Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: This application relates to methods for assessing patient populations and determining subpopulations vulnerable to viral infection, methods of reducing the risk of infection and/or inducing heterologous immunity to said viral infection.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: Grouping vectors describing functional interactions between compounds and reagents into clusters identifies groups of similar compounds, and may identify drug targets, pathways of action, deleterious toxicities, immune stimulation and evasion potential, or other information regarding interactions between compounds and reagents.

Abstract: This application relates to isolated proteins comprising an amino acid sequence which comprises at least 2 mutations relative to a SARS-CoV-2 Spike protein template sequence, isolated nucleic acid encoding said proteins, and vaccine compositions comprising said proteins.

Abstract: A method including receiving an input of unstructured data; extracting at least one size entity and at least one location entity from the unstructured data using a named entity recognition model; parsing dimensions of the at least one size entity; and outputting structured size data and structured location data.

Abstract: This application relates to methods of treating and/or preventing a coronavirus infection in a subject in need thereof, and treating cardiac injury in said subjects.

Abstract: In one aspect, a method includes receiving a plurality of biological sequence datasets, wherein each of the biological sequence datasets comprises a plurality of biological sequences; identifying a plurality of combinations of biological sequences, wherein each combination comprises one of the plurality of biological sequences from each of the biological datasets; and for each combination of biological sequences: generating a plurality of n-mers for each biological sequence of the combination using a sliding window with length n, comparing the plurality of n-mers for each biological sequence of the combination with the plurality of n-mers for the other biological sequences of the combination, identifying distinctive n-mers for each biological sequence of the combination which are not present among the plurality of n-mers for the other biological sequences of the combination, and determining a number of distinctive n-mers for at least one biological sequence of the combination.

Abstract: Techniques for predicting the immunosuppression status of an individual patient in a computing environment are disclosed. In one particular embodiment, the techniques may be realized as a method comprising receiving a set of medical records associated with a patient, extracting a set of immunosuppression features based on the set of medical records, estimating, a likelihood of immunosuppression of the patient based on the set of immunosuppression features, generating an immunosuppression output comprising one or more features among the set of immunosuppression features and an immunosuppression classification based on the likelihood of immunosuppression of the patient, and displaying the immunosuppression output through at least one interface.

Abstract: This disclosure relates to systems including a plurality of secure enclaves, each in communication with a central node, wherein software within the secure enclave is configured to execute instructions on one or more processors by: receiving input data in an encrypted form; decrypting the input data using one or more cryptographic keys; executing application computing processes to generate output data; generating a proof of execution that proves that the one or more instructions of the one or more application computing processes operated on the received input data; and sending the output data to a central node; wherein software within the central node is configured to execute instructions on one or more processors by receiving the output data from the plurality of secure enclaves; executing application computing processes to apply an aggregate analysis to the output data of each of the secure enclaves; and providing an aggregate output.

Abstract: This application relates to methods for assessing patients suffering from coronavirus infection as responders or a non-responders to an immunomodulating therapy, and methods of treating said subjects.

Abstract: Compositions for use as a vaccine against SARS-CoV-2 infection are disclosed, which comprise either a polypeptide that comprises at least one surge-associated mutation (e.g., deletion) in its amino acid sequence or a nucleic acid (e.g., mRNA) that encodes said polypeptide. Also disclosed are formulations that include these compositions, antibodies or their antigen-biding fragments directed to these polypeptides, methods of making such antibodies, methods of vaccinating subjects against SARS-CoV-2 infection, and methods of selecting an antibody, convalescent plasma, or vaccine against SARS-CoV-2 infection.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: Techniques are provided for computing with private healthcare data. The techniques include a method comprising constructing an isolated memory partition that forms a secure enclave and pre-provisioning software within the secure enclave. The pre-provisioned software is configured to receive at least one of input data or the instructions for the one or more application computing processes in an encrypted form; decrypt the at least one of input data or instructions using one or more cryptographic keys; execute the one or more application computing processes based on the decrypted at least one of input data or instructions to generate output data; generate a proof of execution that indicates that the one or more application computing processes operated on the received input data; encrypt the output data using the one or more cryptographic keys; and provide external access to the encrypted output data and the proof of execution.

Abstract: This application relates to methods for treating or reducing viral infection in a human subject, comprising administering to the subject a pharmaceutical composition comprising a protease inhibitor.

Abstract: Provided herein are methods, systems, and computer program products for the detection of pulmonary hypertension comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.