This document relates to methods and materials for assessed, monitored, and/or treated mammals (e.g., humans) having cancer. For example, methods and materials for identifying a mammal as having cancer (e.g., a localized cancer) are provided. For example, methods and materials for assessing, monitoring, and/or treating a mammal having cancer are provided.

A method for making a modified release composition, comprising: selecting a desired active agent and polymer matrix for formulating into a modified release composition; assessing degradation effect on release of the active agent from the composition including plotting polymer molecular weight (M.sub.wr) at onset of active agent release vs. active agent molecular weight (M.sub.wA); predicting performance of multiple potential formulations for the composition based on the degradation assessment and average polymer matrix initial molecular weight (M.sub.wo) to define a library of building blocks; determining the optimal ratio of the building blocks to satisfy a specified release profile; and making a modified release composition based on the optimal ratio determination.

A method for making a modified release composition, comprising: selecting a desired active agent and polymer matrix for formulating into a modified release composition; assessing degradation effect on release of the active agent from the composition including plotting polymer molecular weight (M.sub.wr) at onset of active agent release vs. active agent molecular weight (M.sub.wA); predicting performance of multiple potential formulations for the composition based on the degradation assessment and average polymer matrix initial molecular weight (M.sub.wo) to define a library of building blocks; determining the optimal ratio of the building blocks to satisfy a specified release profile; and making a modified release composition based on the optimal ratio determination.

The technology disclosed relates to training a pathogenicity predictor. In particular, the technology disclosed relates to accessing a gapped training set that includes respective gapped protein samples for respective positions in a proteome, accessing a non-gapped training set that includes non-gapped benign protein samples and non-gapped pathogenic protein samples, generating respective gapped spatial representations for the gapped protein samples, and generating respective non-gapped spatial representations for the non-gapped benign protein samples and the non-gapped pathogenic protein samples, training a pathogenicity predictor over one or more training cycles and generating a trained pathogenicity predictor, wherein each of the training cycles uses as training examples gapped spatial representations from the respective gapped spatial representations and non-gapped spatial representations from the respective non-gapped spatial representations, and using the trained pathogenicity classifier to determine pathogenicity of variants.

Provided herein are structures and methods for detecting one or more analyte molecules present in a sample. In some embodiments, the one or more analyte molecules are detected using one or more supramolecular structures. In some embodiments, the supramolecular structures are bi-stable, wherein the supramolecular structures shift from an unstable state to a stable state through interaction with one or more analyte molecules from the sample. In some embodiments, the stable state supramolecular structures are configured to provide a signal for analyte molecule detection and quantification.

Provided herein are structures and methods for detecting one or more analyte molecules present in a sample. In some embodiments, the one or more analyte molecules are detected using one or more supramolecular structures. In some embodiments, the supramolecular structures are bi-stable, wherein the supramolecular structures shift from an unstable state to a stable state through interaction with one or more analyte molecules from the sample. In some embodiments, the stable state supramolecular structures are configured to provide a signal for analyte molecule detection and quantification.

This disclosure describes an efficient method to copy all polynucleotides encoding digital data of digital files in a polynucleotide storage container while maintaining random access capabilities over a collection of files or data items in the container. The disclosure further describes a process whereby random-access and sequencing of the polynucleotides are combined in a single step.

As disclosed herein a precision based immunomolecular augmentation (PBIMA) high specificity patient profiling networked computer system, rapid therapeutic vaccine design method, and personalized vaccine, which utilizes immuno-molecular biopathway HLA affinity mapping and selection prediction ranking tools. This PBIMA approach comprises: Strategic-Selection, Molecular-Mapping, Antigen-Alignment, Receptor-Recognition, and Tactical Technology (SMART). The platform obtains data from a patient’s genes and proteins as input. NGS data, including WES, WGS, ctDNA and cfDNA, RNAseq uses as input. PBIMA comprises a gene-protein-cell Cloud-based sequence editing interface to select the high confidence peptides. The PBIMA vaccine is a solution-based multi-purpose vaccine design strategy. PBIMA technology can produce therapeutic vaccines for cancer, autoimmune, neurodegenerative, inflammation-driven disease, and novel pathogen infection treatment. PBIMA therapeutic design is multi-mechanistic and broad-spectrum.

As disclosed herein a precision based immunomolecular augmentation (PBIMA) high specificity patient profiling networked computer system, rapid therapeutic vaccine design method, and personalized vaccine, which utilizes immuno-molecular biopathway HLA affinity mapping and selection prediction ranking tools. This PBIMA approach comprises: Strategic-Selection, Molecular-Mapping, Antigen-Alignment, Receptor-Recognition, and Tactical Technology (SMART). The platform obtains data from a patient’s genes and proteins as input. NGS data, including WES, WGS, ctDNA and cfDNA, RNAseq uses as input. PBIMA comprises a gene-protein-cell Cloud-based sequence editing interface to select the high confidence peptides. The PBIMA vaccine is a solution-based multi-purpose vaccine design strategy. PBIMA technology can produce therapeutic vaccines for cancer, autoimmune, neurodegenerative, inflammation-driven disease, and novel pathogen infection treatment. PBIMA therapeutic design is multi-mechanistic and broad-spectrum.

Disclosed is a registry system, including member institutions, in which transfusion donors and recipients are registered following genotyping, which would typically take place in a member institution, or a member institution would have access to the genotyping information, if performed outside. The registry database can be accessed and searched by members seeking samples of particular type(s). Systems are disclosed for maintaining economic viability of genotyping in connection with transfusions, by maximizing the number of units placed with the minimal number of candidate donors typed. Genotyping of potential donors, and product supply, is matched to forecasted demand. Genotyping can also be limited to the more clinically relevant markers. The registry system can also be integrated with one format of assay which generates an image for analysis, whereby the imaged results can be analyzed and redacted by experts in a central location, and then transmitted back to the patient or their representative.