The invention relates to a new potency assay for characterizing the quality and activity of an immune cell expressing a chimeric antigen receptor, the kit to carry out this assay and uses thereof.

Disclosed is a method of isolating extracellular vesicles and identifying membrane proteins therefrom. The method includes providing human plasma and/or serum; separating lipoproteins and extracellular vesicles from the human plasma and/or serum by a density gradient preparation, collecting the extracellular vesicles from the separated lipoproteins and extracellular vesicles; isolating and purifying the collected extracellular vesicles by using size exclusion chromatography; treating the isolated and purified extracellular vesicles with an aqueous solution to obtain membranes of the extracellular vesicles, wherein the aqueous solution has a pH in a range of 9 to 14; adding salt in a concentration range between 0.5-2.0M to the aqueous solution; isolating the membranes from the treated extracellular vesicles and identifying proteins on the isolated membranes by employing mass spectrometry.

The invention relates to methods for detecting and/or characterising a nanovesicle in a sample or a method of detecting a target that is bound or associated with said nanovesicle, wherein the sample is brought into contact with nanoparticles that are capable of binding on the surface of nanovesicle and form, in situ, a nanoshell that surround said nanovesicle. In a preferred embodiment, the nanovesicle is exosome labelled with fluorescent probes and the nanoparticles are gold nanoparticles (AuNP). The invention also relates to a kit or microfluidic chip for performing such methods, as well as a method of determining the prognosis of a cancer in a subject by performing such methods.

Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

Disclosed is a T cell receptor (TCR) having antigenic specificity for an HLA-A2-restricted epitope of human papillomavirus (HPV) 16 E6, E6.sub.29-38. Related polypeptides and proteins, as well as related nucleic acids, recombinant expression vectors, host cells, and populations of cells are also provided. Antibodies, or an antigen binding portion thereof, and pharmaceutical compositions relating to the TCRs of the invention are also provided. Also disclosed are methods of detecting the presence of a condition in a mammal and methods of treating or preventing a condition in a mammal, wherein the condition is cancer, HPV 16 infection, or HPV-positive premalignancy.

Introduced here is an approach to improving the automatic identification of hematological diseases using computer-implemented models that are trained to rapidly distinguish between different collections of immunophenotypes that represent different disease types or disease states. Understanding the different patterns of immunophenotype collections contained in a given sample may permit a proposed diagnosis for a given hematological disease to be produced for the corresponding patient. For example, the proposed diagnoses may be output by a classification model based on the distribution of immunophenotypes across the given sample.

A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Provided are methods for determining the amount of thyroglobulin in a sample using various purification steps followed by mass spectrometry. The methods generally involve purifying thyroglobulin in a test sample, digesting thyroglobulin to form peptide T129, purifying peptide T129, ionizing peptide T129, detecting the amount of peptide T129 ion generated, and relating the amount of peptide T129 ion to the amount of thyroglobulin originally present in the sample.

The invention relates to a collection of signature peptides representing at least 10 proteins for use in cancer diagnosis and/or prognosis, to an artificial protein comprising signature peptides representing at least 10 proteins and to a nucleic acid construct encoding for such an artificial protein. The invention further relates to a collection of at least 10 proteins for use in cancer diagnosis and/or prognosis. Additionally, the invention relates to a method for cancer diagnosis and/or prognosis comprising the step of analyzing at least 10 proteins in a urine sample of a subject. Finally, the invention relates to an immunoassay product comprising antibodies for detecting at least 10 proteins.

Provided herein are methods, compositions, kits, and systems for diagnosing, predicting, and treating gastric cancer for a subject based on the presence and level of antibodies against particular H. pylori proteins in a biological sample obtained from the subject. In particular, provided herein are methods for identifying a subject having increased risk of developing gastric cancer, methods for detecting gastric cancer in a subject, methods for determining an H. pylori antibody signature comprising antibodies, contained in a biological sample from a subject, that specifically bind to immobilized H. pylori antigens, and kits comprising components and instructions for performing the methods of this disclosure.