become infected with a limited spectrum of bacteria, especially P. aeruginosa. New data now indicate that decreased depth of periciliary fluid and abnormal hydration of mucus, which impede mucociliary clearance, contribute to initial infection. Diminished production of the antibacterial molecule nitric oxide, increased bacterial binding sites (e.g., asialo GM-1) on CF airway epithelial cells, and adaptations made by the bacteria to the airway microenvironment, including the production of virulence factors and the ability to organize into a biofilm, contribute to susceptibility to initial bacterial infection. Once the patient is infected, an overzealous inflammatory response in the CF lung likely contributes to the host's inability to eradicate infection. In response to increased IL-8 and leukotriene B(4 )production, neutrophils infiltrate the lung where they release mediators, such as elastase, that further inhibit host defenses, cripple opsonophagocytosis, impair mucociliary clearance, and damage airway wall architecture. The combination of these events favors the persistence of bacteria in the airway. Until a cure is discovered, further investigations into therapies that relieve obstruction, control infection, and attenuate inflammation offer the best hope of limiting damage to host tissues and prolonging survival

Functional dissection of the R domain of cystic fibrosis transmembrane conductance regulator1The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked `advertisement' in accordance.1

AbstractExogenously expressed unphosphorylated sub-domains of the R domain block CFTR Cl− channels in the planar lipid bilayer, though the block differs from block with full length R domain. Full length R domain peptide (aa 588–855) blocks CFTR Cl− channels quickly, completely and permanently [1]. Two sub-domains, RD1RD2 (aa 588–805) and RD2TM (aa 672–855), also inhibit CFTR Cl− channels, but the block takes longer to effect and is not complete. Shorter sequences, RD1 (aa 588–746) and RD2 (aa 672–805), fail to effect any block. These data suggest that either the amino-terminal or carboxy-terminal portions of the R domain protein or its stabilized secondary structure are critical to functional regulation

Decaying Dark Matter can explain the electron/positron excesses

PAMELA and ATIC recently reported excesses in e+ e- cosmic rays. Since the interpretation in terms of DM annihilations was found to be not easily compatible with constraints from photon observations, we consider the DM decay hypothesis and find that it can explain the e+ e- excesses compatibly with all constraints, and can be tested by dedicated HESS observations of the Galactic Ridge. ATIC data indicate a DM mass of about 2 TeV: this mass naturally implies the observed DM abundance relative to ordinary matter if DM is a quasi-stable composite particle with a baryon-like matter asymmetry. Technicolor naturally yields these type of candidates.Comment: 20 pages, 7 figure

The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network

Background: Up to 30% of acute stroke evaluations are deemed stroke mimics (SM). As telestroke consultation expands across the world, increasing numbers of SM patients are likely being evaluated via Telestroke. We developed a model to prospectively identify ischemic SMs during Telestroke evaluation. Methods and Results: We analyzed 829 consecutive patients from January 2004 to April 2013 in our internal New England–based Partners TeleStroke Network for a derivation cohort, and 332 cases for internal validation. External validation was performed on 226 cases from January 2008 to August 2012 in the Partners National TeleStroke Network. A predictive score was developed using stepwise logistic regression, and its performance was assessed using receiver‐operating characteristic (ROC) curve analysis. There were 23% SM in the derivation, 24% in the internal, and 22% in external validation cohorts based on final clinical diagnosis. Compared to those with ischemic cerebrovascular disease (iCVD), SM had lower mean age, fewer vascular risk factors, more frequent prior seizure, and a different profile of presenting symptoms. The TeleStroke Mimic Score (TM‐Score) was based on factors independently associated with SM status including age, medical history (atrial fibrillation, hypertension, seizures), facial weakness, and National Institutes of Health Stroke Scale >14. The TM‐Score performed well on ROC curve analysis (derivation cohort AUC=0.75, internal validation AUC=0.71, external validation AUC=0.77). Conclusions: SMs differ substantially from their iCVD counterparts in their vascular risk profiles and other characteristics. Decision‐support tools based on predictive models, such as our TM Score, may help clinicians consider alternate diagnosis and potentially detect SMs during complex, time‐critical telestroke evaluations

