Abstract: Kits, methods, systems and software are provided to predict personalized approaches for increasing butyrate production. Such approaches may include diet and/or supplemental interventions. Simulations may be used to predict—for each of one or more background diet—an extent to which a supplemental intervention may improve predicted butyrate production for an individual (e.g., absolutely or relative to a population corresponding to the diet). Disclosed techniques may then be used to identify one or more select diets/supplemental inventions or to rank or one or more select diets/supplemental inventions for a particular individual.

Abstract: Provided are computer-implemented methods, systems and products of determining omic body index and class of a subject.

Abstract: Peptide-major histocompatibility (MHC) Class I nucleic acids and proteins are provided. Methods of their use, for example in methods of identifying antigen-specific T cells and adoptive cell therapy, are also provided.

Abstract: Disclosed herein are novel compounds for treating apicomplexan parasite related disorders, methods for their use; cell line and non-human animal models of the dormant parasite phenotype and methods for their use in identifying new drugs to treat apicomplexan parasite related disorders, and biomarkers to identify disease due to the parasite and its response to treatment.

Abstract: Predicting viability and treatment of disease agents is described herein. In an example, a system accesses a disease agent transcriptome data of a disease agent. The system generates a disease agent viability score by applying a classifier to the disease agent transcriptome. The classifier defines a universal transcriptome signature for a viability of the disease agent in different host-relevant contexts. The system generates a viability state of the disease agent by determining a deviation of the disease agent viability score from a viability threshold of the universal transcriptome signature for viability and determines a treatment recommendation based on the viability state of the disease agent. The system outputs the treatment recommendation.

Abstract: Predicting a weight loss potential from gut microbiome data is described herein. In an example, a system accesses gut microbiome metagenomic sequence data for a subject and determines a measurement of one or more weight loss features from the gut microbiome metagenomic sequence data. The one or more weight loss features define a gut microbiome signature for weight loss response for the subject independent of a body mass index of the subject. The system determines a weight loss potential for the subject based on a comparison of the measurement of the subject to a plurality of reference measurements of the one or more weight loss features for a reference population showing variable weight loss responses. The system outputs the weight loss potential for the subject.

Abstract: Predicting therapy from gut compositional data is described herein. In an example, a system accesses gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject. The system generates a gut microbiome signature for a safety and an efficacy of a statin therapy for the subject by applying a classifier to the gut compositional data. The safety of the statin therapy is characterized by an insulin resistance of the subject and the efficacy of the statin therapy is characterized by a blood hydroxymethylglutarate level of the subject. The system determines a recommended therapy for the subject based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject. The recommended therapy is selected from a statin therapy intensity, a probiotic therapy, a prebiotic therapy, or a combination thereof. The system outputs the recommended therapy.

Abstract: The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.

Abstract: Machine-learning processing of aggregate data including record-size data to predict failure probability is described herein. In an example, a system identifies electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object. The system generates a record-size metric that characterizes a size of the electronic data and determines a physical attribute of the given subject or the given object. The system generates a physical-attribute metric based on the physical attribute, generates an input data set that includes the record-size metric and the physical-attribute metric, and generates a failure probability across a given time period and for the given subject or the given object by processing the input data set using a trained machine-learning model. The system determines that an alert condition is satisfied based on the failure probability and outputs an alert representing the failure probability.

Abstract: Peptide-major histocompatibility (MHC) Class II nucleic acids and proteins are provided. Methods of their use, for example in methods of identifying antigen-specific T cells and adoptive cell therapy, are also provided.

Abstract: Reagents and methods to use said reagents for detection of markers of various aspects of Lyme disease have been identified in biological fluids thus permitting diagnosis or classification of Lyme disease subjects as well as indicating suitable methods of treatment.

Abstract: Disclosed are compounds, compositions, and methods involving cyclic peptides that can bind to KRAS (G12D) oncogenic protein. For example, disclosed are cyclic peptides that selectively bind KRAS (G12D) oncogenic protein. Also disclosed are methods of inhibiting KRAS (G12D) oncogenic protein in a cancer cell expressing KRAS (G12D) oncogenic protein. In some forms, the method comprises incubating the cancer cell with any one or more of the disclosed cyclic peptides. In some forms, the method comprises bringing into contact the cancer cell with any one or more of the disclosed cyclic peptides.

Abstract: Methods for diagnosing and/or predicting presence of sepsis in a subject using a gene signature of three or more genes are provided. Also provided are sets containing specific binding molecules for each of the three or more genes, and kits containing such sets.

Abstract: A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated.

Abstract: The invention provides an unbiased method to assess the binding of a test compound to a multiplicity of proteins in the same sample, including samples from living cells by applying the unbiased determination technique of SWATH-MS or the biased technique of SRM-MS to a thermal shift assay to evaluate drug target interactions. In addition, the results created by SWATH-MS can be analyzed by SRM-MS in a biased manner to assess the binding of a test compound to a multiplicity of proteins in the same sample, including samples from living cells.

Abstract: The invention provides an unbiased method to assess the binding of a test compound to a multiplicity of proteins in the same sample, including samples from living cells by applying the unbiased determination technique of SWATH-MS or the biased technique of SRM-MS to a thermal shift assay to evaluate drug target interactions. In addition, the results created by SWATH-MS can be analyzed by SRM-MS in a biased manner to assess the binding of a test compound to a multiplicity of proteins in the same sample, including samples from living cells.

Abstract: The invention provides an unbiased method to assess the binding of a test compound to a multiplicity of proteins in the same sample, including samples from living cells by applying the unbiased determination technique of SWATH-MS or the biased technique of SRM-MS to a thermal shift assay to evaluate drug target interactions. In addition, the results created by SWATH-MS can be analyzed by SRM-MS in a biased manner to assess the binding of a test compound to a multiplicity of proteins in the same sample, including samples from living cells.

Abstract: Practical assays to identify compounds that overcome the resistance of M. tuberculosis to bedaquiline are based on transcription factors Rv0324 and Rv0880 shown to mediate this resistance.

Abstract: The present disclosure describes systems and methods to elucidate unknown relationships and interactions between and among complex biological systems and components thereof. The systems and methods can inform clinical interventions in individuals before phenotypes of an adverse condition emerge.

Abstract: Crosslinking molecules that permit efficient identification of specifically associated proteins and the sites of crosslinking in biological or other samples include two cleavable bonds that are cleavable under the same conditions as peptide bonds in mass spectrometric determinations. Sets of the invention crosslinkers may be provided with isobaric labels containing reporter ions of differing molecular weights to permit relative quantitation of crosslinked protein pairs.