22 results about "Disease status" patented technology

The presently described and claimed technology relates immunoassay methods of assessing a subject's Alzheimer's Disease status and zygosity for the APOE ε4 allele.

In some embodiments, the disclosed subject matter relates to systems and methods for designing and conducting clinical trials and biomarker validation studies. An example method includes: obtaining access to a trained computer-implemented model; providing, to the trained computer-implemented model, a plurality of sets of values for a plurality of features, each set of values corresponding to a candidate individual from a set of candidate individuals; receiving, from the trained computer-implemented model, a prediction of a disease state for each set of values; identifying a group of participants from the set of candidate individuals based on the prediction of the disease state; and facilitating at least one of a clinical trial or a biomarker validation study involving the group of participants.

The application discloses a prediction method and device for the influence of different stage PM2.5 components on diseases and a medium; the method comprises the following steps: collecting sample data of a prospective cohort and preprocessing the sample data to obtain a standardized feature dataset; constructing a multi-state trajectory model; using the multi-state trajectory model to calculate the risk ratio and dynamic transition probability of transition between diseases under different PM2.5 pollutant exposures; constructing an accelerated failure time model; using the accelerated failure time model to quantify the time ratio of transition of each disease state under PM2.5 pollutant exposure, identify the acceleration effect of the disease course, and output the predicted time of state transition; and outputting the risk ratio, dynamic transition probability and predicted time of state transition as the prediction result. The application can improve the accuracy of predicting the dynamic and acceleration effects of PM2.5 components on the whole disease course of metabolic cardiovascular diseases, and provide a scientific decision basis for early health intervention of metabolic cardiovascular diseases.

A method to assess the disease status, determine the mean / average control of the disease and monitor the compliance to treatment over a period of time of a respiratory disease, comprising of predicting the degree of airway inflammation and disease activity by measuring the inflammatory markers, evaluating the mucosal integrity / damage by measuring the moisture levels of the exhaled air, evaluating the breathing function and obstruction by measuring the FEV1 value, and evaluating the symptom control by determining the COPD Score.

Alzheimer's disease, the most common cause of dementia in older individuals, is a debilitating neurodegenerative disease for which there is currently no cure. The present invention discloses and describes panels of markers that are differentially expressed in the disease state relative to their expression in the normal state and, in particular, identifies and describes panels of markers associated with neurocognitive disorders. In some aspects, the marker panel comprises phosphoglucomutase 1 and / or thymosin beta-4, or an isoform, a variant or a fragment of either protein, alone or in combination with additional markers.

The application discloses a risk assessment system for cross matching difficulty, and relates to the technical field of clinical medical examination and blood transfusion management. The system comprises a data input module, a risk calculation module, a risk stratification module and a result output and suggestion module. The data input module obtains key information of a patient, including irregular red blood cell antibody screening results, multiple myeloma diagnosis, hematology department medical history, age of 60 years or older, pregnancy-related disease state and blood type identification difficulty; the risk calculation module adopts a preset scoring algorithm to assign weights to each factor and calculate a total risk score; the risk stratification module divides the patient into three grades of low risk, medium risk and high risk; and the result output module provides a differentiated clinical management path corresponding to the risk grade. The application realizes early warning of cross matching difficulty through quantitative assessment, can optimize the efficiency of the blood transfusion process and reduce clinical risk, has good prediction performance (AUC=0.819) and a high negative predictive value (93.7%).

The application discloses an axSpA diagnosis biomarker combination and application thereof in judging axSpA diagnosis effect, relates to the biomedical field, and comprises the following four plasma proteins: LIM and SH3 domain protein 1 (LASP1), calponin binding protein 2 (CNN2), neuron protein kinase C and tyrosine protein kinase substrate 2 (PACSIN2) and tropomyosin 4 (TPM4). The expression levels of the four proteins in the plasma of axSpA light / severe group patients are all up-regulated relative to a healthy control group, and there is a significant difference between the severe group and the light group. Through deep proteomics, multi-algorithm statistics, machine learning models and independent queue immunological verification, the protein combination composed of LASP1, CNN2, PACSIN2 and TPM4 is first systematically identified and verified as an excellent performance of axSpA diagnosis biomarker.

