The curvHDR method for gating flow cytometry samples.

BACKGROUND: High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry. RESULTS: We develop a statistical procedure, named curvHDR, for automatic and semi-automatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to human-perceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry infor-matics, is developed. CONCLUSION: The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. It offers big savings in human labour when processing high-throughput flow cytometry data whilst retaining a good degree of efficacy.

The HLDA8 blind panel: findings and conclusions.

There were over 600 antibodies submitted to HLDA8, with many of unknown specificity. Of these, 101 antibodies were selected for a blind panel study that also included 5 negative controls and 27 positive controls of known CD specificity making a total of 133 antibodies in the final panel. Of the 101 unknowns, 31 antibodies were identified during the course of this blind panel study as being specific for known molecules and included some specific for MHC class II antigens, CD45 isoforms and the Dombrock antigen. Several antibody pairs among those in the blind panel were found to have very similar staining patterns and were therefore compared by immunohistochemical and/or Western blot analyses for identity.

Association of expired nitric oxide with occupational particulate exposure.

Particulate air pollution has been associated with adverse respiratory health effects. This study assessed the utility of expired nitric oxide to detect acute airway responses to metal-containing fine particulates. Using a repeated-measures study design, we investigated the association between the fractional concentration of expired nitric oxide (F(E)NO) and exposure to particulate matter with an aerodynamic mass median diameter of less than or equal to 2.5 micro m (PM(2.5)) in boilermakers exposed to residual oil fly ash and metal fumes. Subjects were monitored for 5 days during boiler repair overhauls in 1999 (n = 20) or 2000 (n = 14). The Wilcoxon median baseline F(E)NO was 10.6 ppb [95% confidence interval (CI): 9.1, 12.7] in 1999 and 7.4 ppb (95% CI: 6.7, 8.0) in 2000. The Wilcoxon median PM(2.5) 8-hr time-weighted average was 0.56 mg/m(3) (95% CI: 0.37, 0.93) in 1999 and 0.86 mg/m(3) (95% CI: 0.65, 1.07) in 2000. F(E)NO levels during the work week were significantly lower than baseline F(E)NO in 1999 (p < 0.001). A significant inverse exposure-response relationship between log-transformed F(E)NO and the previous workday's PM(2.5) concentration was found in 1999, after adjusting for smoking status, age, and sampling year. With each 1 mg/m(3) incremental increase in PM(2.5) exposure, log F(E)NO decreased by 0.24 (95% CI: -0.38, -0.10) in 1999. The lack of an exposure-response relationship between PM(2.5) exposure and F(E)NO in 2000 could be attributable to exposure misclassification resulting from the use of respirators. In conclusion, occupational exposure to metal-containing fine particulates was associated with significant decreases in F(E)NO in a survey of workers with limited respirator usage.

Generalized additive models for cancer mapping with incomplete covariates.

Maps depicting cancer incidence rates have become useful tools in public health research, giving valuable information about the spatial variation in rates of disease. Typically, these maps are generated using count data aggregated over areas such as counties or census blocks. However, with the proliferation of geographic information systems and related databases, it is becoming easier to obtain exact spatial locations for the cancer cases and suitable control subjects. The use of such point data allows us to adjust for individual-level covariates, such as age and smoking status, when estimating the spatial variation in disease risk. Unfortunately, such covariate information is often subject to missingness. We propose a method for mapping cancer risk when covariates are not completely observed. We model these data using a logistic generalized additive model. Estimates of the linear and non-linear effects are obtained using a mixed effects model representation. We develop an EM algorithm to account for missing data and the random effects. Since the expectation step involves an intractable integral, we estimate the E-step with a Laplace approximation. This framework provides a general method for handling missing covariate values when fitting generalized additive models. We illustrate our method through an analysis of cancer incidence data from Cape Cod, Massachusetts. These analyses demonstrate that standard complete-case methods can yield biased estimates of the spatial variation of cancer risk.

The association of expired nitric oxide with occupational particulate metal exposure.

Toxicologic studies have shown that soluble transition metals in residual oil fly ash (ROFA) can induce pulmonary injury. In this study, we investigated the association between the fractional concentration of expired nitric oxide (FENO) and exposure to metal constituents of particulate matter with an aerodynamic mass median diameter < or =2.5 microm (PM2.5) in boilermakers exposed to ROFA and metal fume. Metals investigated included vanadium, chromium, manganese, nickel, copper, and lead. Subjects were monitored for 5 consecutive days during boiler repair overhauls in 1999 (n=20) and 2000 (n=14). In 1999, we found a significant inverse association between log-transformed FENO and PM2.5 metal concentrations. LogFENO changed by -0.03 (95% CI: -0.04, -0.01), -0.56 (95% CI: -0.88, -0.24), -0.09 (95% CI: -0.16, -0.02), and -0.04 (95% CI: -0.07, -0.02) per microg/m3 of PM2.5 vanadium, chromium, manganese, and nickel, respectively. In 2000, no significant associations were observed, most likely due to exposure misclassification resulting from the use of respirators. The inverse association between PM2.5 metal exposure and FENO in subjects with limited respirator usage suggests that soluble transition metals might be partially responsible for the adverse pulmonary responses seen in workers exposed to ROFA.

Association of expired nitric oxide with urinary metal concentrations in boilermakers exposed to residual oil fly ash.

BACKGROUND: Exposure to metal-containing particulate matter has been associated with adverse pulmonary responses. Metals in particulate matter are soluble, hence are readily recovered in urine of exposed individuals. This study investigated the association between urinary metal concentrations and the fractional concentration of expired nitric oxide (F(E)NO) in boilermakers (N = 32) exposed to residual oil fly ash (ROFA). METHODS: Subjects were monitored at a boiler overhaul site located in the New England area, USA. F(E)NO and urine samples were collected pre- and post-workshift for 5 consecutive workdays. Metals investigated included vanadium (V), chromium (Cr), manganese (Mn), nickel (Ni), copper (Cu), and lead (Pb). RESULTS: The median F(E)NO was 7.5 ppb (95% CI: 7.4-8.0), and the median creatinine-adjusted urinary metal concentrations (mug/g creatinine) were: vanadium, 1.37; chromium, 0.48; manganese, 0.30; nickel, 1.52; copper, 3.70; and lead, 2.32. Linear mixed-effects models indicated significant inverse exposure-response relationships between log F(E)NO and the log-transformed urinary concentrations of vanadium, manganese, nickel, copper, and lead at several lag times, after adjusting for smoking status. CONCLUSIONS: Urine samples may be utilized as a biomarker of occupational metal exposure. The inverse association between F(E)NO and urinary metal concentrations suggests that exposure to metals in particulate matter may have an adverse effect on respiratory health.