78 results about "Kidney problem" patented technology

The invention relates to the technical field of medical care informatics, in particular to a nephropathy medication interaction risk screening system which comprises a data acquisition unit, an SOC chip processing and analysis unit, a risk early warning unit and a man-machine interaction and communication unit. Through multi-source data access, dynamic weight scheduling and Kalman filtering fusion correction, accurate and on-demand acquisition of nephropathy core indexes is realized, and real-time intelligent discrimination of personalized medication risks is realized by relying on knowledge base support, adaptive path adjustment and a safe atomization updating mechanism, so that the risk of the nephropathy is accurately determined. Therefore, the problems that traditional system data acquisition lacks specialized pertinence, risk assessment personalization is insufficient, and model updating is lagged are effectively solved, and the accuracy and timeliness of nephropathy medication interaction risk screening are remarkably improved.

The invention provides a chronic kidney patient cardiovascular adverse event prediction and clinical treatment system based on deep learning, and the system comprises a multi-source information integration module which obtains multi-modal fusion data; the risk prediction module is used for constructing a risk prediction model and outputting the risk prediction probability, the risk level and the risk report of each patient; the decision support module is used for generating a plurality of interpretable layers, calculating high-risk significant factors and automatically recommending clinical decision suggestions; the personalized diagnosis and treatment module is used for constructing a process knowledge graph, carrying out personalized treatment recommendation and distributing corresponding rehabilitation execution tasks for different roles; and the doctor-patient interaction module is used for establishing a multi-terminal collaborative micro-service architecture and carrying out multi-terminal collaborative management and early warning information real-time pushing. The cardiovascular adverse event prediction accuracy and efficiency of the chronic kidney disease patient can be effectively improved, personalized treatment schemes are pushed to different patients, and early diagnosis and early intervention of chronic kidney disease related complications are achieved.

Certain aspects of the present disclosure provide a monitoring system comprising a continuous analyte sensor configured to penetrate a skin of a patient and generate a sensor current indicative of analyte levels of the patient, and a sensor electronics module coupled to the continuous analyte sensor. The sensor electronics module comprises an analog to digital converter configured to receive the sensor current and convert the sensor current generated by the continuous analyte sensor into digital signals, a processor configured to convert the digital signals to a set of analyte measurements indicative of the analyte levels of the patient, and a Bluetooth antenna configured to transmit the set of analyte measurements wirelessly to a wireless communications device using Bluetooth or BLE communications protocols.

The invention relates to proteins for use in the prevention, treatment or amelioration of kidney diseases or kidney damage, more particularly where the protein is a follistatin-fusion protein.

Systems, methods and computer-readable media are provided for identifying patients having an elevated near-term risk of chronic kidney disease (CKD) progression, including predicting an individual's risk of progression to Stage 3 CKD within a future time interval, which may be up to 36 months. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In an embodiment, measurements of physiological variables are obtained, including serial measurements for uric acid levels from a longitudinal time series of serum or plasma samples spanning the previous two to five years. An annualized uric acid velocity of the patient is determined and used to generate a multivariable mathematical model for determining a likelihood of risk for developing Stage 3 CKD within 36 months.

The present disclosure provides a method and an apparatus for predicting a progression trajectory from acute kidney injury (AKI) to a kidney disease. The method includes the following steps: receiving a first set of features of a particular acute kidney injury (AKI) patient; selecting a second set of features from the first set of features using a preset algorithm; and predicting, using a first machine-learning model, a progression trajectory of a kidney disease of the particular AKI patient based on the second set of features.

Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.

The invention discloses a fundus lesion evaluation method and device for chronic nephropathy, and the method comprises the steps: obtaining an ultra-wide-angle fundus image of a to-be-detected target object with chronic nephropathy, and extracting fundus features from the ultra-wide-angle fundus image; based on the extracted fundus features, utilizing a multivariable regression model to predict a fundus lesion state; performing multi-modal fusion processing on the fundus lesion state, the patient information of the to-be-detected target object and the clinical information; and performing lesion probability prediction processing of a dynamic confidence interval on the multi-modal fusion features to generate a chronic kidney disease fundus lesion risk level. Therefore, the fundus lesion risk grade of the chronic kidney disease can be accurately predicted, and the reliability of evaluation of the fundus lesion risk grade of the chronic kidney disease is improved; in addition, a set of evaluation standards for the risk level of fundus lesions complicated by early chronic nephropathy are also constructed, so that the purposes of early discovery and early treatment of patients are achieved, and irreversible fundus lesions caused by CKD are reduced.

Provided herein are compounds that are useful in treating cancer, metabolic disease, cardiovascular disease, and kidney disease.

