913 results about "Heart failure" patented technology

The invention provides an AI-based heart failure risk assessment method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: recognizing a mutation point of a physiological index time sequence through a recursive segmentation algorithm, extracting a mutation feature matrix, carrying out the time sequence mapping, recognizing a periodic mutation and gradual change mode, and converting into a risk score to generate a risk accumulation curve. Calculating index conduction time delay, drawing a risk conduction path diagram, marking an intervention time window, and finally determining a heart failure risk level and generating an evaluation report. According to the invention, early accurate recognition of the heart failure risk can be realized, and the best opportunity is provided for clinical intervention.

Various systems, devices, and methods are disclosed herein for treating acute myocardial infarction (AMI) patients using a heart pump controlled in a manner that maximizes mechanical unloading of the left ventricle in the presence of cardiovascular instability and minimizes myocardial oxygen consumption (MVO2) and consequentially infarct size to prevent the development of subsequent heart failure. In a closed feedback system, the system can include a sensor configured to generate an output used to measure or calculate a left ventricular systolic pressure (LSVP) within the left ventricle of a heart and a controller coupled to a heart pump. The controller can be configured to measure or calculate the LVSP based on the output of the sensor and to control an operation of the heart pump to maximize mechanical unloading of the left ventricle based on the measured or calculated LVSP.

The invention relates to the technical field of medical health information, in particular to a heart failure risk prediction method and system based on multi-source heterogeneous data fusion, and the prediction system comprises a data collection module, a data management module, a multi-modal feature extraction module and a dynamic risk prediction model module. An intervention strategy recommendation module; and a visualization and iterative optimization module. The prediction method is applied to the prediction system, patient health data is collected and obtained, a multi-source heterogeneous health database is constructed, physiological time sequence features, traditional Chinese medicine dialectical features and western medicine clinical features are extracted respectively to be subjected to structured coding processing, a multi-modal feature set is formed, the multi-modal feature set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are combined. A Bayesian attention mechanism is introduced, dynamic prediction of the heart failure risk is achieved through the constructed depth time sequence model, an intervention scheme can be adjusted and formulated in the whole course of heart failure management, excessive medical treatment is reduced, meanwhile, disease progress is effectively restrained or delayed, and the life quality of a patient is improved.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

To provide a device and a method for treating at least one of hypertension, pulmonary arteries, diabetes, obesity, heart failure, end-stage renal disease, digestive disease, urological disease, cancers, tumors, pain, asthma or chronic obstructive pulmonary disease by delivering an effective amount of a formulation to a tissue.SOLUTION: In embodiments of the present invention, the formulation may include at least one of a gas, a vapor, a liquid, a solution, an emulsion or a suspension of one or more ingredients. In embodiments of the present invention, amounts of the formulation and / or energy are effective in injuring or damaging tissue, nerves and nerve endings in order to relieve disease symptoms.SELECTED DRAWING: Figure 3B

The invention discloses a heart failure patient remote monitoring and early warning system based on the Internet of Things, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source physiological and behavior data, and generating an input sample set; the feature evaluation module is used for constructing an extreme gradient boosting tree algorithm model and obtaining feature importance scores; the feature screening module is used for executing a recursive feature elimination process and outputting an optimal feature subset; the risk judgment module is used for inputting the optimal feature subset into an extreme gradient boosting tree algorithm model to generate a heart failure risk score; the early warning triggering module is used for triggering early warning and sending early warning information to the patient and the platform; the feedback recording module is used for generating feedback information; and the dynamic optimization module is used for updating the input sample set based on the feedback information, retraining the extreme gradient boosting tree algorithm model and screening the features. According to the method, the extreme gradient boosting tree and recursive feature elimination are fused, and accurate evaluation and dynamic early warning of the multi-source data of the heart failure patient are realized.

A class of compounds and the use thereof in a drug. Specifically, the present invention relates to a class of new compounds, a pharmaceutical composition containing the compounds, a method for preparing the compounds, and the use of the compounds or the pharmaceutical composition in the preparation of a drug for treating GLP-1 receptor agonist-mediated diseases and / or disorders, particularly the use in the preparation of a drug for treating diabetes, non-alcoholic fatty liver disease, obesity, myocardial infarction, heart failure, diabetic nephropathy, Parkinson's disease and Alzheimer's disease.

