Abstract: Described and provided herein are methods and systems useful for the analysis of polynucleotides and/or detection/quantification of cancer risk identifiers for cancer screening. For example, in certain instances, the methods and systems described are useful for determining changes in the rates of genetic change (e.g., mutation accumulation as a function of time) and/or epigenetic changes (e.g., methylation status change as a function of time), which can be used to profile the risk that an individual has cancer or is likely to develop cancer.

Abstract: Herein is reported a class of chiral compounds with paradoxical effects on the androgen receptor (AR). The (R)-enantiomers behave like classical anti-androgens while the (S)-enantiomers activate AR signaling. In castration-resistant prostate cancer, the change during the course of therapy to growth in the presence of AR targeted therapeutics, a harbinger of progression to lethal disease, is commonly attributed to acquired mutations of the AR-ligand binding domain. This is the first report of an antagonist-agonist duality solely due to structural enantiomerism, without any modification to the AR binding site.

Abstract: Herein is reported a class of chiral compounds with paradoxical effects on the androgen receptor (AR). The (R)-enantiomers behave like classical anti-androgens while the (S)-enantiomers activate AR signaling. In castration-resistant prostate cancer, the change during the course of therapy to growth in the presence of AR targeted therapeutics, a harbinger of progression to lethal disease, is commonly attributed to acquired mutations of the AR-ligand binding domain. This is the first report of an antagonist-agonist duality solely due to structural enantiomerism, without any modification to the AR binding site.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: Herein is reported a class of chiral compounds with paradoxical effects on the androgen receptor (AR). The (R)-enantiomers behave like classical anti-androgens while the (S)-enantiomers activate AR signaling. In castration-resistant prostate cancer, the change during the course of therapy to growth in the presence of AR targeted therapeutics, a harbinger of progression to lethal disease, is commonly attributed to acquired mutations of the AR-ligand binding domain. This is the first report of an antagonist—agonist duality solely due to structural enantiomerism, without any modification to the AR binding site.

Abstract: Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.

Abstract: Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.

Abstract: The present application describes the use of ErbB antagonist, especially ErbB2 antibodies such as rhuMAb 2C4, for treating pain.

Abstract: This invention relates to methods and kits for positive selection of species of interest based on peptide/protein sequence from a biological sample. The species of interest may be proteins and/or peptides of interest which may be placed through a mass spectrometer to obtain a blood peptide/protein signature. The blood peptide/protein signature may be used in proteomic analysis. The techniques include but are not limited to the use of collectors comprising nucleic acid molecules to extract a composition that has a lower concentration of a high abundance species of interest from a sample. This limits the level of influence that any collectors species may have on the results of a mass spectra.

Abstract: The present application describes the use of ErbB antagonist, especially ErbB2 antibodies such as rhuMAb 2C4, for treating pain.

Abstract: Described herein are genes whose expression are up-regulated or down-regulated in prostate cancer. Also described are such genes whose expression is further up-regulated or down-regulated in drug-resistant prostate cancer cells. Related methods and compositions that can be used for diagnosis and treatment of prostate cancer are disclosed. Also described herein are methods that can be used to identify modulators of prostate cancer.

Abstract: The present invention relates to a system and method for mass spectrometry (100) that allows for bi-directional introduction of collections of charged particles into the magnetic field of a mass spectrometer. More particularly, the present invention includes a system for mass spectrometry (100) (e.g., an FTMS mass spectrometer) with a cylindrical magnet (101) configured to receive and measure the cyclotron frequencies (104) of charged particles that are introduced (102, 103) into the cylindrical magnet (101) from either of the two axial ends thereof. Methods of the invention relate to performing mass spectrometry analysis on collections of charged particles that are introduced (102, 103), serially, simultaneously or both, into a cylindrical magnet (101) from opposing axial ends thereof. The present invention exhibits significantly increased magnet throughput relative to currently available devices, by allowing flow in the opposite direction to a second detector, e.g.

Abstract: Described herein are methods for treating cancer and other disease conditions in individuals who have either developed a resistance to conventional tyrosine kinase inhibitor (TKI) therapy or who are non-responsive ab initio to conventional TKI therapy. In various embodiments, the methods include administering to a patient a resistance-surmounting quantity of a TKI on a weekly or semi-weekly basis. Alternate embodiments of the present invention include a diagnostic method for assessing an individual's probability of being resistant to TKI therapy, based upon an expression level of epithelial membrane protein-1 (EMP-1); one of the genes believed to be responsible for TKI resistance. The methods of the present invention may be particularly useful in the treatment of lung, breast, prostate, ovarian, brain and colon cancers. The methods of the present invention may be effective in blocking the HER-2 kinase domain either in addition to or in lieu of blocking the EGFR kinase domain.

Abstract: This invention relates to methods and kits for positive selection of species of interest based on peptide/protein sequence from a biological sample. The species of interest may be proteins and/or peptides of interest which may be placed through a mass spectrometer to obtain a blood peptide/protein signature. The blood peptide/protein signature may be used in proteomic analysis. The techniques include but are not limited to the use of collectors comprising nucleic acid molecules to extract a composition that has a lower concentration of a high abundance species of interest from a sample. This limits the level of influence that any collectors species may have on the results of a mass spectra.

Abstract: The present invention relates to a system and method for mass spectrometry (100) that allows for bi-directional introduction of collections of charged particles into the magnetic field of a mass spectrometer. More particularly, the present invention includes a system for mass spectrometry (100) (e.g., an FTMS mass spectrometer) with a cylindrical magnet (101) configured to receive and measure the cyclotron frequencies (104) of charged particles that are introduced (102, 103) into the cylindrical magnet (101) from eith of the two axial ends thereof. Methods of the invention relate to performing mass spectrometry analysis on collections of charged particles that are introduced (102, 103), serially, simultaneously or both, into a cylindrical magnet (101) from opposing axial ends thereof. The present invention exhibits significantly increased magnet throughput relative to currently available devices, by allowing flow in the opposite direction to a second detector, e.g.