137 results about "Disease status" patented technology

Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

The invention provides an experimental dyeing result image processing and calculating method. The method comprises the following steps: identifying a dyeing positive region in a preprocessed tissue sample digital image by using a first identification model, and carrying out area quantification on the identified dyeing positive region to obtain positive region area data; using a second recognition model to recognize and extract the intracellular cavitation region in the preprocessed tissue sample digital image, and performing area quantification on the extracted intracellular cavitation region to obtain cavitation region area data; calculating a dyeing positive region area proportion based on the positive region area data, and calculating a cavitation region area proportion based on the cavitation region area data; and inputting the area proportion of the dyeing positive region and the area proportion of the cavitation region into a preset disease evaluation model, and outputting a disease state evaluation result. According to the method, the accuracy and comprehensiveness of experimental dyeing image analysis are improved, and the automation degree and result credibility of pathological analysis can be remarkably improved.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

The invention discloses a co-disease collaborative identification and risk early warning method based on multi-modal data and an adaptive large model. The method comprises the following steps: collecting multi-modal medical data; carrying out preprocessing and feature extraction on the multi-modal medical data; based on the fine-tuned adaptive large model, deep interactive fusion is carried out on the extracted multi-modal features, potential association among different data sources and mutual influence and synergistic effect among various diseases are learned, and combined judgment and future onset risk prediction of the current common disease state of the patient are output; determining a specific co-disease combination and a co-disease risk level based on the combined judgment of the current co-disease state of the patient and a future onset risk prediction result; and when the determined co-disease risk level is equal to or higher than an early warning threshold, generating a collaborative early warning and intervention suggestion. According to the invention, a cross-modal and cross-disease feature fusion technology can be utilized to realize conjoint analysis and comprehensive risk early warning of multiple disease states of the patient, and the co-disease management efficiency and the clinical auxiliary decision-making level are significantly improved.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific organs and / or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

A method of automatically diagnosing pneumonia in a patient includes obtaining values of two or more diagnostic parameters of the patient from a caregiver of the patient using an input / output interface device. The method includes applying, using a processor connected to an input / output interface, two or more diagnostic parameters to an electronic memory storing a plurality of pre-compiled pneumonia diagnostic models to identify an optimal diagnostic model for performing the diagnosis. The values of the two or more diagnostic markers are applied to the identified optimal diagnostic model to generate a diagnostic output. An input / output interface device is operated according to the diagnostic output to indicate to the caregiver the presence or absence of pneumonia in the patient. The caregiver may use the diagnostics to provide appropriate care for the patient. A pneumonia diagnostic model is from investigations of pneumonia-positive and non-pneumonia population of subjects.

A system and method for rendering a diseased organ implant eligible, the method including acquiring an image data set of a potential donor organ, identifying one or more disease states in the potential donor organ, wherein the one or more disease states render the organ ineligible for transplant, harvesting the potential donor organ, applying histotripsy therapy, and confirming elimination / reduction of the disease state in the potential donor organ.

Some embodiments relate to nanoparticle probes for the detection of disease states in a patient or for tissue engineering. In some embodiments, the nanoparticle probe comprises one or more slip bonds that bind to a cell surface structure. In some embodiments, the binding of the nanoparticle probe is selective. In some embodiments, the nanoparticle probe binds to cells having a certain maximum glycocalyx thickness.

Disclosed herein are systems and methods for determining a disease state of an individual. The systems can be configured to perform the methods disclosed herein, and these methods can include receiving sequencing data for one or more cell-free nucleic acid fragments obtained or derived from a biological sample of the individual. These methods can also include determining a methylation profile for the individual based on identifying abnormal patterns of methylation features. A trained machine learning model can be used to generate an indication of the disease state.

The invention provides a prawn disease-oriented intestinal tract key metabolite design method and device, and relates to the technical field of strategy optimizing.The prawn disease-oriented intestinal tract key metabolite design method comprises the steps that prawn intestinal tract metabolite data are obtained, a random forest model distinguishes prawn health conditions through intestinal tract metabolite, and indication intestinal tract metabolite for distinguishing the health conditions is determined; the importance of the distinguishing metabolites is quantified through an MDA algorithm, and the most important distinguishing metabolite combination is screened; and distinguishing the metabolite and converting the metabolite concentration into a weighted undirected network, obtaining the feature vector centrality of each metabolite in the molecular ecological network, and determining a key metabolite. According to the invention, the random forest and the molecular ecological network are combined to identify key metabolites in a disease state, and the relative importance of each variable to a classification result is quantified. Meanwhile, the proportion of each key metabolite is determined by tracing back to healthy prawn intestinal tracts, and diseases are prevented and controlled symptomatically by compensating the combination of the inhibited key metabolite in the disease prawns.

The application discloses a medical report generation method and system based on multi-modal information fusion, which extracts features from the multi-modal data of the patient, fuses the features, and further encodes the disease state information, combines the disease state and disease name related information, generates rich disease embedding, inputs the disease embedding into a Transformer decoder with multi-head self-attention and position encoding to generate a medical report, builds a classification network based on a text encoder, summarizes the generated medical report to obtain an abstract embedding, and classifies the abstract embedding into a corresponding disease state; and guides the generator through loss optimization, and the loss of the generator participates in total loss calculation to ensure the accuracy of the report. The method can accurately describe the symptoms, avoid mistakes and omissions, give reliable diagnostic conclusions, and is highly consistent with clinical facts, and can meet the clinical diagnosis needs.

A method includes contacting one or more volatile organic compounds (“VOCs”) evolved from a sample to one or more detection regions configured for sensitivity against one or more VOCs. The contacting causes a change to the detection regions that is visible to the human eye. The method also includes determining the presence or absence of a VOC by comparing the change to the detection regions to a key, with the key being a visible change. A system utilizing the method is also described.

