Does microbicide use in consumer products promote antimicrobial resistance? A critical review and recommendations for a cohesive approach to risk assessment

The increasing use of microbicides in consumer products is raising concerns related to enhanced microbicide resistance in bacteria and potential cross resistance to antibiotics. The recently published documents on this topic from the European Commission have spawned much interest to better understand the true extent of the putative links for the benefit of the manufacturers, regulators, and consumers alike. This white paper is based on a 2-day workshop (SEAC-Unilever, Bedford, United Kingdom; June 2012) in the fields of microbicide usage and resistance. It identifies gaps in our knowledge and also makes specific recommendations for harmonization of key terms and refinement/standardization of methods for testing microbicide resistance to better assess the impact and possible links with cross resistance to antibiotics. It also calls for a better cohesion in research in this field. Such information is crucial to developing any risk assessment framework on microbicide use notably in consumer products. The article also identifies key research questions where there are inadequate data, which, if addressed, could promote improved knowledge and understanding to assess any related risks for consumer and environmental safety

Body composition among long distance runners

OBJECTIVE: The current study aimed to examine the body composition of adult male ultra-trail runners (UTR) according to their level of participation (regional UTR-R, vs. national UTR-N). METHODS: The sample was composed of 44 adult male UTR (aged 36.5±7.2 years; UTR-R: n=25; UTR-N: n=19). Body composition was assessed by air displacement plethysmography, bioelectrical impedance, and dual-energy X-ray absorptiometry. In addition, the Food Frequency Questionnaire (FFQ) was applied. A comparison between the groups was performed using independent samples t-test. RESULTS: Significant differences between groups contrasting in the competitive level were found for chronological age (in years; UTRR: 38.8±8.2 vs. UTR-N: 33.5±4.1); body density (in L.kg-1; UTR-R: 1.062±0.015 vs. UTR-N: 1.074±0.009); and fat mass (in kg; UTR-R: 12.7±6.8 vs. UTR-N: 7.6±2.7). CONCLUSION: UTR-N were younger, presented higher values for body density, and had less fat mass, although no significant differences were found for fat-free mass. The current study evidenced the profile of long-distance runners and the need for weight management programs to regulate body composition.OBJETIVO: O presente estudo objetivou examinar a composição corporal dos corredores de ultra-trail (UTR) e, adicionalmente, comparar dois grupos de acordo com o nível de participação (Regional vs. Nacional, respectivamente UTR-R e UTR-N). MÉTODOS: A amostra foi composta por 44 corredores adultos masculinos (36,5±7,2 anos de idade; UTR-R: n=25; UTR-N: n=19). A composição corporal foi avaliada recorrendo à pletismografia de ar deslocado, bioimpedância elétrica e absorciometria de raios X de dupla energia. Adicionalmente, foi utilizado o Questionário de Frequência Alimentar. A comparação entre grupos foi realizada com base na prova t-student para amostras independentes. RESULTADOS: Foram encontradas diferenças significativas por nível de competição para as seguintes variáveis dependentes: idade cronológica (em anos; UTR-R: 38,8±8,2 vs. UTR-N: 33,5±4,1); densidade corporal (em kg/L; UTR-R: 1,062±0,015 L/kg vs. UTR-N: 1,074±0,009); massa gorda (em kg; UTR-R: 12,7±6,8 kg vs. UTR-N: 7,6±2,7). CONCLUSÃO: Os UTR-N tendem a ser mais jovens e apresentam valores superiores de densidade corporal e, consequentemente, valores menores de massa gorda, sendo a massa isenta de gordura semelhante entre os grupos. O presente estudo determinou o perfil dos corredores adultos masculinos de longa distância (ultra-trail), realçando a importância de uma cuidadosa regulação da massa corporal

Microbial Diversity of a Brazilian Coastal Region Influenced by an Upwelling System and Anthropogenic Activity

