DR. MICHAEL FURMAN GREENE, MD
Medical Practice at Fruit St, Boston, MA

2013020, Aug 8, 2013

Feb 10, 2011

13/578294

Lawreen Connors - Woburn MA, US
Michael J. Greene - Allston MA, US

TRUSTEES OF BOSTON UNIVERSITY - Boston MA

G01N 33/68

435 792, 436501, 435 71, 422 69, 4352872

The present invention relates generally to diagnostic methods, systems, assays and kits for identification of subjects with cardiac amyloid deposits, where a low level of clusterin protein in a peripheral fluid sample, e.g., a serum sample from the subject, indicates the subject likely has cardiac amyloid deposits. Other aspects relate to methods of treatment of diseases or disorders characterized by cardiac amyloid deposits and transthyretin (TTR) amyloidosis, and more particularly to methods of treatment of cardiac-related amyloidosis and cardiac amyloid deposits in subjects with familial transthyretin (TTR), senile systemic amyloidosis (SSA), or familial amyloidodic polyneuropathy (FAP), or immunoglobulin light chain (AL) amyloidosis. Other aspects relate to methods and compositions comprising clusterin (CLU) or a clusterin agent (e.g. an agonist of clusterin activity or a biologically active fragment or derivative thereof), and their use in methods to treat a disease or disorder characterized by transthyretin (TTR) amyloidosis, e.g. senile systemic amyloidosis (SSA) or familial amyloidodic polyneuropathy (FAP), and their use in methods to treat amyloidotic cardiomyopathy associated with transthyretin (TTR) amyloidosis.

2014005, Feb 27, 2014

Jul 24, 2013

13/987437

Michael F. Greene - Boston MA, US
John R. Lucker - Simsbury CT, US
Steven E. Ellis - Linthicum Heights MD, US
James C. Guszcza - Santa Monica CA, US
Steven L. Berman - Havertown PA, US
Amin Torabkhani - New York NY, US

G06Q 40/08

705 4

An unsupervised statistical analytics approach to detecting fraud utilizes cluster analysis to identify specific clusters of claims or transactions for additional investigation, or utilizes association rules as tripwires to identify outliers. The clusters or sets of rules define a “normal” profile for the claims or transactions used to filter out normal claims, leaving “not normal” claims for potential investigation. To generate clusters or association rules, data relating to a sample set of claims or transactions may be obtained, and a set of variables used to discover patterns in the data that indicate a normal profile. New claims may be filtered, and not normal claims analyzed further. Alternatively, patterns for both a normal profile and an anomalous profile may be discovered, and a new claim filtered by the normal filter. If the claim is “not normal” it may be further filtered to detect potential fraud.