This disclosure describes a technique for performing random access in a pool of polynucleotides by using one unique primer and one homopolymer primer to selectively amplify some but not all of the polynucleotides in the pool. The polynucleotides are synthesized by a template independent polymerase such as terminal deoxynucleotide transferase (TdT) rather than by phosphoramidite synthesis. Enzymatic synthesis efficiently creates homopolymer sequences through unregulated synthesis. Use of one homopolymer primer instead of two unique primers decreases the complexity, time, and cost of synthesizing the polynucleotides. Use of a unique primer provides a sequence that can be varied to uniquely identify multiple different groups of polynucleotides. This enables random access by polymerase chain reaction (PCR) amplification while still benefitting from the efficiency of homopolymer synthesis. The polynucleotides may include payload regions that use a sequence of nucleotides to encode digital data.

In accordance with some embodiments, systems, methods, and media for automatically predicting a classification of incidental adrenal tumors based on clinical variables and urinary steroid levels are provided. In some embodiments, the system comprises: a processor programmed to: generate a feature vector including clinical variables and biomarker levels associated with the patient presenting with an unclassified adrenal mass; provide the feature vector to a machine learning model trained using a labeled feature vectors associated patients having adrenal masses classified as benign, adrenal cortical carcinoma, or another malignant adrenal mass; receive, from the trained machine learning model, an output indicative of a classification of the unclassified adrenal mass; and cause information indicative of the classification to be presented to a user to aid the user in classification of the unclassified adrenal mass.

In accordance with some embodiments, systems, methods, and media for automatically predicting a classification of incidental adrenal tumors based on clinical variables and urinary steroid levels are provided. In some embodiments, the system comprises: a processor programmed to: generate a feature vector including clinical variables and biomarker levels associated with the patient presenting with an unclassified adrenal mass; provide the feature vector to a machine learning model trained using a labeled feature vectors associated patients having adrenal masses classified as benign, adrenal cortical carcinoma, or another malignant adrenal mass; receive, from the trained machine learning model, an output indicative of a classification of the unclassified adrenal mass; and cause information indicative of the classification to be presented to a user to aid the user in classification of the unclassified adrenal mass.

A system for generating an immune protocol for identifying and reversing immune disease is presented. The system comprising a computing device configured to receive at least an immune biomarker from a graphical user interface, determine a current immunological state of the user including an immune dysfunction as a function of the immune biomarker and an immune profile, assign an immune category to the immune profile as a function of the current immunological state, identify an effect on the immune profile for each nutritional element of a plurality of nutritional elements, determine at least a nutritional element that contributes to the immune category as a function of an immune machine-learning model and the nutritional input, identify a plurality of protocol elements, wherein each protocol element contains at least a nutrient amount intended to address the immunological dysfunction, and generate an immune protocol as a function of the plurality of protocol elements.

A system for generating an immune protocol for identifying and reversing immune disease is presented. The system comprising a computing device configured to receive at least an immune biomarker from a graphical user interface, determine a current immunological state of the user including an immune dysfunction as a function of the immune biomarker and an immune profile, assign an immune category to the immune profile as a function of the current immunological state, identify an effect on the immune profile for each nutritional element of a plurality of nutritional elements, determine at least a nutritional element that contributes to the immune category as a function of an immune machine-learning model and the nutritional input, identify a plurality of protocol elements, wherein each protocol element contains at least a nutrient amount intended to address the immunological dysfunction, and generate an immune protocol as a function of the plurality of protocol elements.

The present disclosure describes methods and systems for predicting if a subject has an increased risk of having or developing venous thromboembolism, including prior to the detection of symptoms thereof and/or prior to onset of any detectable symptoms thereof. The present disclosure also describes a method of generating a model for predicting venous thromboembolism.

The present disclosure describes methods and systems for predicting if a subject has an increased risk of having or developing venous thromboembolism, including prior to the detection of symptoms thereof and/or prior to onset of any detectable symptoms thereof. The present disclosure also describes a method of generating a model for predicting venous thromboembolism.

The present invention is directed to methods for detecting a colon cancer, methods for determining whether a colon cancer is stable or progressive, methods for determining a risk for disease relapse, and methods for determining a response by a subject having a colon cancer to a therapy.

A method and system comprising storing a set of user application programs each requiring an application specific subset of data to perform application processes and generate a respective genomic variant characterization for each of a plurality of patients with cancerous cells and receiving cancer treatment. The method including, obtaining clinical records data including cancer related information, generating genomic sequencing data for the patient's cancerous cells and normal cells, shaping at least a subset of the genomic sequencing data to generate system structured data. Storing the system structured data in a first database, selecting the application specific data from the first database, storing the application specific data in a second database for application program interfacing, receiving the respective genomic variant characterization from the user application program, and storing the genomic variant characterization from the user application program in a third database.

The invention relates to a method for determining whether a subject with an infection has a viral infection. The invention further relates to method for determining the cellular immune response to a viral infection or a vaccine. The methods may be performed on a blood sample obtained from a subject, and is based on the finding that specific cellular signaling pathways are active. The invention further relates to components for performing the methods and use of those components in a method of diagnosis.