The invention relates to the technical field of medical instruments, and discloses an intelligent device for monitoring the cardio-pulmonary function of a heart failure patient, the intelligent device comprises a multi-mode sensor module, an AI analysis module, a biological feedback module and a wearable carrier, the multi-mode sensor module is used for collecting heart rate, respiratory rate, oxyhemoglobin saturation and body movement data in real time, and the AI analysis module is used for analyzing the heart rate, the respiratory rate, the oxyhemoglobin saturation and the body movement data; monitoring modes can be dynamically switched in rest, motion and sleep scenes; the AI analysis module is used for detecting abnormity based on real-time data and predicting a heart failure deterioration risk in combination with historical data; and the biological feedback module is used for providing personalized intervention in a vibration or audio mode according to the analysis result. The heart rate, the respiration rate, the blood oxygen saturation and the body movement data are collected at the same time through the multi-mode sensor module, sleep apnea or periodic respiration is recognized in combination with the AI analysis module, the defect of separation of cardiopulmonary function monitoring and sleep mode analysis in the prior art is overcome, and therefore early intervention and treatment adjustment are supported.

The invention relates to a heart failure home patient-oriented multivariable trend anomaly detection method, which comprises the following steps of: constructing an initial contour of a multivariable health trend for a patient, and introducing a trend inertia vector: updating trend inertia to form an individual trend trajectory relationship; detecting an inertia breaking point in the trend trajectory diagram; analyzing whether the variables within 12-36 hours before and after the focusing breaking point analysis generate collaborative disturbance or not; performing reprocessing through a disturbance amplification operator to form a potential early warning factor; constructing a multivariable intervention graph by using the disturbance cooperation matrix; monitoring the offset direction and strength of the causal propagation chain on each path; if the plurality of paths are subjected to direction deviation in the same period at the same time, judging that the deviation is pathological trend deviation; converting the causal offset path into a single trend risk factor, spreading a plurality of weak signals in a variable graph, evaluating systematic influence, forming a fuzzy risk scoring curved surface relationship, and outputting an individual trend risk level; the sensitivity and continuity of trend identification are improved, individual differences are adapted, and the false alarm rate is reduced.

Interatrial shunts having incorporated physiologic sensors are provided for monitoring and treating cardiovascular syndromes, including heart failure and pulmonary hypertension, in which the one or more sensors are affixed to the shunt to measure a physiologic parameter within the interatrial shunt. The one or more sensors may be directly affixed to or within a lumenal surface of the shunt or may be disposed on a support structure in a spaced relation to the shunt lumen, the one or more sensors disposed at locations subject to little or no pannus formation or cardiac wall motion artifact.

The invention is applicable to the technical field of biological detection, provides a fluorescence immunochromatography detection method based on monatomic nano-enzyme enhancement, and particularly relates to two application forms: in the first form, a detection signal enhanced fluorescence immunochromatography detection card and a sample diluent containing a monatomic nano-enzyme are involved; in the second form, the invention relates to a fluorescence immunochromatography detection card with enhanced detection signals, and a sample pad of the detection card is obtained by soaking a sample pad treatment solution containing a monatomic nano-enzyme solution. The two application forms both relate to a monatomic nano-enzyme solution, and the solution is composed of monatomic nano-enzyme, bio-enzyme composite microspheres, a protective agent, a surfactant, a thickening agent and a preservative. The monatomic nano enzyme is composed of an atomic-scale dispersed metal active center and a carrier material. When the method is used for detecting myocardial and inflammatory markers, the limit of quantitation can reach the pg / mL level, the detection linear range can span three orders of magnitude, and the clinical examination use requirements can be met. The kit can be applied to rapid quantitative detection of myocardial and inflammatory markers, has the advantages of high sensitivity, rapidness, convenience and high efficiency, and provides an auxiliary diagnosis basis for early screening of myocardial infarction, heart failure and infection related diseases.

The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and / or estimate a metric associated with the presence, non-presence, severity, and / or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.