Novel compositions of recombinant human CC10 protein have been generated by chemically modifying the pure protein in vitro. Several new synthetic preparations containing isoforms of chemically modified rhCC10 have been generated by processes that utilize reactive oxygen species and reactive nitrogen species. These preparations contain novel isoforms of rhCC10 which have been characterized with enhanced or altered biological properties compared to the unmodified protein. Preparations containing novel isoforms may be used as standards to identify and characterize naturally occurring isoforms of native CC10 protein from blood or urine and ultimately to measure new CC10-based biomarkers to assess patient disease status. These preparations may also be used to treat respiratory, autoimmune, inflammatory, and other medical conditions that are not effectively treated with the unmodified protein.

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.

The invention discloses a risk assessment system for difficulty in cross matching of blood, 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 layering module and a result output and suggestion module. The data input module obtains key information of a patient, wherein the key information comprises an erythrocyte irregular antibody screening result, multiple myeloma diagnosis, hematology department medical history, age of more than or equal to 60 years, pregnancy-related disease state and blood type identification difficulty; the risk calculation module adopts a preset scoring algorithm to endow each factor with a weight and calculates a total risk score; the risk layering module divides the patients into three levels of low risk, medium risk and high risk; and the result output module provides differentiated clinical management paths corresponding to the risk levels. According to the method, early warning of difficulty in cross matching of blood is achieved through quantitative evaluation, blood transfusion process efficiency can be optimized, clinical risks can be reduced, and the method has good prediction performance (AUC = 0.819) and a high negative prediction value (93.7%).

Methods and systems for characterizing a biological structure including: obtaining image data for the biological structure using an imaging system; constructing a volumetric dataset of the biological structure based on the image data; determining at least one of a plurality of sections along a plane or curve within the volumetric dataset; and characterizing, using a trained algorithm and based on at least one of the plurality of sections, the biological structure for potential tissue type or disease state.

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 disclosure is related to training a machine learning model using fragmentomic features. A method includes obtaining a dataset comprising data indicating a known disease status of each of a plurality of individuals and sequencing data for each of the plurality of individuals. The method includes recursively training a machine learning model to predict a status of a patient. Recursively training the machine learning model includes: randomly partitioning the dataset into a first partition and a second partition; selecting one or more fragmentomic features for training the machine learning model; using the selected fragmentomic features and the first partition to train the machine learning model; and applying the machine learning model to the second partition to determine a performance of the machine learning model.

The invention relates to the technical field of machine learning medical prediction, in particular to a diabetes-related pancreatic cancer risk prediction method based on a machine learning model and biological age, and the method comprises the steps: constructing a health reference population queue and a type 2 diabetes application verification queue; determining a core index panel through an automatic machine learning process, and training by adopting a regularization survival analysis model to obtain biological age and age acceleration; based on multi-modal features such as age acceleration, predicting a future pancreatic cancer absolute risk probability by using a machine learning competitive risk model, performing risk grade division, calculating an equal-risk age and supporting risk trajectory simulation according to the future pancreatic cancer absolute risk probability; and packaging the model into a risk prediction toolkit with an adaptive calibration function, and outputting a comprehensive risk assessment report. According to the method, the biological age is trained by adopting the pure health queue, so that the interference of the disease state on aging measurement is avoided, and the prediction precision of the pancreatic cancer risk of the type 2 diabetes mellitus population is improved.

Provided is a medical assistance device including an acquisition unit configured to acquire personal data including examination data and medical interview data of a user, and an output unit configured to determine a disease state of the user for an individual disease forming a complex disease, based on the personal data, and configured to output an improvement target to be achieved by the user to improve a complex disease state, when a combination of the disease states of the individual diseases or a combination of the disease state of the individual disease and the personal data is the complex disease state.

The invention discloses a marker combination for diabetes diagnosis or prognosis evaluation and application, and belongs to the technical field of disease diagnosis. The technical problem to be solved is that a marker combination in the prior art does not conform to the pathophysiological difference of diabetes and diabetic nephropathy, the distinguishing effect of diabetes and diabetic nephropathy is insufficient, and early intervention and precise diagnosis and treatment of diabetic nephropathy are difficult to realize. According to the key point of the technical scheme, the marker combination is used for diagnosis or prognosis evaluation of diabetes mellitus, and the marker combination is composed of O43795MYO1B and O43707ACTN4. The kit has an excellent effect of distinguishing the type 2 diabetes from the early diabetic nephropathy, can efficiently and accurately distinguish the two disease states, assists the screening and identification of the early diabetic nephropathy, and improves the diagnosis and treatment accuracy and timeliness.

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.

The present disclosure provides compositions comprising taurine and related methods that can provide advantages to animals. For instance, administering taurine to animals provided numerous observed improvements in the animals. Importantly, administering taurine to animals after observation of disease symptoms was shown to be effective in preventing the secondary consequences that can be associated with disease state in animals.

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.

The application relates to a kinship data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring kinship data, generating a kinship graph and a graph structure connection index according to the kinship data; for a graph node in the kinship graph, determining a graph node label of the graph node according to a disease state of an individual represented by the graph node in the kinship data, and determining a supervised mask value of the graph node according to a node feature of the graph node; inputting the node feature, an edge attribute vector and the graph structure connection index into a graph neural network model to output a disease probability prediction value of each graph node; determining a target graph node from the kinship graph based on the supervised mask value of each graph node, calculating a loss value according to the graph node label of the target graph node and the disease probability prediction value of the target graph node, and updating a model parameter according to the loss value, so that the prediction accuracy of a genetic disease risk can be improved.