The invention relates to biomarkers for assessing the disease status of a subject with an antibody-associated autoimmune disease, in particular a neurological disease such as neuromyelitis optica spectrum disorders (NMOSD) or LGI1-antibody encephalitis. The invention in particular looks at the presence or levels of IgM isotype antibodies which recognise a specific antigen in an anti-body-associated autoimmune disease, which is used to determine whether a subject is in a relapse.

The present technology relates generally to determining a risk of recurrence of disease in a cancer patient. In particular, this approach to determining a risk of recurrence involves utilizing standardized quantitative assessments of the level of biomarker expression selected from estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and Ki-67 (Ki67) in a patient's tumor to determine the risk of recurrence, thereby allowing a caretaker to determine the best course of treatment for the patient.

Provided are methods for evaluating the risk of an adverse clinical outcome in a subject, deciding whether to discharge or continue treating a subject (e.g., treatment on an inpatient basis), or to initiate or terminate treatment, selecting a subject for participation in a clinical study, and selecting a therapeutic treatment for a subject that include determining a level of ST2 in a biological sample from the subject and determining a level of galectin-3 in a biological sample from the subject. Kits are also provided that contain an antibody that specifically binds to ST2, an antibody that specifically binds to galectin-3, and instructions for using the kit to evaluate the risk of an adverse clinical outcome in a subject, to decide whether to discharge or continue treating a subject (e.g., treatment on an inpatient basis) or to initiate or terminate treatment, to select a subject for participation in a clinical study, and/or to select treatment for a subject.

The present invention relates to biomarkers associated with breast cancer prognosis. These biomarkers include coding transcripts and their expression products, as well as non-coding transcripts, and are useful for predicting the likelihood of breast cancer recurrence in a breast cancer patient. The present invention also relates to a novel method of identifying intergenic sequences that correlate with a clinical outcome.

Methods and apparatuses for athletic performance and technique monitoring are disclosed. In one example, a sensor output is received associated with a movement of a user torso during a running motion. The sensor output is analyzed to identify an undesirable torso motion.

Described in exemplary embodiments herein are methods, compositions, and kits for diagnosing, prognosing, monitoring, treating and/or preventing a hemopoietic malignancy and/or relapse thereof in a subject. In some embodiments, the methods can include determining an average cellular mass of cells in a sample from the subject and/or detecting one or more molecular signatures in one or more of the cells. In some embodiments, treatment includes administering one or more BCR-ABL tyrosine kinase inhibitors or a pharmaceutical formulation thereof, one or more pre-BCR signaling pathway inhibitors or a pharmaceutical formulation thereof, one or more p38 MAPK inhibitors or a pharmaceutical formulation thereof; or any combination thereof.

A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.

An analysis and display system generates and displays a score indicative of whether cancer will recur in a patient. In a learning phase, a phenomic feature of tumor tissue is measured. A corresponding phenomic feature is defined. The phenomic feature may be measured through image analysis of digital images taken of tissue slices stained with IHC-based stains. A genomic feature of the tissue is also measured. This may entail obtaining a probe count indicative of a degree of expression of a particular gene. A bivariate feature is calculated using both the phenomic and genomic information. A network including the bivariate feature is displayed. In a diagnostic phase, raw phenomic and genomic data is obtained from a tissue sample taken from the patient. From the data, a score for the bivariate feature, and scores for the other features, are calculated. The score is a function of the underlying feature scores.

The invention relates to a method to predict a patient's risk of developing prostate cancer, or to distinguish indolent vs. aggressive cancers among patients suspected of bearing PCa and therefore selected for biopsy, and/or predict the risk of progression of prostate cancer in a patient having undergone prostate cancer treatment, particularly by radical prostatectomy, brachytherapy, and/or external beam radiation therapy, wherein said method comprises detecting/quantifying the level of indoleamine-2,3-dioxygenase (IDO) mRNA or protein in a urine sample obtained from said patient.

This disclosure relates to a composition for diagnosing cancer by using potassium channel proteins; to a kit for diagnosing cancer comprising the composition; and to an information providing method for diagnosing cancer. Specifically, the composition or kit for diagnosing cancer provided in this disclosure may be used to diagnose the onset of cancer regardless of its type, by measuring the expression levels of potassium channels, KCa3.1 channel and KCa2.3 channel, or a regulator thereof from vascular endothelial cells treated with a sample of a subject, or from red blood cells isolated from the subject, and thus can be widely utilized in determining the stages of progression (growth, metastasis, prognosis, and recurrence) of various cancers.