Old Plants, New Tricks:Phenological Research Using Herbarium Specimens

The timing of phenological events, such as leaf-out and flowering, strongly influence plant success and their study is vital to understanding how plants will respond to climate change. Phenological research, however, is often limited by the temporal, geographic, or phylogenetic scope of available data. Hundreds of millions of plant specimens in herbaria worldwide offer a potential solution to this problem, especially as digitization efforts drastically improve access to collections. Herbarium specimens represent snapshots of phenological events and have been reliably used to characterize phenological responses to climate. We review the current state of herbarium-based phenological research, identify potential biases and limitations in the collection, digitization, and interpretation of specimen data, and discuss future opportunities for phenological investigations using herbarium specimens

Getting under the skin: children's health disparities as embodiment of social class

Social class gradients in children’s health and development are ubiquitous across time and geography. The authors develop a conceptual framework relating three actions of class—material allocation, salient group identity, and inter-group conflict—to the reproduction of class-based disparities in child health. A core proposition is that the actions of class stratification create variation in children’s mesosystems and microsystems in distinct locations in the ecology of everyday life. Variation in mesosystems (e.g., health care, neighborhoods) and microsystems (e.g., family structure, housing) become manifest in a wide variety of specific experiences and environments that produce the behavioral and biological antecedents to health and disease among children. The framework is explored via a review of theoretical and empirical contributions from multiple disciplines and high-priority areas for future research are highlighted

Computing and applying atomic regulons to understand gene expression and regulation

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.01819/full#supplementary-materialUnderstanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here we describe an approach for inferring ARs that leverages large-scale expression data sets gene context and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness CLR analysis finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms we computed ARs for Shewanella oneidensis Pseudomonas aeruginosa Thermus thermophilus and Staphylococcus aureus each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.JF acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. CH and PW were supported by the National Science Foundation under grant number EFRI-MIKS-1137089. RT was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), U.S. Department of Energy(DOE),and his work is a contribution of the Pacific North west National Laboratory (PNNL) Foundational Scientific Focus Area. This work was partially supported by an award from the National Science Foundation to MD, AB, NT, and RO (NSFABI-0850546). This work was also supported by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [Contract No. HHSN272201400027C]

Reliability of Rapid Diagnostic Tests in Diagnosing Pregnancy-Associated Malaria in North-Eastern Tanzania.

Accurate diagnosis and prompt treatment of pregnancy-associated malaria (PAM) are key aspects in averting adverse pregnancy outcomes. Microscopy is the gold standard in malaria diagnosis, but it has limited detection and availability. When used appropriately, rapid diagnostic tests (RDTs) could be an ideal diagnostic complement to microscopy, due to their ease of use and adequate sensitivity in detecting even sub-microscopic infections. Polymerase chain reaction (PCR) is even more sensitive, but it is mainly used for research purposes. The accuracy and reliability of RDTs in diagnosing PAM was evaluated using microscopy and PCR. A cohort of pregnant women in north-eastern Tanzania was followed throughout pregnancy for detection of plasmodial infection using venous and placental blood samples evaluated by histidine rich protein 2 (HRP-2) and parasite lactate dehydrogenase (pLDH) based RDTs (Parascreen™) or HRP-2 only (Paracheck Pf® and ParaHIT®f), microscopy and nested Plasmodium species diagnostic PCR. From a cohort of 924 pregnant women who completed the follow up, complete RDT and microscopy data was available for 5,555 blood samples and of these 442 samples were analysed by PCR. Of the 5,555 blood samples, 49 ((proportion and 95% confidence interval) 0.9% [0.7 -1.1]) samples were positive by microscopy and 91 (1.6% [1.3-2.0]) by RDT. Forty-six (50.5% [40.5 - 60.6]) and 45 (49.5% [39.4 - 59.5]) of the RDT positive samples were positive and negative by microscopy, respectively, whereas nineteen (42.2% [29.0 - 56.7]) of the microscopy negative, but RDT positive, samples were positive by PCR. Three (0.05% [0.02 - 0.2]) samples were positive by microscopy but negative by RDT. 351 of the 5,461 samples negative by both RDT and microscopy were tested by PCR and found negative. There was no statistically significant difference between the performances of the different RDTs. Microscopy underestimated the real burden of malaria during pregnancy and RDTs performed better than microscopy in diagnosing PAM. In areas where intermittent preventive treatment during pregnancy may be abandoned due to low and decreasing malaria risk and instead replaced with active case management, screening with RDT is likely to identify most infections in pregnant women and out-performs microscopy as a diagnostic tool

What Is a Representative Brain? Neuroscience Meets Population Science

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain–behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective—population neuroscience—that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas

Spectrum of Infection and Risk Factors for Human Monkeypox, United States, 2003

Infection is associated with proximity to virus-infected animals and their excretions and secretions