BACKGROUND: Upwelling systems are characterised by an intense primary biomass production in the surface (warmest) water after the outcrop of the bottom (coldest) water, which is rich in nutrients. Although it is known that the microbial assemblage plays an important role in the food chain of marine systems and that the upwelling systems that occur in southwest Brazil drive the complex dynamics of the food chain, little is known about the microbial composition present in this region. METHODOLOGY/PRINCIPAL FINDINGS: We carried out a molecular survey based on SSU rRNA gene from the three domains of the phylogenetic tree of life present in a tropical upwelling region (Arraial do Cabo, Rio de Janeiro, Brazil). The aim was to analyse the horizontal and vertical variations of the microbial composition in two geographically close areas influenced by anthropogenic activity (sewage disposal/port activity) and upwelling phenomena, respectively. A lower estimated diversity of microorganisms of the three domains of the phylogenetic tree of life was found in the water of the area influenced by anthropogenic activity compared to the area influenced by upwelling phenomena. We observed a heterogenic distribution of the relative abundance of taxonomic groups, especially in the Archaea and Eukarya domains. The bacterial community was dominated by Proteobacteria, Cyanobacteria and Bacteroidetes phyla, whereas the microeukaryotic community was dominated by Metazoa, Fungi, Alveolata and Stramenopile. The estimated archaeal diversity was the lowest of the three domains and was dominated by uncharacterised marine Crenarchaeota that were most closely related to Marine Group I. CONCLUSIONS/SIGNIFICANCE: The variety of conditions and the presence of different microbial assemblages indicated that the area of Arraial do Cabo can be used as a model for detailed studies that contemplate the correlation between pollution-indicating parameters and the depletion of microbial diversity in areas close to anthropogenic activity; functional roles and geochemical processes; phylogeny of the uncharacterised diversity; and seasonal variations of the microbial assemblages

Revisiting statistical analysis curriculum in a data era: a learning-by-mistake approach

Contribution: We re-think the ‘Statistical Analysis’ curriculum building upon system engineering tools where assumptions (e.g., ABET criteria and student profiles) are carefully assessed, a learn-by-mistake approach ensures that several of the main statistical mistakes are learned, and advanced topics are proposed to make a strong connection with forthcoming courses in data science. Background: Today’s data science requires students and prospective data scientists to have a strong foundational background in statistical analysis methods and decision making. Given the diversity of students' profiles, and a multitude of statistical analysis curricula across the USA, we seek to provide guidelines on a curriculum that is in line with today’s data demanding era. Intended outcomes: The target audience comprises students in engineering courses that deal with data and seek to obtain a domain-specific technological or societal solution. Using a learn-by-mistake approach, we try to mend some of the most common mistakes in statistical analysis for the new generations of data professionals. The proposed curriculum equips students with multiple statistical methodologies that enable them to understand, process, extract, visualize, and communicate statistical evidence. Application design: We propose a systems engineering approach to design the curriculum that leverages tools and methodologies from operations research and statistics. Findings: Our approach ensures that the designed ‘Statistical Analysis’ course satisfies some of the intended constraints and goals by design. In particular, we designed an overarching hands-on example that integrates the topics covered in the curriculum into a transversal example and can be further customized to the different students’ profiles

Revisiting statistical analysis curriculum in a data era: a learning-by-mistake approach

Contribution: We re-think the ‘Statistical Analysis’ curriculum building upon system engineering tools where assumptions (e.g., ABET criteria and student profiles) are carefully assessed, a learn-by-mistake approach ensures that several of the main statistical mistakes are learned, and advanced topics are proposed to make a strong connection with forthcoming courses in data science. Background: Today’s data science requires students and prospective data scientists to have a strong foundational background in statistical analysis methods and decision making. Given the diversity of students' profiles, and a multitude of statistical analysis curricula across the USA, we seek to provide guidelines on a curriculum that is in line with today’s data demanding era. Intended outcomes: The target audience comprises students in engineering courses that deal with data and seek to obtain a domain-specific technological or societal solution. Using a learn-by-mistake approach, we try to mend some of the most common mistakes in statistical analysis for the new generations of data professionals. The proposed curriculum equips students with multiple statistical methodologies that enable them to understand, process, extract, visualize, and communicate statistical evidence. Application design: We propose a systems engineering approach to design the curriculum that leverages tools and methodologies from operations research and statistics. Findings: Our approach ensures that the designed ‘Statistical Analysis’ course satisfies some of the intended constraints and goals by design. In particular, we designed an overarching hands-on example that integrates the topics covered in the curriculum into a transversal example and can be further customized to the different students’ profiles.Team Tamas Keviczk

