Abstract: This invention provides primers and primer combinations that function efficiently in LAMP-based and/or other amplification systems and are useful for the diagnosis and subsequent treatment of acute infectious disease.

Abstract: Systems, methods, compositions, apparatuses, and kits for determining the responsiveness of subjects with inflammatory bowel disease (IBD) to anti-TNF alpha therapy using biological samples obtained from the subjects, are provided herein. The disclosed methods and compositions involve biomarkers identified from the application of a machine learning workflow to TNF alpha response training data from biological samples. The biomarkers allow the calculation of a score that can be used to determine the TNF alpha responder status of the subjects.

Abstract: Techniques for treating a detrimental physical condition are disclosed. In one embodiment, a method includes processing one or more samples obtained from the individual through use of one or more microfluidic chips to provide data indicating one or more constituents present within the one or more samples, and processing the data to determine a quantity of at least one constituent that indicates the detrimental physical condition of the individual. Next, the method determines at least one dosage of at least one treatment agent for mitigation of the detrimental physical condition based on the quantity of the at least one constituent that indicates the detrimental physical condition, and facilitates an administration of the at least one dosage for mitigation of the detrimental physical condition.

Abstract: This invention provides triple primer and probe combinations that function efficiently in PCR-based amplification systems using undigested genomic DNA and are useful for the diagnosis and subsequent treatment of systemic inflammatory disease.

Abstract: The present disclosure relates to methods and devices that may be used to separate components from one or more samples.

Abstract: Systems and methods for subject clinical condition evaluation using a plurality of modules are provided. Modules comprise features whose corresponding feature values associate with an absence, presence or stage of phenotypes associated with the clinical condition. A first dataset is obtained having feature values, acquired through a first technical background from respective subjects in transcriptomic, proteomic, or metabolomic form, for at least a first of the plurality of modules. A second training dataset is obtained having feature values, acquired through a technical background other than the first technical background, from training subjects of the second dataset, in the same form as for the first dataset, of at least the first module. Inter-dataset batch effects are removed by co-normalizing feature values across the training datasets, thereby calculating co-normalized feature values used to train a classifier for clinical condition evaluation of the test subject.