The invention relates to the technical field of intelligent medical treatment and traditional Chinese medicine diagnosis and treatment, in particular to a traditional Chinese medicine heart failure intelligent diagnosis and treatment system and method based on machine vision, and the diagnosis and treatment method comprises the steps: collecting a tongue image and a face image of a patient and an infrared thermogram of a corresponding part, and carrying out the preprocessing of the collected tongue image, face image and infrared thermogram; features in the tongue image, the face image and the infrared thermal image are quantified into tongue image feature vectors, face feature vectors and temperature feature vectors by using different algorithms, a multi-modal fusion deep learning model is established, and the feature vectors are input into the multi-modal fusion deep learning model for training; and inputting the collected tongue image, the face image and the infrared thermal image into a multi-modal fusion deep learning model, outputting a diagnosis result, and carrying out visual display. The diagnosis and treatment system is applied to the diagnosis and treatment method, the system can objectively quantify traditional Chinese medicine diagnosis information, the sensitivity of early recognition is remarkably improved, and the accuracy and efficiency of diagnosis are improved.

Provided herein are methods of treating a subject with heart failure, comprising administering to the subject an initial dose of a cardiac sarcomere activator (CSA) for an initial time period, and subsequently administering to the subject a dose of the CSA based on the subject's plasma concentration of the CSA at the end of the initial time period.

The invention relates to the technical field of active peptides, in particular to a myocardial active peptide capable of relieving heart failure and protecting heart and a preparation method thereof. The method takes pig hearts as raw materials, and sequentially comprises the following steps: pretreating the pig hearts, adding water, and homogenizing; adding alkaline protease to carry out first constant-temperature enzymolysis; adding neutral protease and flavourzyme to carry out second constant-temperature enzymolysis; then lipase is added for third-time constant-temperature enzymolysis, and enzyme deactivation and cooling are carried out; and finally, adding activated carbon into the enzymatic hydrolysate, filtering with diatomite, and spray-drying to obtain the myocardial active peptide. According to the method, the yield and the purity of the active peptide are remarkably improved through a synergistic process of three-step enzymolysis and activated carbon purification, the obtained product is rich in small molecule peptide with a definite heart protection function, and a new way is provided for high-value utilization of pig heart resources and development of functional products for preventing and treating heart failure.

The invention provides a method and system for carrying out heart failure early risk assessment by using multi-modal data, and the method comprises the steps: collecting medical data of a patient, and carrying out the preprocessing of the medical data; and inputting the preprocessed medical data into a pre-established heart failure assessment model, performing assessment by using the heart failure assessment model, and outputting an assessment result, the assessment result comprising a heart failure category or a heart failure risk level of the patient. According to the method, the data from different sources are comprehensively analyzed, so that the relevance and complementarity among the data sources are fully mined. By using the BiLSTM-CRF model, the time sequence data of a patient can be effectively analyzed, the dynamic change of the patient can be captured, and the dependency relationship among different characteristics can be established, so that a doctor can be helped to more accurately identify the symptom and risk level of heart failure.

The invention provides a non-invasive LVEF monitoring method and system based on millimeter wave radar chest displacement signals. The method comprises the steps that S1, millimeter wave radar signals are transmitted; s2, thoracic cavity micro-motion signals are collected; s3, preprocessing the thoracic cavity micro-motion signal; step S4, separating vital signs; s5, extracting heart failure features; step S6, risk assessment; and S7, outputting a result. According to the invention, a set of non-contact and continuous monitoring cardiac function evaluation system is constructed through creative combination of a radar sensing technology and medical diagnosis, and compared with a traditional LVEF detection method (such as echocardiography, cardiac MRI and the like), breakthrough improvement in clinical practicability, data continuity and technical universality is realized.

The disclosure describes compounds of Formula (I) and Formula (II), which in non-limiting aspects contain an asialoglycoprotein receptor (ASGPR) binding moiety and an anti-β1AR binding moiety. Compounds of Formula (I) and Formula (II) are useful in preventing, treating and / or ameliorating heart failure in a subject when administered therapeutically effective amounts.

