6 results about "Transplant Donors" patented technology

Method and systems for sorting potential hematopoietic stem cell transplant (HSCT) donors as a donor or non-donor using immunophenotyping of blood samples. The methods and systems can be used to identify a donor for a HSCT or to choose a donor to generate a HSCT blood product that is not likely to result in bad outcomes for a recipient. Also provided herein are methods and systems for training machine learning models that can be used in methods and systems for sorting potential HSCT donors.

Disclosed are compositions and methods for preventing graft versus host disease (GVHD) or allograft rejection in subjects receiving donor cells. Also disclosed are methods enhancing regulatory T (Treg) cells for use in preventing GVHD. Also disclosed are methods of suppressing alloreactive donor cells in a subject receiving transplant donor cells that involves adoptive transfer of the treated Treg cells. Also disclosed are enhanced Treg cells produced by the disclosed methods that have been engineered to express chimeric antigen receptor (CAR) polypeptide cells.

Systems and methods are described herein for processing electronic medical images to predict one or more donor recipients for a patient. For example, a digital medical image of the patient may be received, wherein the patient is in need of a transplant. A trained machine learning system may be determined. The digital medical image may be provided into the trained machine learning system, the trained machine learning system determining a patient embedding. Using the patient embedding, a subset of donor recipients may be determined. Based on the subset of donor recipients a recommendation of optimal donors may be determined.

The present disclosure relates to the identification of single nucleotide polymorphisms (SNPs) in the Gamma genomic block in the central region of the major histocompatibility complex (MHC) that can be used for matching transplant donors and recipients and determining disease susceptibility.

The embodiment of the application discloses a kind of fecal bacteria transplantation donor-recipient dynamic matching method, device, equipment and medium, in response to donor-recipient matching request, the fecal bacteria transplantation after monitoring data of current preset sampling time of to-be-matched recipient is acquired;Wherein, the fecal bacteria transplantation after monitoring data is determined based on the fecal bacteria group of any one target donor to to-be-matched recipient after completing fecal bacteria transplantation operation, and the target donor is determined based on the genetic feature information, immune feature information and clinical feature information of to-be-matched recipient and multiple candidate donors for adaptability matching;Fecal bacteria transplantation after monitoring data is input into transplantation strategy control model, and at least one of the donor switching strategy and dose adjustment strategy for adjusting to-be-matched recipient in next period is output, overall compatibility evaluation is realized by multi-modal data integration, and treatment scheme is continuously optimized by means of dynamic feedback mechanism, which significantly improves the precision and adaptability of fecal bacteria transplantation treatment.

Method and systems for sorting potential hematopoietic stem cell transplant (HSCT) donors as a donor or non-donor using immunophenotyping of blood samples. The methods and systems can be used to identify a donor for a HSCT or to choose a donor to generate a HSCT blood product that is not likely to result in bad outcomes for a recipient. Also provided herein are methods and systems for training machine learning models that can be used in methods and systems for sorting potential HSCT donors.