5 results about "Non responders" patented technology

A system trains a set of machine-learning models to predict which atrial fibrillation patients would respond to which treatments using ablation lines. The models identify an ablation line treatment for a patient based on atrial fibrillation-related features associated with the patient. The models estimate reconnection probabilities for ablation sites associated with at least one ablation line treatment. The models identify patient atrial fibrillation predictors associated with the patient, based on demographics and heart component dimension parameters associated with the patient, and use the at least one ablation line treatment, reconnection probabilities, patient atrial fibrillation predictors, and patient demographics to predict whether the patient would not respond to pulmonary vein isolation only treatment. In response to a prediction that the patient would not respond to pulmonary vein isolation only treatment, the system enables a healthcare provider to provide the at least one ablation line treatment for the patient.

A clinical decision support system and method for predicting a clinical response of a target tumor to an immune checkpoint blocking therapy (ICB) by: obtaining a training dataset, the training dataset comprising training transcriptome records of tumor tissue samples of a plurality of known responders and a plurality of known non-responders to the ICB; deconvolution is carried out on the training data set to identify differential expression genes DEG in the training data set, the DEG is regarded as features of the training data set, and related responder and non-responder states are regarded as labels recorded in the training data set; performing regression analysis on the features to select a feature subset which can predict the responder tag or the non-responder tag and related feature weights; incorporating the feature subset and the associated feature weight into a reaction estimation model; receiving transcriptome data of the target tumor tissue sample; the transcriptome data is processed using the generated response estimation model to generate an estimated response indicator indicative of a possible response of the target tumor to the ICB.

The present invention relates to a prebiotic composition for use in a method for maintaining or improving the health of a subject in need thereof, comprising at least one inulin-type fructan (ITF) and at least one human milk oligosaccharide (HMO), wherein the subject in need thereof is an HMO non-responder; a food, a dietary supplement or a drink comprising a prebiotic composition for use in a method for maintaining or improving the health of a subject in need thereof, wherein the subject in need thereof is an HMO non-responder; and an inulin-type fructan (ITF) for use in a method for maintaining or improving the health of a subject in need thereof by administering a prebiotic composition comprising at least one human milk oligosaccharide (HMO), wherein the subject in need thereof is an HMO non-responder.The present invention also relates to the use of said prebiotic composition as a dietary supplement, food or drink for prebiotic non-responders. The present invention also relates to a prebiotic composition comprising at least one inulin-type fructan (ITF) and at least one human milk oligosaccharide (HMO) for use in a method for the treatment of a mood disorder, preferably depression or anxiety, wherein a subject in need thereof is an HMO non-responder.

The invention generally relates to the field of diagnostics and prognostics. In particular, the invention relates to a method for determining the responsiveness of a chronic hepatitis B (CHB) patient to IFN-alpha treatment. In an aspect of the invention, there is provided a method for determining the responsiveness of a chronic hepatitis B (CHB) patient to IFN-alpha treatment, comprising: (a) measuring an expression level of at least one biomarker of a prognostic biomarker panel selected from the group consisting of NQO2, MBL2, IGKV3D-20, PKHD1L1, PGLYRP1, KHSRP, LCN2, and ROBO1 in a biological sample obtained from the patient, and (b) comparing the expression level of the at least one biomarker to corresponding expression level of a known non-responder, wherein an expression level higher than the corresponding expression level is indicative of a likelihood of a positive response to IFN-alpha treatment.

A system for predicting a response to nutritional therapy for treating Crohn's Disease (CD), including a computer with a processor and memory, a machine learning module, wherein the machine learning module is programed to train a model by accepting a training data set comprising multiomics data of subjects with Crohn's disease that were treated by exclusive enteral nutrition (EEN) and a determination if each subject was a responder that successfully responded to the treatment or was a non-responder that unsuccessfully responded to the treatment, and wherein the machine learning module uses the trained model to accept multiomics data of a patient and predict if the patient is a responder or a non-responder.