A system and method for determining patient hospitalization risk and treating patients is disclosed. The system and method may include extracting patient data from one or more databases corresponding to a pool of patients having end stage renal disease; using a predictive model with the extracted patient data to generate, for each of the patients, a respective expected probability for hospitalization within a predetermined time period; identifying a subset of patients having respective expected probabilities that are higher than other patients in the pool of patients; identifying, for each patient, at least one factor from the patient data that increased the expected probability of hospitalization; and based on the identified factors, determining and executing clinical interventions to lower the probability of hospitalization within the subset of the pool of patients.

Disclosed are compounds according to Formula (I) or (II), and pharmaceutical compositions comprising them. Also disclosed are therapeutic methods, e.g., of treating kidney diseases, using the compounds of Formula (I) or (II).

In one aspect, the invention provides compounds of Formula I, and salts, hydrates and isomers thereof. In another aspect, the invention provides a method of promoting bone formation in a subject in need thereof by administering to the subject a therapeutically effective amount of a compound of Formula I. The present invention also provides orthopedic and periodontal devices, as well as methods for the treatment of renal disease, diabetes, bone loss, and cancer, using a compound of Formula I.

It refers to a dietary product for hypoproteic diet suitable for supplementing the diet in patients suffering from chronic kidney disease, hepatic insufficiency and urea cycle disorders. It also refers to the use of the dietary product as supplement of the diet of said patients. The dietary product comprises amino acids, amino acid analogues, and pharmaceutically acceptable excipients.

The present disclosure relates to methods related to the treatment of B-cell responsive autoimmune glomerulopathies.

This invention relates to an activator of FXR, which can be used to treat autoimmune diseases, liver diseases, intestinal diseases, kidney diseases, cancer, and other diseases in which FXR plays a role, and has the formula (I): wherein L1, A, X1, X2, R 1 R 2 and R 3 As described in this article.

The disclosure is directed to a method for predicting whether a patient is at risk for developing Fast Progressing Renal Disease (FPRD) and treating patients to avoid such risk.

The application discloses a fundus lesion evaluation method and device for chronic kidney disease, and the method comprises the following steps: acquiring an ultra-wide-angle fundus image of a target object to be measured with chronic kidney disease, and extracting fundus features from the ultra-wide-angle fundus image; based on the extracted fundus features, predicting a fundus lesion state by using a multivariate regression model; performing multimodal fusion processing on the fundus lesion state, patient information of the target object to be measured and clinical information; performing lesion probability prediction processing on the dynamic confidence interval of the multimodal fusion features to generate a chronic kidney disease fundus lesion risk grade. Therefore, not only can the chronic kidney disease fundus lesion risk grade be accurately predicted, but also the reliability of the chronic kidney disease fundus lesion risk grade evaluation is improved; moreover, an evaluation standard for the fundus lesion risk grade of early chronic kidney disease is constructed, so that the purpose of early detection and early treatment of patients is achieved, and irreversible fundus damage caused by CKD is reduced.

Methods and kits for assessing the propensity of developing urolithiasis, kidney disease, or bladder disease in pets; and compositions to treat such conditions are described herein.

The present invention relates to a pharmaceutical composition for preventing or treating renal diseases, comprising, as an active ingredient, exosomes isolated from precursor cells of induced pluripotent stem cell-derived mesenchymal stem cells, the precursor cells having been treated or not having been treated with a pretreatment material. Exosomes of the present invention exhibit an effect of preventing or treating renal diseases that is more improved than that of exosomes isolated from conventional mesenchymal stem cells, thereby being effectively usable for relevant research and development and productization.

The invention belongs to the technical field of vascular access assessment, and relates to a vascular access assessment system and method based on machine learning, and the system employs a layered architecture, integrates follow-up management, postoperative maturity assessment and a machine learning decision support engine, achieves AVF maturity risk grading through multi-parameter adaptive threshold weighted scoring, and improves the assessment accuracy. A personalized training scheme is generated in combination with similar case matching, continuous self-learning is realized based on expert feedback, the problems of subjective evaluation, low efficiency, scheme homogenization and the like in internal arteriovenous fistula management are solved, patient data can be automatically processed, and structured reports and visual charts can be generated in batches; the method improves evaluation accuracy and clinical efficiency, improves patient prognosis, provides data support for medical quality control and scientific research, and is suitable for AVF full-cycle management of end-stage nephropathy hemodialysis patients.

An exercising device with fistula maturation management and assessment abilities and a method of fistula maturation management and assessment is described. The device is a tool for remote monitoring of compliance of exercise routines prescribed to Chronic Kidney Disease (CKD) patients for proximal fistula maturation. The device measures and syncs exercise data captured via a motion-sensing sensors (eg: a MEMS / NEMS micro-electro-mechanical systems or nano-electro-mechanical systems or other techniques - based accelerometer, gyroscope, magnetometer, inertial measurement unit etc.) and via computational device (eg: mobile phone, tablet, or an onboard device) to a cloud platform (eg: cloud platform, such as Amazon web service [AWS], Google cloud etc.) from which the Nephrologist can retrieve patient data and check for compliance.

