b Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a relatively new addition to the clinical microbiology laboratory. The performance of the MALDI Biotyper system (Bruker Daltonics) was compared to those of phenotypic and genotypic identification methods for 690 routine and referred clinical isolates representing 102 genera and 225 unique species. We systematically compared direct-smear and extraction methods on a taxonomically diverse collection of isolates. The optimal score thresholds for bacterial identification were determined, and an approach to address multiple divergent results above these thresholds was evaluated. Analysis of identification scores revealed optimal species-and genus-level identification thresholds of 1.9 and 1.7, with 91.9% and 97.0% of isolates correctly identified to species and genus levels, respectively. Not surprisingly, routinely encountered isolates showed higher concordance than did uncommon isolates. The extraction method yielded higher scores than the direct-smear method for 78.3% of isolates. Incorrect species were reported in the top 10 results for 19.4% of isolates, and although there was no obvious cutoff to eliminate all of these ambiguities, a 10% score differential between the top match and additional species may be useful to limit the need for additional testing to reach single-species-level identifications. Recent decades have seen advances in automation of traditional phenotypic and biochemical methods for microbial identification (ID), and advances in sequencing and the proliferation of genomic data hold great promise for further improvements. The development of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has brought microbial diagnostics to another cusp of rapid development. The speed and low cost of bacterial identification by MALDI-TOF MS make it an attractive technology in the clinical microbiology laboratory, and it has shown promise for identification of Gram-positive cocci (2, 6, 8), enteric and nonfermenting Gram-negative rods (11,21,24), HACEK organisms (10), anaerobes (14,17,19,20,31), and broad cohorts of clinically relevant bacteria (3,4,22,27,30).Commercial MALDI-TOF systems identify a broad range of microorganisms based on analysis of unique "fingerprints" of abundant proteins from whole cells or cellular extracts (15,23,26,28). These profiles are searched against databases of reference spectra, and similarity scores for the top database matches are used to determine the identification of unknown isolates. As observed previously, a systematic evaluation of scoring criteria on diverse isolates could improve results (2,10,25,27,29). Identification may be complicated when multiple species-or genus-level matches are among the top 10 results. Most current publications on the MALDI Biotyper system (Bruker Daltonics, Billerica, MA) do not address these complicated situations; however, one example where this problem is addressed is the use of the "10% rule," which stat...

Background Reported odds ratios and population attributable fractions (PAF) for late-onset Alzheimer’s disease (LOAD) risk loci (BIN1, ABCA7, CR1, MS4A4E, CD2AP, PICALM, MS4A6A, CD33, and CLU) come from clinically ascertained samples. Little is known about the combined PAF for these LOAD risk alleles and the utility of these combined markers for case-control prediction. Here we evaluate these loci in a large population-based sample to estimate PAF and explore the effects of additive and non-additive interactions on LOAD status prediction performance. Methods 2,419 samples from the Cache County Memory Study were genotyped for APOE and nine LOAD risk loci from AlzGene.org. We used logistic regression and ROC analysis to assess the LOAD status prediction performance of these loci using additive and non-additive models, and compared ORs and PAFs between AlzGene.org and Cache County. Results Odds ratios were comparable between Cache County and AlzGene.org when identical SNPs were genotyped. PAFs from AlzGene.org ranged from 2.25–37%; those from Cache County ranged from 0.05–20%. Including non-APOE alleles significantly improved LOAD status prediction performance (AUC = 0.80) over APOE alone (AUC = 0.78) when not constrained to an additive relationship (p < 0.03). We identified potential allelic interactions (p-values uncorrected): CD33-MS4A4E (Synergy Factor = 5.31; p < 0.003) and CLU-MS4A4E (SF = 3.81; p < 0.016). Conclusions While non-additive interactions between loci significantly improve diagnostic ability, the improvement does not reach the desired sensitivity or specificity for clinical use. Nevertheless, these results suggest that understanding gene-gene interactions may be important in resolving Alzheimer’s disease etiology.

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A method for measuring the thickness of grease films between the rollers and rings of rolling bearings is described. Measurements in two designs of bearing show that a grease can initially form thicker films than can its base oil but that, without frequent relubrication, the grease films become slightly the thinner with time. The thinning of grease films, which is attributed to partial starvation, is accompanied by a reduction in viscous friction. Except in critical applications, where the thickest films are required, the consequences of partial starvation associated with grease lubrication can be advantageous.