The Burkholderia contaminans prevalent phenotypes as possible markers of poor clinical outcomes in chronic lung infection of children with cystic fibrosis.

Burkholderia contaminans, a species of the Burkholderia cepacia complex-prevalent in certain Latin-American and European countries-can cause chronic pulmonary infection in persons with cystic fibrosis. Our aim was to gain insights into long-term lung infections with a focus on correlating how bacterial phenotypic traits in the chronic infection impact on patients' clinical outcome. Genotypic characteristics of 85 B. contaminans isolates recovered from 70 patients were investigated. For 16 of those patients, the clinical status and bacterial phenotypic characteristics, e.g. several virulence factors, phenotypic variants, and the antimicrobial susceptibility pattern, were evaluated. Two clones were found in the whole bacterial population: (i) the multiresistant ST 872 PCR-recA-RFLP-HaeIII-K-pattern clone, which carries a pathogenic island homologous to BcenGI11 of B. cenocepacia J2315, and (ii) the ST 102 PCR-recA-RFLP-HaeIII-AT-pattern clone. The emergence of certain bacterial phenotypes in the chronic infection such as the nonmucoid phenotype, small colony variants, brownish pigmented colonies, and hypermutators, proved to be, together with coinfection with Pseudomonas aeruginosa, the possible markers of more challenging infections and poor prognosis. The presence of cocolonizers and the bacterial phenotypes that are especially adapted to persist in long-term respiratory tract infections have a crucial role in patients' clinical outcomes.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization.

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.