The present disclosure relates to Kcnn 4 (i.e.KCa3.1) inhibitors and uses thereof in the treatment and / or prevention of cystic kidney disease such as polycystic kidney disease.

The present application provides a polypeptide targeting renal tubular epithelial cells, and the amino acid sequence of the polypeptide is ALINDTH. The present application also provides a targeted delivery system targeting renal tubular epithelial cells, which contains the polypeptide targeting renal tubular epithelial cells. The present application also provides a preparation method of the targeted delivery system targeting renal tubular epithelial cells. The present application also provides the use of the targeted delivery system targeting renal tubular epithelial cells in the preparation of a drug for treating kidney diseases. The present application realizes precise drug delivery for kidney diseases, and effectively solves the key technical problems of insufficient targeting of exosomes and low drug loading efficiency.

The present invention also refers to finerenone or a hydrate, solvate, pharmaceutically acceptable salt or polymorph thereof for use in the prevention or treatment of chronic kidney disease in patients with type 1 diabetes, wherein the prevention or treatment comprises administering to the patient a therapeutically effective amount of finerenone or a hydrate, solvate, pharmaceutically acceptable salt or polymorph thereof.

The present invention relates to a method for monitoring renal function, the method comprising: obtaining at least one raw data from at least one data source, pre-processing the at least one raw data to automatically generate at least one processed data, classifying the at least one processed data as at least one renal function status, and triggering at least one artificial intelligence assistance module, wherein triggering of the at least one artificial intelligence assistance module is based on the at least one renal function state. Furthermore, the invention relates to a system for monitoring renal function, the system comprising: an acquisition component configured to acquire at least one raw data from at least one data source; a processing component configured to pre-process at least the at least one raw data to automatically generate at least one processed data; a classification component configured to classify the at least one processed data as at least one renal function state, and a trigger component configured to trigger at least one artificial intelligence assistance module, wherein the trigger component is configured to trigger the at least one artificial intelligence assistance module based on the at least one renal function status.

The application discloses a kidney disease dialysis data processing method based on artificial intelligence and particularly relates to the technical field of medical data processing, comprising multi-modal dynamic coupling graph feature extraction: real-time collection of dialysis machine high-frequency numerical values and nurse-entered low-frequency events, construction of a cross-modal coupling graph through dynamic mutual information and an adaptive threshold, introduction of a time decay factor to correct edge weights, aggregation of output deep time sequence features through a graph attention network; stage adaptive time sequence risk prediction: input of features into a multi-scale dilated convolution network, fusion of different receptive field features through channel attention, division of non-equidistant intervals according to a dialysis process, stage sensitivity weighting using a learnable interval embedding vector and multi-head attention, and output of hypotension probability and first derivative trend; the application realizes cross-modal feature deep coupling, dialysis stage adaptive perception and executable causal intervention decision-making, and significantly improves early warning accuracy and clinical practical value.

Use of a compound of Formula I and related compositions for treating kidney diseases, disorders and conditions in a subject in need of such treatment, wherein the subject in need of such treatment is a subject carrying one or more genetic mutations in ALPK1.

This invention discloses a method for constructing a traditional Chinese and Western medicine-integrated kidney disease prediction model, belonging to the field of medical prediction model construction technology. The method includes: collecting Western medicine clinical data covering indicators such as kidney function and routine urine tests, and traditional Chinese medicine diagnostic data including symptoms, signs, tongue and pulse characteristics; ensuring consistent and complete fields through standardized template recording; processing missing data using multiple imputation and mode imputation methods, and removing outlier data using box plots and isolated forest algorithms; standardizing and encoding numerical and categorical data respectively; and extracting features through recursive feature elimination and word embedding techniques. This method achieves deep integration of traditional Chinese and Western medicine data, overcoming the limitations of single models. The model captures comprehensive features, has stable predictive performance, strong generalization ability, adapts to different medical scenarios, ensures data security, offers flexible deployment options, assists in precise clinical decision-making, promotes the implementation of early kidney disease prediction technology, and provides support for early detection and intervention of kidney disease.

The present disclosure relates to an alternative intervention to improve skeletal muscle health in chronic kidney disease (CKD). CKD causes skeletal muscle complications that lead to increased risk of falls, hospitalizations and mortality. While exercise is a common treatment for these impairments in the general population, in patients with CKD exercise has shown only marginal improvements in aerobic capacity and inconsistent effects on muscle strength, walking distance, and timed-up-and-go tests indicating this ‘usual’ treatment is not enough in patients in CKD. The present disclosure relates to methods of using an aminoindazole to treat inflammation and metabolic derangements and to improve skeletal muscle health in CKD.