NEW METHOD FOR MEASUREMENT OF CDT LEVEL IN SERUM
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408 GLEN PARK DR, Bay Village, OH, 44140
Name: ZASLAVSKY, BORIS Y
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Phone: (216) 808-8708
AbstractDESCRIPTION: (Adapted from the applicant's abstract) The innovation described in this proposal is aimed at providing a new method for detection of carbohydrate-deficient transferrin (CDT) in human blood as a biochemical marker of alcohol abuse. CTD has been extensively shown to be a sensitive and specific marker related to long term excessive alcohol consumption. Existing analytical techniques for the measurements of CTD in the blood or its ration to intact transferrin involve separation of CTD from other blood proteins, followed by concentration measurement typically using immunoassay methods. These techniques are in general time consuming, expensive, and must be performed by trained professionals. ANALIZA is proposing a new technique for the measurement of CDT/transferrin ratio directly using blood samples without pre- separation. The technique is based upon a novel application of aqueous two-phase partitioning. The technique is unique in the type of information obtained, is inexpensive and time-and labor-efficient, and may provide additional information not possible with existing technologies. Phase I work will involve the design and analysis of the test and its preliminary feasibility and validation study. Phases II and III work will concentrate on a wide-range validation protocol, together with optimization studies and a construction of a prototype automated instrument. $ = TOTAL AWARD AMTS & NOT LIMITED TO PORTION OF PROJECT RELATED TO SUBJECT OF SEARCH SUBPROJECT $ = TOTAL AWARD AMOUNT DIVIDED BY NUMBER OF SUBPROJECTS SOURCE: CRISP FORMAT F FY 97 LAST UPDATE 04-07-98 1QUERY 1536 ID SEARCH 06/01/98 PAGE 9 --PROJECT NUMBER......2 R44 AG12305-02A2 INVESTIGATOR NAME/ADDRESS FY 97 LOWRIMORE, GENE R IRG/INTRAMURAL UNIT..ZRG5 DECISION SYSTEMS, INC AWARD AMOUNT......... $366,588 1007 INDIAN TRIAL RALEIGH, NC 27609 PERFORMING ORGANIZATION: DECISION SYSTEMS, INC. TITLE SUPPORT ENVIRONMENT FOR GRADE OF MEMBERSHIP MODEL ABSTRACT: Grade of Membership (GoM) is a multivariate analytic technique for analyzing high dimensional discrete response data with very general distributional assumptions. GoM has been developed as a research tool at Duke University over the last 10 years with efforts focused on developing and assessing the statistical foundations of GoM. Little effort has been directed to producing a fully documented version of GoM. Thus, various versions of GoM exist with different capabilities. The first project task will be to identify an appropriate research version of the GoM which will be enhanced and documented for distribution to NIA and NIH researchers. The GoM version will be capable of handling datasets with sizes on the order of 10,000 observations and 30 analytic variables and will minimally operate on workstations and 486-based PCs. Such a version of GoM would be capable of analyzing many of the national survey datasets including the National Long Term Care Surveys. An intelligent support environment for the GoM model is also proposed to assist researchers in applying GoM. The support environment will include three modules: Question and Answers (Q&A), Data Preparation, and Report Analysis. Development of a prototype environment and a DOS version of GoM is proposed for Phase I. $ = TOTAL AWARD AMTS & NOT LIMITED TO PORTION OF PROJECT RELATED TO SUBJECT OF SEARCH SUBPROJECT $ = TOTAL AWARD AMOUNT DIVIDED BY NUMBER OF SUBPROJECTS SOURCE: CRISP FORMAT F FY 97 LAST UPDATE 04-07-98 1QUERY 1536 ID SEARCH 06/01/98 PAGE 10 --PROJECT NUMBER......2 R44 AG12308-02A2 INVESTIGATOR NAME/ADDRESS FY 97 SCHWARTZ, MARK H IRG/INTRAMURAL UNIT..ZRG2 MANDALA SCIENCES AWARD AMOUNT......... $377,142 4089 ABERDEEN COURT ORCHARD LAKE, MI 48323 PERFORMING ORGANIZATION: MANDALA SCIENCES TITLE COMPUTER TOOLS FOR OUTCOMES ANALYSIS OF HIP REPLACEMENT ABSTRACT: Mandala Sciences (MSI) CODA project has 2 main objectives: (1) develop analysis tools to test hypotheses regarding effectiveness of surgical procedures and patient outcomes and (2) generate proprietary decision support prediction models for hip and knee replacements. MSI hybrid Neural Network/Expert System methodology uses an Entropy NN TM structure which has the innovative ability to generate a rule base. The discovered rules will be used to create "portable" Expert System predictive modules. Phase II progress is built upon successful MSI collaboration with Henry Ford Health System to show NN techniques can generate and evaluate prognostic models using outcomes data. Consultation with orthopedic surgeons identified 13 patient-provided variables as potential predictors of hip replacement surgery failure. An NN trained on these data predicted the 1-year post-surgical change in the patient's self-assessed pain and physical function scores. Comparison with standard statistical analysis techniques showed superior accuracy of NN-based predictions. Phase II research will generalize the product by adopting the ASTM-E-1238 interface standard for data collection from multiple sources. NN/Expert prediction models will be improved by pooling data from geographically diverse sites and field trial performance to evaluate physician-rated adoption, usefulness, and influence on their actual decision making.
* information listed above is at the time of submission.