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Biomedical subjects

Andrew Worth

Publications and source records attributed to Andrew Worth.

2 recordsLinked to original sources

Measurement and modeling of human T cell kinetics.

The ability to measure, describe and interpret T cell kinetics is pivotal in understanding normal lymphocyte homeostasis and diseases that affect T cell numbers. Following in vivo labeling of dividing cells with 6,6-D(2)-glucose in eight healthy volunteers, peripheral blood T cells were sorted by CD4, CD8 and CD45 phenotype. Enrichment of deuterium in DNA was measured by gas chromatography-mass spectrometry. A novel model of T cell kinetics, allowing for heterogeneity within T cell pools, was used to analyze data on acquisition and loss of label and calculate proliferation and disappearance rates for each subpopulation. Proliferation rates for CD45RO(+)CD8(+) cells and CD45RO(+)CD4(+) cells were 5.1% and 2.7% /day, respectively (equivalent doubling times: 14 and 26 days). CD45RA(+)CD8(+) lymphocytes and CD45RA(+)CD4(+) lymphocytes had slower proliferation rates, 0.5% and 0.6% / day, respectively (doubling time about 4 months). Disappearance rates of labeled cells were similar for all cell types (7%-12% / day) and exceeded corresponding proliferation rates. This disparity may be understood conceptually in terms of either phenotypic heterogeneity (rapid versus slow turnover pools), or history (recently divided cells are more likely to die). The new kinetic model fits the data closely and avoids the need to postulate a large external source of lymphocytes to maintain equilibrium.

Adult↗

Functional MRI activity characterization using response time shift estimates from curve evolution.

Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brain's response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.

Adult↗