Expression and capping of a proliferation-associated surface membrane p34 kDa antigen on different human hematopoietic cell lines

A novel surface membrane nonglycosylated acidic polypeptide (34 kDa), encoded by a structural gene on chromosome 11, has been identified using murine monoclonal antibody (MoAb) 53.6 (IgG2a). MoAb 53.6, raised against uninduced cells of a human erythroleukemia line (HEL), recognizes a surface membrane antigen that is displayed on proliferating (cell cycle phase G1, S, and M + G2 phase) human leukocytes. The expression and redistribution (i.e., patching and capping) of the p34 kDa antigen on 27 different long-term human hematopoietic cell (HHC) lines was defined by fluorescence microscopy. These lines had been established from patients with leukemia or healthy donors and included phenotypically defined populations of T cells, B cells, and myelomonocytic cells. Almost all (greater than 95%) of the leukocytes of the 27 lines reacted strongly with MoAb 53.6. The majority of the leukocytes displayed p34 kDa antigen patching (26/27 lines; patched cells, 96-100%); moreover, 20 of 27 lines exhibited p34 kDa antigen capping (capped cells, 8-96%). Presentation of the p34 kDa antigen on surface membrane ultrastructures, imaged with immunogold using an indirect antibody labeling procedure, was illustrated by scanning electron microscopy, and endocytosis of the gold-tagged antigen-antibody complex was studied by transmission electron microscopy. The HHC lines are thought to represent immortalized populations of different human leukocyte subsets that are in different stages of maturation and/or differentiation; thus these lines should prove useful as models for further characterizing this unique p34 kDa proliferation-associated antigen and for defining the mechanisms and significance of surface membrane antigen redistribution and modulation that has been associated with leukocyte activation and propagation.     

Independent component analysis and clustering improve signal-to-noise ratio for statistical analysis of event-related potentials

OBJECTIVE: To evaluate the effectiveness of a new method of using Independent Component Analysis (ICA) and k-means clustering to increase the signal-to-noise ratio of Event-Related Potential (ERP) measurements while permitting standard statistical comparisons to be made despite the inter-subject variations characteristic of ICA. METHODS: Per-subject ICA results were used to create a channel pool, with unequal weights, that could be applied consistently across subjects. Signals derived from this and other pooling schemes, and from unpooled electrodes, were subjected to identical statistical analysis of the N170 own-face effect in a Joe/No Joe face recognition paradigm wherein participants monitored for a target face (Joe) presented amongst other unfamiliar faces and their own face. Results between the Joe, unfamiliar face and own face conditions were compared using Cohen's d statistic (square root of signal-to-noise ratio) to measure effect size. RESULTS: When the own-face condition was compared to the Joe and unfamiliar-face conditions, the channel map method increased effect size by a factor ranging from 1.2 to 2.2. These results stand in contrast to previous findings, where conventional pooling schemes failed to reveal an N170 effect to the own-face stimulus (Tanaka JW, Curran T, Porterfield A, Collins D. The activation of pre-existing and acquired face representations: the N250 ERP as an index of face familiarity. J Cogn Neurosci 2006;18:1488-97). Consistent with conventional pooling schemes, the channel map approach showed no reliable differences between the Joe and Unfamiliar face conditions, yielding a decrease in effect size ranging from 0.13 to 0.75. CONCLUSIONS: By increasing the signal-to-noise ratio in the measured waveforms, the channel pool method demonstrated an enhanced sensitivity to the neurophysiological response to own-face relative to other faces. SIGNIFICANCE: By overcoming the characteristic inter-subject variations of ICA, this work allows classic ERP analysis methods to exploit the improved signal-to-noise ratio obtainable with ICA.     

Using adiabatic inversion pulses for long-T2 suppression in ultrashort echo time (UTE) imaging.

Ultrashort echo time (UTE) imaging is a technique that can visualize tissues with sub-millisecond T(2) values that have little or no signal in conventional MRI techniques. The short-T(2) tissues, which include tendons, menisci, calcifications, and cortical bone, are often obscured by long-T(2) tissues. This paper introduces a new method of long-T(2) component suppression based on adiabatic inversion pulses that significantly improves the contrast of short-T(2) tissues. Narrow bandwidth inversion pulses are used to selectively invert only long-T(2) components. These components are then suppressed by combining images prepared with and without inversion pulses. Fat suppression can be incorporated by combining images with the pulses applied on the fat and water resonances. Scaling factors must be used in the combination to compensate for relaxation during the preparation pulses. The suppression is insensitive to RF inhomogeneities because it uses adiabatic inversion pulses. Simulations and phantom experiments demonstrate the adiabatic pulse contrast and how the scaling factors are chosen. In vivo 2D UTE images in the ankle and lower leg show excellent, robust long-T(2) suppression for visualization of cortical bone and tendons.     

Clinical importance,statistical significance and the assessment of economic and quality-of-life outcomes

The assessment of economic and quality-of-life outcomes of health care interventions is moving into a new era, with such assessments increasingly being made within the context of controlled clinical trials. Traditionally the measurement of many variables in economic evaluations, particularly costs, has been deterministic. In the context of clinical trials the measurement of variables is stochastic, with the standard principles of statistical inference being applied to analyse differences between treatments in terms of effectiveness. Economists participating in clinical research are therefore being called upon to specify the sample size for the economic component of the evaluation and to undertake statistical tests for differences in cost or cost-effectiveness. This paper discusses the current methodological issues surrounding stochastic measurement in clinical trials, discusses the additional issues raised by the assessment of economic and quality-of-life outcomes and specifies the challenges facing economists if they are to answer the questions now being posed about economic analysis by statisticians and clinical researchers. It is concluded that application of the standard principles of statistical inference to economic data is not straightforward and will require value judgements to be made about statistical significance and economic importance, which may differ from those already made in purely clinical studies.