Abstract: Disclosed herein are peptides or peptide analogs with multiple amphipathic ?-helical domains that promote lipid efflux from cells via an ABCA1-dependent pathway. Also provided herein are methods of using multi-domain amphipathic ?-helical peptides or peptide analogs to treat or inhibit dyslipidemic disorders. Methods for identifying non-cytotoxic peptides that promote ABCA1-dependent lipid efflux from cells are also disclosed herein.

Abstract: Disclosed herein are peptides or peptide analogs with multiple amphipathic ?-helical domains that promote lipid efflux from cells via an ABCA1-dependent pathway. Also provided herein are methods of using multi-domain amphipathic ?-helical peptides or peptide analogs to treat or inhibit dyslipidemic disorders. Methods for identifying non-cytotoxic peptides that promote ABCA1-dependent lipid efflux from cells are also disclosed herein.

Abstract: This invention relates to methods and compositions for the treatment of sepsis, inflammation or infection. In particular, the invention concerns the use of molecule(s) that target SR-BI, which is also referred to as CLA-1 (SR-BI/CLA-1), to treat sepsis, bacterial and viral infections, and inflammatory diseases. SRB I/CLA-1 ligands contributing to the pathogenesis of disease include LPS, LTA, viral envelope proteins, beta-amyloid, serum Amyloid A and/or heat shock proteins.

Abstract: A method and system for detecting errors in a process such as laboratory analysis of patient specimens and generation of test results is described. The steps of the method include collecting data elements having a range of values from the process. The number of data elements having values within predetermined intervals of the range are then counted. The counts of the data elements are applied as inputs to nodes of a neural network, each count being applied to a node representing the predetermined interval corresponding to the count. Output is then generated from the neural network based on the inputs, the output indicative of whether an error in the process (such as bias error or a precision error) has occurred. If the technology is used with a laboratory instrument, the output is generated in real time and available immediately for automatic or manual correction of the instrument.

Abstract: An apparatus for predicting a residual hormone concentration in a patient after a removal of a portion of the patient's glandular tissue which secretes the hormone, includes an input device constructed to receive a plurality of measured hormone concentrations corresponding to a plurality of human fluid samples taken from a patient at a plurality of sample times, respectively; and further includes a computer processor configured to iteratively calculate a residual hormone concentration. In accordance with one feature of the present invention, the computer processor is configured to generate data denoting a residual amount of glandular tissue that will remain in the patient after the removal of the portion of the patient's glandular tissue; and the apparatus further comprises an output device constructed to output the generated data denoting the residual amount of glandular tissue.