The impact of sanitation on infectious disease and nutritional status: A systematic review and meta-analysis.

BACKGROUND: Sanitation aims to sequester human feces and prevent exposure to fecal pathogens. More than 2.4 billion people worldwide lack access to improved sanitation facilities and almost one billion practice open defecation. We undertook systematic reviews and meta-analyses to compile the most recent evidence on the impact of sanitation on diarrhea, soil-transmitted helminth (STH) infections, trachoma, schistosomiasis, and nutritional status assessed using anthropometry. METHODS AND FINDINGS: We updated previously published reviews by following their search strategy and eligibility criteria. We searched from the previous review's end date to December 31, 2015. We conducted meta-analyses to estimate pooled measures of effect using random-effects models and conducted subgroup analyses to assess impact of different levels of sanitation services and to explore sources of heterogeneity. We assessed risk of bias and quality of the evidence from intervention studies using the Liverpool Quality Appraisal Tool (LQAT) and Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach, respectively. A total of 171 studies met the review's inclusion criteria, including 64 studies not included in the previous reviews. Overall, the evidence suggests that sanitation is protective against diarrhea, active trachoma, some STH infections, schistosomiasis, and height-for-age, with no protective effect for other anthropometric outcomes. The evidence was generally of poor quality, heterogeneity was high, and GRADE scores ranged from very low to high. CONCLUSIONS: This review confirms positive impacts of sanitation on aspects of health. Evidence gaps remain and point to the need for research that rigorously describes sanitation implementation and type of sanitation interventions

Identification and characterization of a novel non-structural protein of bluetongue virus

Bluetongue virus (BTV) is the causative agent of a major disease of livestock (bluetongue). For over two decades, it has been widely accepted that the 10 segments of the dsRNA genome of BTV encode for 7 structural and 3 non-structural proteins. The non-structural proteins (NS1, NS2, NS3/NS3a) play different key roles during the viral replication cycle. In this study we show that BTV expresses a fourth non-structural protein (that we designated NS4) encoded by an open reading frame in segment 9 overlapping the open reading frame encoding VP6. NS4 is 77–79 amino acid residues in length and highly conserved among several BTV serotypes/strains. NS4 was expressed early post-infection and localized in the nucleoli of BTV infected cells. By reverse genetics, we showed that NS4 is dispensable for BTV replication in vitro, both in mammalian and insect cells, and does not affect viral virulence in murine models of bluetongue infection. Interestingly, NS4 conferred a replication advantage to BTV-8, but not to BTV-1, in cells in an interferon (IFN)-induced antiviral state. However, the BTV-1 NS4 conferred a replication advantage both to a BTV-8 reassortant containing the entire segment 9 of BTV-1 and to a BTV-8 mutant with the NS4 identical to the homologous BTV-1 protein. Collectively, this study suggests that NS4 plays an important role in virus-host interaction and is one of the mechanisms played, at least by BTV-8, to counteract the antiviral response of the host. In addition, the distinct nucleolar localization of NS4, being expressed by a virus that replicates exclusively in the cytoplasm, offers new avenues to investigate the multiple roles played by the nucleolus in the biology of the cell

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

Anchors aweigh: the sources, variety, and challenges of mission drift

The growing number of studies which reference the concept of mission drift imply that such drift is an undesirable strategic outcome related to inconsistent organizational action, yet beyond such references little is known about how mission drift occurs, how it impacts organizations, and how organizations should respond. Existing management theory more broadly offers initial albeit equivocal insight for understanding mission drift. On the one hand, prior studies have argued that inconsistent or divergent action can lead to weakened stakeholder commitment and reputational damage. On the other hand, scholars have suggested that because environments are complex and dynamic, such action is necessary for ensuring organizational adaptation and thus survival. In this study, we offer a theory of mission drift that unpacks its origin, clarifies its variety, and specifies how organizations might respond to external perceptions of mission drift. The resulting conceptual model addresses the aforementioned theoretical tension and offers novel insight into the relationship between organizational actions and identity

Correcting Mortality for Loss to Follow-Up: A Nomogram Applied to Antiretroviral Treatment Programmes in Sub-Saharan Africa

Matthias Egger and colleagues present a nomogram and a web-based calculator to correct estimates of program-level mortality for loss to follow-up, for use in antiretroviral treatment programs

Characterization of the SNAG and SLUG Domains of Snail2 in the Repression of E-Cadherin and EMT Induction: Modulation by Serine 4 Phosphorylation

Snail1 and Snail2, two highly related members of the Snail superfamily, are direct transcriptional repressors of E-cadherin and EMT inducers. Previous comparative gene profiling analyses have revealed important differences in the gene expression pattern regulated by Snail1 and Snail2, indicating functional differences between both factors. The molecular mechanism of Snail1-mediated repression has been elucidated to some extent, but very little is presently known on the repression mediated by Snail2. In the present work, we report on the characterization of Snail2 repression of E-cadherin and its regulation by phosphorylation. Both the N-terminal SNAG and the central SLUG domains of Snail2 are required for efficient repression of the E-cadherin promoter. The co-repressor NCoR interacts with Snail2 through the SNAG domain, while CtBP1 is recruited through the SLUG domain. Interestingly, the SNAG domain is absolutely required for EMT induction while the SLUG domain plays a negative modulation of Snail2 mediated EMT. Additionally, we identify here novel in vivo phosphorylation sites at serine 4 and serine 88 of Snail2 and demonstrate the functional implication of serine 4 in the regulation of Snail2-mediated repressor activity of E-cadherin and in Snail2 induction of EMT