A tool for collection of exhaled breath includes a microreactor for capturing and concentrating target compounds in a gaseous sample, such as exhaled breath. An airflow of exhaled breath enters the interior of the microreactor, then contacts the central area of the microreactor and is redirected radially outwards, passing through microfluidic channels formed between micropillars, and exits the microreactor through a plurality of vent holes at the periphery. The micropillars are coated with a capture material, such as a reactive compound capable of forming adducts with carbonyl-containing VOCs. The captured target compounds may then be eluted from the micropillars and analyzed for detection of disease states or other purposes.

Methods are provided for the prognosis, diagnosis and treatment of various pathological states, including cancer, chemotherapy resistance and dementia associated with Alzheimer's disease. The methods provided herein are based on the discovery that various proteins with a high level of sialylation are shown herein to be associated with disease states, such as, cancer, chemotherapy resistance and dementia associated with Alzheimer's disease. Such methods provide a lysosomal exocytosis activity profile comprising one or more values representing lysosomal exocytosis activity. Also provided herein, is the discovery that low lysosomal sialidase activity is associated with various pathological states. Thus, the methods also provide a lysosomal sialidase activity profile, comprising one or more values representing lysosomal sialidase activity. A lysosomal sialidase activity profile is one example of a lysosomal exocytosis activity profile.

Systems and methods are disclosed for developing models for predicting Alzheimer's Disease (AD) and other disease states with improved fairness and bias mitigation. An example method includes receiving an EMR dataset comprising a first EMR subset for a first population of patients having confirmed positive disease indications, and an unlabeled EMR subset for a remainder population of the patients. The patients are categorized into various demographic groups. The method further includes using the EMR dataset to train a set of machine learning models via positive unlabeled learning (PUL) to first determine a second EMR subset for a second population of patients having reliable negative indications for the disease state, and then determine additional positive and additional negative indications. Biases specific to each group are mitigated by applying probabilistic criteria specific to each demographic group to subsets of the EMR data during the training of these machine learning models.

A system for generating a sustenance plan for managing genetic disorders is disclosed. The system includes a computing device. The computing device is configured to receive an input which may include genetics test data. The computing device is configured to identify a plurality of biological indices of a disease state as a function of the genetics test data. The plurality of biological indices comprises at least one biological index related to a genetic disease state. The computing device is configured to generate a genetic disorder classifier. The computing device is configured to produce a homeopathic program as a function of the genetic disorder. A method for producing a homeopathic program for managing genetic disorders is disclosed.

A state information determination method and device, and a control method and device are disclosed, and relate to the technical field of medical data processing. The state information determination method comprises: determining a state evaluation parameter corresponding to a to-be-tested body based on a preset analysis time interval, and determining first state information corresponding to the to-be-tested body based on the state evaluation parameter. The state evaluation parameter comprises at least one of a first heart rate characteristic parameter, a first heart rate variability characteristic parameter, a second heart rate characteristic parameter and a second heart rate variability characteristic parameter. The first heart rate characteristic parameter and the first heart rate variability characteristic parameter correspond to a first time interval, and the second heart rate characteristic parameter and the second heart rate variability characteristic parameter correspond to a second time interval. The present disclosure can improve the accuracy of the determined first state information and the comparability at different times, thereby providing favorable conditions for assisting in determining the disease state of the to-be-tested body and assisting in predicting the progress and the like based on the determined first state information.

This disclosure provides a method, system, and computer program product for animal auscultation. The method includes acquiring auscultation sound data of an animal; obtaining an initial prediction result based on the auscultation sound data using an animal auscultation model, the initial prediction result representing the animal's health or disease state; and calibrating the initial prediction result based on preset rules and individualized data of the animal to obtain a final prediction result. Combining a deep learning model with a more standardized rule base enables more effective diagnosis of animal diseases and health conditions.

The objective is to identify biomarkers that can monitor the disease state of tumors and to provide methods for determining the effectiveness of tumor treatment, monitoring recurrence, assessing the risk of recurrence, predicting prognosis, or assisting in diagnosis. [Solution] Focusing on the methylation status of ctDNA as a regulatory mechanism for epigenetic gene modification in tumor development, we found that certain genes are highly methylated in tumor tissue from tumor patients compared to non-tumor tissue, and that changes in the methylation level of these genes in ctDNA correlate with the disease state of the tumor. Therefore, we discovered that this can be used as a biomarker to monitor the disease state of tumors.

