Genome sequence of OXA-48 carbapenemase-producing Klebsiella pneumoniae KpO3210.

Klebsiella pneumoniae KpO3210 is a OXA-48 carbapenemase-producing isolate obtained from a blood culture in the context of an outbreak in Hospital Universitario La Paz (Madrid, Spain) in 2010. It belongs to the major clone detected during the outbreak and is resistant to all beta-lactams and to several other antibiotics.

Biomonitoring acidic drainage impact in a complex setting using periphyton.

Acid mine drainage (AMD) often exerts various environmental pressures on nearby water courses: chemical stress from low pH and dissolved metals; physical stress from metal oxide deposits. Affected streams can thus display a spatially variable combination of stress agents that may complicate its biomonitoring using native communities such as periphyton. Here, we have measured water and periphyton variables in four streams that surround an abandoned copper mine to determine which periphyton attributes consistently detected AMD impact in a complex environmental setting. Seventeen years after the end of commercial exploitation, the abandoned mine still decreases water quality in nearby streams: moderate acidification, very high metal load (Al, Ni, Cu, Zn), and a conspicuous presence of metal oxide deposits with diverse composition. Even under the resultant complex pattern of polluted conditions, periphyton was a reliable bioindicator of AMD. Epilithic diatom taxa tolerant of acidic conditions increased in AMD sites and, at severely impacted locations, species richness decreased. Also, algal biomass may have been negatively affected in some stream reaches affected by metal oxide deposits. Other periphyton attributes (total biomass, diatom diversity) seemed mostly unrelated to AMD. Diatom assemblage composition was the most sensitive and consistent bioindicator of mine drainage; besides, it rendered a biological assessment of AMD impact that largely coincided with the physicochemical evaluation. Still, including other taxonomic (proportion of acid-tolerant diatom species, diatom richness) and non-taxonomic (algal biomass) attributes in the biomonitoring procedure rendered a more comprehensive assessment of the negative consequences generated by AMD.

Using Electronic Health Records to Assess Depression and Cancer Comorbidities.

Comorbid diseases are an important concern in oncology since they can affect the choice and effectiveness of treatment. What is particularly relevant is the fact that the diagnosis of depression in cancer patients has an important impact on the quality of life of these patients. Although there is no consensus about a specific relationship of depression with certain cancer types, some authors have proposed that depression constitutes a risk factor for cancer. The objective of this study is to identify the presence of comorbidities in a massive EHR system, between depression and the 10 most common cancers in women and men and to determine if there is a preferred temporal ordering in the co-occurrence of these diseases. All the cancers studied showed a significant co-occurrence with depression, more specifically, twice more frequent than what could be expected by chance. A preferred directionality was identified between some of the comorbid diseases, such as breast cancer followed by depression, and depression followed by either stomach cancer, colorectal cancer or lung cancer. Future work will address other potential factors that have an influence on the likelihood of suffering from depression in patients with cancer, such as drug therapies received, exposure to substance of abuse or other comorbidities.

COVER: a priori estimation of coverage for metagenomic sequencing.

In any metagenomic project, the coverage obtained for each particular species depends on its abundance. This makes it difficult to determine a priori the amount of DNA sequencing necessary to obtain a high coverage for the dominant genomes in an environment. To aid the design of metagenomic sequencing projects, we have developed COVER, a web-based tool that allows the estimation of the coverage achieved for each species in an environmental sample. COVER uses a set of 16S rRNA sequences to produce an estimate of the number of operational taxonomic units (OTUs) in the sample, provides a taxonomic assignment for them, estimates their genome sizes and, most critically, corrects for the number of unobserved OTUs. COVER then calculates the amount of sequencing needed to achieve a given goal. Our tests and simulations indicate that the results obtained through COVER are in very good agreement with the experimental results.