Abbott Laboratories Corporation

Abbott Molecular Inc.

Eric L Russell

Julian D Gordon

Stephen D Stroupe

John C Russell

Tracey L Colpitts

Maurice Cohen

Paula N Friedman

Michael R Klass

Steven C Hodges

Patricia A Billing-Medel

Jon D Kratochvil

Javier Ramirez

Stephen Frost

Bhawani Singh

Yongli H Xiong

The present invention relates to rapid, sensitive methods for determining whether a subject is at risk of developing lung cancer or has lung cancer based on certain combinations or biomarkers or biomarkers and biometric parameters.

Methods and marker combinations for screening for predisposition to lung cancer

Polypeptides and polynucleotides useful for detecting, diagnosing, staging, monitoring, prognosticating, in vivo imaging, preventing or treating, or determining the predisposition of an individual to diseases and conditions of the urinary tract, such as urinary cancer, are described. These sequences are derived from keratin/cytokeratin, CAS, or mat-8 polypeptides and polynucleotides. Also provided are antibodies which specifically bind to keratin/cytokeratin, CAS, or mat-8-encoded polypeptides or proteins, which molecules are useful for the therapeutic treatment of urinary tract diseases, tumors or metastases.

Reagents and methods useful for detecting diseases of the urinary tract

Embodiments described herein provide methods of analysis of a reaction, such as an agglutination reaction producing an agglutination pattern, and the like. In one method, an image of the agglutination pattern is captured. The image captured is digitized to form a digital image comprising a pixel. Roughness is measured, reflecting pixel local environment, at each pixel comprising a region of interest of the digital image of the agglutination pattern. At least one of classification and quantification of the image captured of the agglutination pattern is performed based on the measured roughness. In another embodiment, roughness is derived using a shared weights neural network approach, the roughness reflecting pixel local environment at each pixel comprising a region of interest of the digital image of the agglutination pattern. At least one of classification and quantification of the image captured of the agglutination pattern is performed based on the derived roughness. In a further embodiment, a feature is derived using a shared weights neural network approach, the feature reflecting pixel local environment at each pixel comprising a region of interest of the digital image of the agglutination pattern. At least one of classification and quantification of the image captured of the agglutination pattern is performed based on the derived feature.

Quantative agglutination reaction analysis method

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Gurnee, IL, United States of America

Eric L Russell