A metabolic imaging method based on 19F-MRI, comprising the steps of:administering one or more 3-fluoro-3-deoxy-sugar (s) to a subject; applying 19F-magnetic resonance imaging (19F-MRI) to identify generation, localization and / or distribution of one or more fluorinated compounds in internal organs of the subject; andassessing and / or diagnosing and / or monitoring a disease state associated with a pattern of abnormal activity of oxidizing and reducing metabolic enzymes.

This invention relates to the field of green space management technology, specifically a cloud-based intelligent green space management system. The system includes a cloud-based multi-source integration module, a temporal disturbance analysis module, a disturbance chain association module, a disease trend identification module, and a management and control linkage module. Based on various green space on-site monitoring points, it determines the online status of sensing devices and the accuracy of plot numbers. This invention constructs a cloud-oriented data integration system, aggregating multi-source environmental parameters and spatial attributes into a dynamic data structure. This achieves global correlation and temporal continuity of information between plots. Data is automatically processed by the cloud platform to form parameter evolution chains, establishing multi-level characteristic criteria for monitoring objects, including coverage, temporal sequence, and trend. Abnormal evolution of disease status is directly fed back to the management and control process through linkage judgment. The system outputs control configurations based on real-time status, thereby improving the consistency of risk assessment and the pertinence of control decisions.

To provide non-invasive and inexpensive digital biomarkers. [Solution] The server of the disease state estimation system stores a database containing data about the user, and as a function, it repeatedly executes the following steps at predetermined intervals for a predetermined number of times: acquiring user identification data (S101), presenting predetermined questions to the user to visualize the state of a predetermined disease (S102), receiving input of answers to the questions from the user (S103), and storing answer data indicating the content of the received answers in the database linked to the user identification data (S104). After a predetermined number of times, it executes the following steps: acquiring answer data stored in the database for a predetermined number of times or more, estimating a predetermined disease state based on the answer data for a predetermined number of times or more, and outputting the estimated result of the predetermined disease state.

The application provides a Chinese and Western medicine perturbation atlas recognition method based on deep learning and protein interaction network topology, and comprises the following steps: S1, collecting protein interaction data, constructing a protein interaction graph and a node index mapping table; S2, constructing a node set and a non-directional edge set; S3, constructing a node feature matrix; S4, constructing a deep graph structure regression model based on a graph attention neural network; S5, through training, the model obtains a continuous regression prediction value of the association between each protein node in the protein interaction network and a disease; S6, constructing a disease high-heat protein node set; S7, constructing a drug-protein-disease interaction network and calculating a coverage function of the drug; S8, based on a topological similarity and a redundancy penalty strategy, constructing a non-redundant drug perturbation atlas under different topological granularities; the drug perturbation atlas can provide a basis for the multi-pathway synergistic mechanism of the drug and the intervention ability of the drug on the disease state.

This invention relates to the field of volatile organic compound (VOC) testing technology, and in particular provides a method for identifying glucose metabolism disorders based on the exhaled VOC spectrum. The method includes: after drying and filtering, the exhaled sample is passed through a separation channel with a built-in linear cooling element at a constant flow rate. Multiple resonant mass-sensitive elements are equidistantly placed along the airflow direction in the channel. The resonant frequency shift of each resonant mass-sensitive element during the passage of the exhaled sample is recorded, forming a frequency shift sequence. A quadratic difference value is calculated for every three consecutive shifts in the frequency shift sequence. The position where the quadratic difference value is zero is located, and the index number of that position in the frequency shift sequence is extracted to form an index sequence. The difference between adjacent index numbers in the index sequence is calculated; if it exceeds the upper limit of the standard deviation, an identification signal for glucose metabolism disorders is output. This invention achieves efficient analysis and disease state classification of exhaled biomarkers through data acquisition, feature extraction, and threshold judgment.

The application provides a physiological age prediction model based on an immune repertoire and a transcriptome feature and a construction method, and belongs to the technical field of bioinformatics. The physiological age prediction model with high precision and high robustness is constructed by fusing an immune repertoire diversity index and an age-related gene expression profile. The physiological age prediction model can not only accurately evaluate the biological aging degree of an individual, but also effectively distinguish the aging difference under the health and disease states, provides a reliable tool for personalized health management, early warning of disease risk and evaluation of the effect of anti-aging measures, and has important clinical application and health management values.