NEW TOOLS FOR MONITORING GAMMA CAMERA UNIFORMITY

Detector uniformity is a fundamental performance characteristic of all modern gamma camera systems, and ensuring a stable, uniform detector response is critical for maintaining clinical images that are free of artifact. For these reasons, the assessment of detector uniformity is one of the most common activities associated with a successful clinical quality assurance program in gamma camera imaging. The evaluation of this parameter, however, is often unclear because it is highly dependent upon acquisition conditions, reviewer expertise, and the application of somewhat arbitrary limits that do not characterize the spatial location of the non-uniformities. Furthermore, as the goal of any robust quality control program is the determination of significant deviations from standard or baseline conditions, clinicians and vendors often neglect the temporal nature of detector degradation (1). This thesis describes the development and testing of new methods for monitoring detector uniformity. These techniques provide more quantitative, sensitive, and specific feedback to the reviewer so that he or she may be better equipped to identify performance degradation prior to its manifestation in clinical images. The methods exploit the temporal nature of detector degradation and spatially segment distinct regions-of-non-uniformity using multi-resolution decomposition. These techniques were tested on synthetic phantom data using different degradation functions, as well as on experimentally acquired time series floods with induced, progressively worsening defects present within the field-of-view. The sensitivity of conventional, global figures-of-merit for detecting changes in uniformity was evaluated and compared to these new image-space techniques. The image-space algorithms provide a reproducible means of detecting regions-of-non-uniformity prior to any single flood image’s having a NEMA uniformity value in excess of 5%. The sensitivity of these image-space algorithms was found to depend on the size and magnitude of the non-uniformities, as well as on the nature of the cause of the non-uniform region. A trend analysis of the conventional figures-of-merit demonstrated their sensitivity to shifts in detector uniformity. The image-space algorithms are computationally efficient. Therefore, the image-space algorithms should be used concomitantly with the trending of the global figures-of-merit in order to provide the reviewer with a richer assessment of gamma camera detector uniformity characteristics.

Hsp110 chaperones control client fate determination in the hsp70-Hsp90 chaperone system.

Heat shock protein 70 (Hsp70) plays a central role in protein homeostasis and quality control in conjunction with other chaperone machines, including Hsp90. The Hsp110 chaperone Sse1 promotes Hsp90 activity in yeast, and functions as a nucleotide exchange factor (NEF) for cytosolic Hsp70, but the precise roles Sse1 plays in client maturation through the Hsp70-Hsp90 chaperone system are not fully understood. We find that upon pharmacological inhibition of Hsp90, a model protein kinase, Ste11DeltaN, is rapidly degraded, whereas heterologously expressed glucocorticoid receptor (GR) remains stable. Hsp70 binding and nucleotide exchange by Sse1 was required for GR maturation and signaling through endogenous Ste11, as well as to promote Ste11DeltaN degradation. Overexpression of another functional NEF partially compensated for loss of Sse1, whereas the paralog Sse2 fully restored GR maturation and Ste11DeltaN degradation. Sse1 was required for ubiquitinylation of Ste11DeltaN upon Hsp90 inhibition, providing a mechanistic explanation for its role in substrate degradation. Sse1/2 copurified with Hsp70 and other proteins comprising the "early-stage" Hsp90 complex, and was absent from "late-stage" Hsp90 complexes characterized by the presence of Sba1/p23. These findings support a model in which Hsp110 chaperones contribute significantly to the decision made by Hsp70 to fold or degrade a client protein.

THE USE OF MULTIVARIATE ANALYSIS OF COVARIANCE IN EVALUATING THE COMPARABILITY OF LABORATORY DETERMINATIONS IN COOPERATIVE STUDIES

The role of clinical chemistry has traditionally been to evaluate acutely ill or hospitalized patients. Traditional statistical methods have serious drawbacks in that they use univariate techniques. To demonstrate alternative methodology, a multivariate analysis of covariance model was developed and applied to the data from the Cooperative Study of Sickle Cell Disease.^ The purpose of developing the model for the laboratory data from the CSSCD was to evaluate the comparability of the results from the different clinics. Several variables were incorporated into the model in order to control for possible differences among the clinics that might confound any real laboratory differences.^ Differences for LDH, alkaline phosphatase and SGOT were identified which will necessitate adjustments by clinic whenever these data are used. In addition, aberrant clinic values for LDH, creatinine and BUN were also identified.^ The use of any statistical technique including multivariate analysis without thoughtful consideration may lead to spurious conclusions that may not be corrected for some time, if ever. However, the advantages of multivariate analysis far outweigh its potential problems. If its use increases as it should, the applicability to the analysis of laboratory data in prospective patient monitoring, quality control programs, and interpretation of data from cooperative studies could well have a major impact on the health and well being of a large number of individuals. ^