The invention discloses an intelligent rehabilitation monitoring method and device for a heart failure patient, and relates to the technical field of rehabilitation data processing.The method comprises the steps that on the basis of multiple collection time scales, monitoring item time sequence data sets are obtained, and the monitoring item time sequence data sets comprise the time sequence data set, corresponding to the multiple collection time scales, of each monitoring item; health feature value extraction is carried out on the time sequence data set of the monitoring items, and feature value sequences corresponding to different collection time scales are generated; constructing a multi-time-scale multivariate sequential sequence, and quantifying a physiological system elasticity coefficient value of the heart failure patient by using a preset event elasticity extraction mechanism; and inputting the multi-time-scale multivariate sequential sequence and the elastic coefficient value as monitoring features into a heart failure rehabilitation trajectory prediction model, outputting a health index sequence in a future intervention window, and performing elastic loss critical point identification as an early intervention point. Therefore, the accuracy and foresight of monitoring the rehabilitation health trend of the heart failure patient are improved.

The invention relates to substituted pyrazolo piperidine carboxylic acids, their salts and to processes for their preparation, and also to their use for preparing medicaments for the treatment and / or prophylaxis of diseases, in particular cardiovascular and cardiac diseases, preferably heart failure with reduced and preserved ejection fraction (HFrEF, HFmrEF and HFpEF), hypertension (HTN), peripheral arterial diseases (PAD, PAOD), cardio-renal and kidney diseases, preferably chronic and diabetic kidney disease (CKD and DKD), cardiopulmonary and lung diseases, preferable pulmonary hypertension (PH), and other diseases, preferably neurodegenerative diseases and different forms of dementias, fibrotic diseases, systemic sclerosis (SSc), sickle cell disease (SCD), wound healing disorders such as diabetic foot ulcer (DFU).

The invention relates to the technical field of heart failure auxiliary diagnosis and treatment, in particular to a heart failure auxiliary diagnosis and treatment knowledge distillation method and system.The knowledge distillation method comprises the steps that an age-crossing fusion data set is built by combining ancient medical case text data related to traditional Chinese medicine heart failure; performing field fine tuning on the pre-training model to generate a basic teacher model; performing semantic alignment, and outputting a cross-time contrast database; constructing a curative effect scoring model by using the time sequence model, and outputting a quantitative association database; using a graph neural network to output an intelligent heart failure knowledge graph, and generating a comprehensive teacher model; performing knowledge distillation on the comprehensive teacher model to generate a clinical student model; the knowledge distillation system is applied to the knowledge distillation method, has a dynamic learning ability, can fuse ancient and modern trans-time knowledge and combine traditional Chinese medicine ancient book wisdom to carry out heart failure auxiliary diagnosis and treatment, and can give consideration to model performance and deployment efficiency through knowledge distillation.

The metabolic marker can be used for early diagnosis of heart failure, has the advantages of high sensitivity, rapidness, convenience and accurate and reliable result, provides a basis for clinical decision, provides a certain basis for subsequent fundamental research and clinical research, and has potential application and research values. The metabolic marker comprises one or more of 1-(4-nitrophenyl) piperidine, sphingolipid d19: 3 / 23: 0, phosphatidyl ethanolamine 16: 1e / 22: 5, phosphatidyl ethanolamine 16: 1e / 22: 6, sphingolipid d14: 0 / 22: 1, 3-phosphoglyceric acid, phosphatidylcholine 15: 0 / 20: 4, phosphatidylcholine o-16: 1 / 18: 0, phosphatidylcholine 14: 0e / 20: 1, phosphatidylcholine 17: 0 / 18: 5, betaine, phosphatidylcholine 15: 0 / 18: 2 and phosphatidylcholine 18: 5e / 20: 4.

The invention provides a heart failure risk prediction method and system based on multi-modal feature fusion, and the method comprises the steps: firstly carrying out the data feature analysis, carrying out the screening and extraction of data key features, and then building a multi-modal fusion model to provide more comprehensive and accurate medical data analysis. And finally, constructing a patient prognosis evaluation model so as to predict the heart failure risk of the heart failure patient. The heart failure key indexes are automatically screened through data feature analysis, then the multi-modal deep learning model is fused, the heart failure risk of the patient can be accurately predicted in advance, and especially in clinic, early intervention, individualized treatment and resource optimization can be achieved accordingly.