The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in <em>Staphylococcus aureus</em>

<div><p>Background</p><p>The rise of antibiotic resistance in pathogenic bacteria is a significant problem for the treatment of infectious diseases. Resistance is usually selected by the antibiotic itself; however, biocides might also co-select for resistance to antibiotics. Although resistance to biocides is poorly defined, different <i>in vitro</i> studies have shown that mutants presenting low susceptibility to biocides also have reduced susceptibility to antibiotics. However, studies with natural bacterial isolates are more limited and there are no clear conclusions as to whether the use of biocides results in the development of multidrug resistant bacteria.</p> <p>Methods</p><p>The main goal is to perform an unbiased blind-based evaluation of the relationship between antibiotic and biocide reduced susceptibility in natural isolates of <i>Staphylococcus aureus</i>. One of the largest data sets ever studied comprising 1632 human clinical isolates of <i>S. aureus</i> originated worldwide was analysed. The phenotypic characterization of 13 antibiotics and 4 biocides was performed for all the strains. Complex links between reduced susceptibility to biocides and antibiotics are difficult to elucidate using the standard statistical approaches in phenotypic data. Therefore, machine learning techniques were applied to explore the data.</p> <p>Results</p><p>In this pioneer study, we demonstrated that reduced susceptibility to two common biocides, chlorhexidine and benzalkonium chloride, which belong to different structural families, is associated to multidrug resistance. We have consistently found that a minimum inhibitory concentration greater than 2 mg/L for both biocides is related to antibiotic non-susceptibility in <i>S. aureus</i>.</p> <p>Conclusions</p><p>Two important results emerged from our work, one methodological and one other with relevance in the field of antibiotic resistance. We could not conclude on whether the use of antibiotics selects for biocide resistance or <i>vice versa</i>. However, the observation of association between multiple resistance and two biocides commonly used may be of concern for the treatment of infectious diseases in the future.</p> </div

New framework of analysis.

<p>In this paper, we propose the use of machine learning techniques to analyse epidemiological data sets, namely decision trees validated by permutation tests and clustering analysis of the data set followed by a visualisation step of the results. This methodology is successful in dealing with the complexity of molecular epidemiology data and its application in studying the potential linkage between antibiotic resistance and biocide reduced susceptibility.</p

Clustering of the non-susceptible population for rule 1.

<p>The different clusters for the non-susceptible population of rule 1 are shown in the dendogram with the respective global percentage of shared resistance of each set of antibiotics. Among the 487 isolates eligible for the clustering analysis, only 17.5% isolates are non-susceptible to all the 13 antibiotics. We have identified 4 clusters of antibiotics which may be related with the potential mechanisms of resistance already reported on the literature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055582#pone.0055582-Katzung1" target="_blank">[60]</a>.</p

Graphic matroid of <i>S. aureus</i> isolates colour mapped based on the first decision tree rule.

<p>Isolates with CLX MIC≤2 (classified as susceptible to each antibiotic, based on the first rule) are coloured in <i>green</i>. In <i>grey</i> are represented the remaining isolates.</p

Graphic matroid of <i>S. aureus</i> antibiotic susceptibility.

<p>Graphic matroid of <i>S. aureus</i> antibiotic susceptibility with a red-blue colour map. Isolates in <i>blue</i> are susceptible to a greater number of antibiotics and isolates in <i>red</i> share a greater non susceptibility to antibiotics. S-Susceptible. NS-Non-susceptible. ATB-Antibiotics.</p