PubMed Health⌕ Search

Biomedical subjects

Paul Speckman

Publications and source records attributed to Paul Speckman.

2 recordsLinked to original sources

Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS.

Proteomic analysis of body fluids, including breast nipple aspirate fluid (NAF), holds promise to aid in early cancer detection. We conducted a prospective trial that collected NAF from women scheduled for diagnostic breast surgery to determine 1) the consistency of proteomic results, 2) protein masses associated with breast cancer, 3) subsets of women with a unique proteomic profile and 4) a breast cancer predictive model. NAF was collected preoperatively in 114 women and analyzed by SELDI-TOF mass spectrometry over a 3-50 kDa range using H4, NP and SAX ProteinChips. For all 3 chips, the same protein peaks were detected over 90% of the time in duplicate samples. The overall coefficient of variation was < or = 0.17% for each chip for the internal standard and < or = 0.29% for the unknown proteins. Seven candidate protein ion masses frequently expressed in NAF were identified. Three (5,200-H4, p=.04, 11,880-H4, p=.07 and 13,880 Da-SAX, p=.03) were differentially expressed in women with/without breast cancer. Protein expression differed between women with/without pathologic nipple discharge (PND), but the 5,200, 11,880 and 13,880 proteins remained associated with breast cancer even if PND samples were excluded. Subset analysis identified differences in expression between benign disease and DCIS and between DCIS and invasive cancer for the 5,200 and 33,400 Da proteins. The best cancer detection model included age, parity and the 11,880 Da protein, and excluded women with PND. 1) NAF proteomic analysis using SELDI-TOF is reproducible with the same sample set across different platforms, 2) differential proteomic expression exists between women/without breast cancer and 3) combining proteomic and clinical information that are available before surgery optimizes the prediction of which women have breast cancer.

Adult↗

A hierarchical model for estimating response time distributions.

We present a statistical model for inference with response time (RT) distributions. The model has the following features. First, it provides a means of estimating the shape, scale, and location (shift) of RT distributions. Second, it is hierarchical and models between-subjects and within-subjects variability simultaneously. Third, inference with the model is Bayesian and provides a principled and efficient means of pooling information across disparate data from different individuals. Because the model efficiently pools information across individuals, it is particularly well suited for those common cases in which the researcher collects a limited number of observations from several participants. Monte Carlo simulations reveal that the hierarchical Bayesian model provides more accurate estimates than several popular competitors do. We illustrate the model by providing an analysis of the symbolic distance effect in which participants can more quickly ascertain the relationship between nonadjacent digits than that between adjacent digits.

Cognition↗