The invention relates to application of tsRNA-3025a as a prognostic marker of acute myocardial infarction and a treatment target spot of myocardial ischemia reperfusion injury. A DNA (Deoxyribonucleic Acid) sequence corresponding to the tsRNA-3025a is shown as SEQ ID NO: 1: 5 '-ATCCTGCCGACTACGCCA-3'. The tsRNA-3025a can be used for treating acute myocardial infarction and myocardial ischemia reperfusion injury. In the aspect of prognosis, a detection kit is provided, and the risk of heart failure and short-term adverse events of a patient is evaluated by quantitatively detecting the expression level of the tsRNA. In the aspect of treatment, the invention provides the application of the anti-tagomir for inhibiting the function or expression of tsRNA-3025a in the preparation of the medicine for treating the myocardial ischemia reperfusion injury, and the anti-tagomir is subjected to specific chemical modification. A novel biomarker is provided for prognosis risk stratification of acute myocardial infarction, and an effective treatment strategy is provided for prevention and treatment of myocardial ischemia-reperfusion injury.

The present disclosure describes a new native peptide designated herein as Dwarf Open Reading Frame, or DWORF. This peptide enhances the apparent activity of the SERCA pump, is positively inotropic and lusitropic, and therefore is provided as a therapeutic agent for disorders characterized by cytosolic calcium overload.

Systems and methods and devices are provided for treating conditions such as heart failure and / or pulmonary hypertension by at least partially occluding flow through the superior vena cava for an interval spanning multiple cardiac cycles. A catheter with an occlusion device is provided along with a controller that actuates a drive mechanism to provide at least partial occlusion of the patient's superior vena cava, which reduces cardiac filling pressures, and induces a favorable shift in the patient's Frank-Starling curve towards healthy heart functionality and improved cardiac performance. The occlusion device may include a lumen obstructed by a relief valve that may permit fluid flow through the occlusion device to release an excessive build-up of pressure.

The invention relates to the technical field of mattresses, in particular to a multifunctional heart failure patient condition monitoring mattress which comprises a mattress body. The mattress further comprises an adjusting mechanism, a monitoring mechanism and an analysis processing module, and the adjusting mechanism, the monitoring mechanism and the analysis processing module are all mounted on the mattress main body; the monitoring mechanism is used for monitoring the physical condition of a patient, the adjusting mechanism is used for adjusting the heights of different positions of the mattress main body, and the analyzing and processing module is used for analyzing and processing data acquired by the monitoring mechanism; a patient lies on the mattress body, the physical condition of the patient is monitored through the monitoring mechanism, then data obtained by the monitoring mechanism is analyzed and processed through the analysis and processing module, and the adjusting mechanism is controlled to adjust the heights of different positions of the mattress body, so that the shape of the mattress body is suitable for the patient, and the return blood volume is reduced; the comfort of the patient is improved, and the morbidity of the patient is reduced.

The illustrative methods and systems facilitate one or more dynamic analyses that can characterize and identify synchronization between acquired cardiac signals and photoplethysmography signals to predict / estimate the presence, location, and / or severity of abnormal cardiovascular conditions or diseases, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or condition, such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension. In some embodiments, statistical properties of the synchronization between the cardiac signal and the photoplethysmography signal are evaluated. In some embodiments, statistical properties of a histogram of synchronization between the cardiac signal and the photoplethysmography signal are evaluated. In some embodiments, statistical and / or geometric properties of a Poincare map of synchronization between the cardiac signal and the photoplethysmography signal are evaluated.

Methods, systems, and devices for evaluating function of a heart are provided. The methods can include use of one or more imaging modalities to assess diastolic function. The methods, systems, and devices can employ various features of a cardiac volume curve to assess cardiac function. The methods, systems, and devices can be used to guide diagnosis, therapy, and / or training for sick or well patients. Methods, systems, and devices for assessing likelihood of hospital readmission for a heart failure patient are also provided.

A pharmaceutical composition and a method of administering the pharmaceutical composition to a patient suffering from edema, heart failure, kidney or liver disease or having symptoms thereof is disclosed. The pharmaceutical composition includes furosemide, or a pharmaceutically acceptable salt, hydrate or ester thereof and tris(hydroxymethyl)aminomethane. The furosemide is present in the pharmaceutical composition at a concentration from about 10 mg / mL to about 30 mg / mL and the tris(hydroxymethyl)aminomethane is present at a concentration of less than or equal to 40 mM. The pH value of the pharmaceutical composition is maintained between about 8.0 and about 8.5 for parenteral administration.