Measures to eradicate multidrug-resistant organism outbreaks: How much does it cost?

This study aimed to assess the economic burden of infection control measures that succeeded in eradicating multidrug-resistant organisms (MDROs) in emerging epidemic contexts in hospital settings. The MEDLINE, EMBASE and Ovid databases were systematically interrogated for original English-language articles detailing costs associated with strict measures to eradicate MDROs published between 1 January 1974 and 2 November 2014. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Overall, 13 original articles were retrieved reporting data on several MDROs, including glycopeptide-resistant enterococci (n = 5), carbapenemase-producing Enterobacteriacae (n = 1), methicillin-resistant Staphylococcus aureus (n = 5), and carbapenem-resistant Acinetobacter baumannii (n = 2). Overall, the cost of strict measures to eradicate MDROs ranged from €285 to €57 532 per positive patient. The major component of these overall costs was related to interruption of new admissions, representing €2466 to €47 093 per positive patient (69% of the overall mean cost; range, 13-100%), followed by mean laboratory costs of €628 to €5849 (24%; range, 3.3-56.7%), staff reinforcement costs of €6204 to €148 381 (22%; range, 3.3-52%), and contact precautions costs of €166 to €10 438 per positive patient (18%; range, 0.7-43.3%). Published data on the economic burden of strict measures to eradicate MDROs are limited, heterogeneous, and weakened by several methodological flaws. Novel economic studies should be performed to assess the financial impact of current policies, and to identify the most cost-effective strategies to eradicate emerging MDROs in healthcare facilities

Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical Review

Annotation of protein residues based on a literature analysis: cross-validation against UniProtKb

<p>Abstract</p> <p>Background</p> <p>A protein annotation database, such as the Universal Protein Resource knowledge base (UniProtKb), is a valuable resource for the validation and interpretation of predicted 3D structure patterns in proteins. Existing studies have focussed on point mutation extraction methods from biomedical literature which can be used to support the time consuming work of manual database curation. However, these methods were limited to point mutation extraction and do not extract features for the annotation of proteins at the residue level.</p> <p>Results</p> <p>This work introduces a system that identifies protein residues in MEDLINE abstracts and annotates them with features extracted from the context written in the surrounding text. MEDLINE abstract texts have been processed to identify protein mentions in combination with taxonomic species and protein residues (F1-measure 0.52). The identified protein-species-residue triplets have been validated and benchmarked against reference data resources (UniProtKb, average F1-measure of 0.54). Then, contextual features were extracted through shallow and deep parsing and the features have been classified into predefined categories (F1-measure ranges from 0.15 to 0.67). Furthermore, the feature sets have been aligned with annotation types in UniProtKb to assess the relevance of the annotations for ongoing curation projects. Altogether, the annotations have been assessed automatically and manually against reference data resources.</p> <p>Conclusion</p> <p>This work proposes a solution for the automatic extraction of functional annotation for protein residues from biomedical articles. The presented approach is an extension to other existing systems in that a wider range of residue entities are considered and that features of residues are extracted as annotations.</p

Terminologie assistée par ordinateur : expérience d'élaboration d'un dictionnaire des biotechnologies de la reproduction à l'aide d'un logiciel d'analyse automatique de texte (Lexter)

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