Abstract: The invention relates to a milling bowl, in particular for vertical mills, and to the production of such a milling bowl. Milling bowls of this type have hitherto typically been produced from a cast material as a single piece. Due to the increasing size of said milling bowls, said milling bowls can generally only be transported to the destinations by means of special transport. Therefore, the invention creates a milling bowl consisting of assemblies, wherein a double-walled center part formed from two housing shells is provided between a head flange and a base flange. Said center part forms a cavity, which is filled with a curable casting compound such that a rotationally rigid milling bowl is present after the casting compound has been cured.

Abstract: The invention relates to a milling bowl, in particular for vertical mills, and to the production of such a milling bowl. Milling bowls of this type have hitherto typically been produced from a cast material as a single piece. Due to the increasing size of said milling bowls, said milling bowls can generally only be transported to the destinations by means of special transport. Therefore, the invention creates a milling bowl consisting of assemblies, wherein a double-walled center part formed from two housing shells is provided between a head flange and a base flange. Said center part forms a cavity, which is filled with a curable casting compound such that a rotationally rigid milling bowl is present after the casting compound has been cured.

Abstract: Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts risk of prostate cancer progression in a patient, where the model is based on features including one or more (e.g., all) of preoperative PSA, dominant Gleason Grade, Gleason Score, at least one of a measurement of expression of AR in epithelial and stromal nuclei and a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of average edge length in the minimum spanning tree (MST) of epithelial nuclei, and a morphometric measurement of area of non-lumen associated epithelial cells relative to total tumor area. In some embodiments, the morphometric information is based on image analysis of tissue subject to multiplex immunofluorescence and may include characteristic(s) of a minimum spanning tree (MST) and/or a fractal dimension observed in the images.

Abstract: Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a patient is likely to have a favorable pathological stage of prostate cancer, where the model is based on features including one or more (e.g., all) of preoperative PSA, Gleason Score, a measurement of expression of androgen receptor (AR) in epithelial and stromal nuclei and/or a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of a ratio of area of epithelial nuclei outside gland units to area of epithelial nuclei within gland units, and a morphometric measurement of area of epithelial nuclei distributed away from gland units. In some embodiments, quantitative measurements of protein expression in cell lines are utilized to objectively assess assay (e.g.

Abstract: Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a patient is likely to have a favorable pathological stage of prostate cancer, where the model is based on features including one or more (e.g., all) of preoperative PSA, Gleason Score, a measurement of expression of androgen receptor (AR) in epithelial and stromal nuclei and/or a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of a ratio of area of epithelial nuclei outside gland units to area of epithelial nuclei within gland units, and a morphometric measurement of area of epithelial nuclei distributed away from gland units. In some embodiments, quantitative measurements of protein expression in cell lines are utilized to objectively assess assay (e.g.

Abstract: In general, one aspect of the subject matter described in this specification can be embodied in methods for assessing risk associated with prostate cancer, the methods including the actions of receiving patient data, comparing, with a processor executing code, the patient data to one or more predictive models, the one or more predictive models comprising at least one of (a) a disease progression (DP) model, the DP model being configured to predicts a likelihood of developing significant disease progression, and (b) a favorable pathology (FP) model, the FP model being configured to predict a likelihood of having organ confined, low grade disease in a prostatectomy, and outputting one or more results of the comparison Other embodiments of the various aspects include corresponding systems, apparatus, and computer program products.

Abstract: In general, one aspect of the subject matter described in this specification can be embodied in methods for assessing risk associated with prostate cancer, the methods including the actions of receiving patient data, comparing, with a processor executing code, the patient data to one or more predictive models, the one or more predictive models comprising at least one of (a) a disease progression (DP) model, the DP model being configured to predicts a likelihood of developing significant disease progression, and (b) a favorable pathology (FP) model, the FP model being configured to predict a likelihood of having organ confined, low grade disease in a prostatectomy, and outputting one or more results of the comparison Other embodiments of the various aspects include corresponding systems, apparatus, and computer program products.

Abstract: A method of in situ immunohistochemical analysis of a biological sample is provided. The method allows for the multiplex and simultaneous detection of multiple antigens, including multiple nuclear antigens, in a tissue sample.

Abstract: Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a patient is likely to have a favorable pathological stage of prostate cancer, where the model is based on features including one or more (e.g., all) of preoperative PSA, Gleason Score, a measurement of expression of androgen receptor (AR) in epithelial and stromal nuclei and/or a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of a ratio of area of epithelial nuclei outside gland units to area of epithelial nuclei within gland units, and a morphometric measurement of area of epithelial nuclei distributed away from gland units. In some embodiments, quantitative measurements of protein expression in cell lines are utilized to objectively assess assay (e.g.

Abstract: A method of in situ immunohistochemical analysis of a biological sample is provided. The method allows for the multiplex and simultaneous detection of multiple antigens, including multiple nuclear antigens, in a tissue sample.