Several models have previously been proposed to predict the probability of non-sentinel lymph node (NSLN) metastases after a positive sentinel lymph node (SLN) biopsy in breast cancer. The aim of this study was to assess the accuracy of two previously published nomograms (MSKCC, Stanford) and to develop an alternative model with the best predictive accuracy in a Czech population. In the basic population of 330 SLN-positive patients from the Czech Republic, the accuracy of the MSKCC and the Stanford nomograms was tested by the area under the receiver operating characteristics curve (AUC). A new model (MOU nomogram) was proposed according to the results of multivariate analysis of relevant clinicopathologic variables. The new model was validated in an independent test population from Hungary (383 patients). In the basic population, six of 27 patients with isolated tumor cells (ITC) in the SLN harbored additional NSLN metastases. The AUCs of the MSKCC and Stanford nomograms were 0.68 and 0.66, respectively; for the MOU nomogram it reached 0.76. In the test population, the AUC of the MOU nomogram was similar to that of the basic population (0.74). The presence of only ITC in SLN does not preclude further nodal involvement. Additional variables are beneficial when considering the probability of NSLN metastases. In the basic population, the previously published nomograms (MSKCC and Stanford) showed only limited accuracy. The developed MOU nomogram proved more suitable for the basic population, such as for another independent population from a mid-European country.

In acutely ill patients, particularly in intensive care units or in mixed infections, time to a microbe-specific diagnosis is critical to a successful outcome of therapy. We report the application of evolving technologies involving mass spectrometry to diagnose and monitor a patient’s course. As proof of this concept, we studied five patients and used two rat models of mono-infection and coinfection. We report the noninvasive combined monitoring of Aspergillus fumigatus and Pseudomonas aeruginosa infection. The invasive coinfection was detected by monitoring the fungal triacetylfusarinine C and ferricrocin siderophore levels and the bacterial metabolites pyoverdin E, pyochelin, and 2-heptyl-4-quinolone, studied in the urine, endotracheal aspirate, or breath condensate. The coinfection was monitored by mass spectrometry followed by isotopic data filtering. In the rat infection model, detection indicated 100-fold more siderophores in urine compared to sera, indicating the diagnostic potential of urine sampling. The tools utilized in our studies can now be examined in large clinical series, where we could expect the accuracy and speed of diagnosis to be competitive with conventional methods and provide advantages in unraveling the complexities of mixed infections.

Using the established commercial system Sherlock (MIDI, Inc.), cellular fatty acid methyl ester analysis for differentiation among Burkholderia cepacia complex species was proven. The identification key based on the diagnostic fatty acids is able to discern phenotypically related Ralstonia pickettii and Pandoraea spp. and further distinguish Burkholderia pyrrocinia, Burkholderia ambifaria, and Burkholderia vietnamiensis.At present, nine Burkholderia species are combined into the Burkholderia cepacia complex (BCC) : B. cepacia, B. multivorans, B. cenocepacia, B. stabilis, B. vietnamiensis, B. dolosa, B. ambifaria, B. anthina, and B. pyrrocinia (2, 12-14). In some cases, the severity of BCC infection is closely connected with one particular species. The proper identification of likely BCC isolates is crucial for patients suffering from cystic fibrosis (CF) (9).Identification of BCC isolates to the species level based on the results obtained by genotypic methods, such as restriction fragment length polymorphism analysis of the recA gene or 16S rRNA genes (17, 18) and recA gene-based PCR (4), is mostly done with research tools provided in reference centers. Phenotypic testing is still frequently used in routine laboratories. Because of constantly changing taxonomy, such identification could be inaccurate. Databases of commonly used commercial kits are often incomplete and may even be insufficient to properly discern BCC isolates from other phylogenetically related isolates or from Ralstonia pickettii and Pandoraea spp. commonly isolated from clinical human sources.Cellular fatty analysis using semiautomated gas chromatography by the microbial identification system (MIS) Sherlock (MIDI, Inc., Newark, Del.) is a relatively rapid and cost-effective method widely available and suitable for clinical laboratories. Identification based on the species-specific differences in fatty acid composition in cell lipidic structure was proved to be a good taxonomic marker (15). Contrary to phenotyping, previous studies have shown that analysis of cellular fatty acid components was able to distinguish between the genera Burkholderia, Ralstonia, and Pandoraea (1,19). Fatty acids of the BCC are rather uniform; nevertheless, species-specific differences useful to separate B. anthina or B. ambifaria from B. cepacia and B. cenocepacia were observed (2, 13). So far there are no reports that include and compare the fatty acid compositions of all known BCC species and related taxa under standardized culture conditions. In this study, we tested whether comparing fatty acid profiles obtained on MIS Sherlock could yield the ability to sufficiently discriminate between BCC species and other closely related taxa.Type strains (Tables 1 and 2) and a set including 47 wellcharacterized clinical isolates recovered during a half-year survey in 2002 in two town hospitals and several ambulatory medical practices in our region (Ostrava, Czech Republic) were studied. Isolates (one per patient) originated primarily from non-CF patients (sputum, blood cultu...