Abstract: The present invention provides a method of identifying and isolating a set of monoclonal and polyclonal antibodies for use in immunometric assays for the detection of UCN III peptide, and the development of a two-site immunoassay for UCN III peptide. Monoclonal antibodies (mAb) have been affinity purified and used to develop a two-site immunometric assay comprising a mAb selected from the list comprising 2F7, 4E3, 4D3, 6D1, 1G10, 2E7, 4F3, 4C3, 5E11, 1A9, 4C4, 4B4, 2G7, 2A4, 1B9, 3H11, 5F2, 4G2, and 2E3 used for capture and a polyclonal antibody used for detection. The immunometric assay of the present invention comprise a method to identify and isolate specific antibodies or fragments thereof establishing the presence and/or amount of a UCN III peptide and detecting the specific binding necessary for determining a sample's UCN III peptide level for correlation with a diagnosis wherein the UCN III peptide level is used to determine whether the subject suffers from OSA.

Abstract: The invention provides personalized medicine management software for determining a series of recommended doses of a medication for a patient. The software contains code to receive information regarding a combination of at least one genetic factor and personal attributes for the patient that are predictive of the patient's reaction to a series of doses of the medication. Using a predictive mathematical model specific to the medication, the code calculates the series of recommended doses specific to the patient's genetic factor and personal attributes and specific to the medication. The series of recommended doses is outputted. In preferred embodiments, the output is the form of an interactive display. The interactive display permits a user, typically a health care professional, to input actual doses and actual patient responses. The subsequent series of recommended doses is preferably then adjusted, in real time, to account for the actual doses and actual patient responses.

Abstract: The invention provides personalized medicine management software for determining a series of recommended doses of a medication for a patient. The software contains code to receive information regarding a combination of at least one genetic factor and personal attributes for the patient that are predictive of the patient's reaction to a series of doses of the medication. Using a predictive mathematical model specific to the medication, the code calculates the series of recommended doses specific to the patient's genetic factor and personal attributes and specific to the medication. The series of recommended doses is outputted. In preferred embodiments, the output is the form of an interactive display. The interactive display permits a user, typically a health care professional, to input actual doses and actual patient responses. The subsequent series of recommended doses is preferably then adjusted, in real time, to account for the actual doses and actual patient responses.