10063 results about "Internal medicine" patented technology

Provided herein is a method or a use for treating a cancer, comprising administering to a subject a therapeutically effective amount of Compound A, a pharmaceutically acceptable salt, or a co-crystal thereof at a daily dose of 100 mg to 3600 mg on salt-free basis, wherein Compound A has the following structure:

Provided herein are methods of treatment comprising administering a selective ribonucleotide reductase (RNR) inhibitor in combination with other therapeutic agents, thereby treating cancer and preventing or reversing resistance to the therapeutic agents.

Provided is a medicine for treating or preventing cancer that contains a first active ingredient and that is used in combination with a second active ingredient, wherein: the first active ingredient is a compound represented by formula (1), a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable solvate thereof; and the second active ingredient is GDC-6036 or a pharmaceutically acceptable salt thereof.

Methods and / or devices to deliver pulmonary therapy to treat sleep disordered breathing are disclosed.

A noninvasive blood pressure monitor. The noninvasive blood pressure monitor may include an inflatable cuff, a pressure transducer, one or more air pumps, and a processor. The processor may control the air pump(s) so as to initiate inflation of the cuff. The processor may also identify an oscillometric signal in an output of the pressure transducer, and may determine an envelope of the oscillometric signal. The processor may also determine one or more characteristics of the envelope of the oscillometric signal, and may control the air pump(s) so as to stop inflation of the cuff based on the one or more characteristics of the envelope of the oscillometric signal.

The presently disclosed subject matter relates to compositions comprising Werner Syndrome Helicase (WRN) inhibitors and methods of using the same.

Provided herein are epinephrine spray formulations. Also provided herein are methods of treating anaphylaxis by administering epinephrine spray formulations to subjects in need of such treatment.

The invention relates to the technical field of image classification, in particular to a fetal heart monitoring image classification method and system based on a neural network. The method comprises the following steps: acquiring a fetal heart monitoring image, generating a standard monitoring image through Gaussian noise elimination and spatial domain normalization, then performing multi-channel convolution on the standard image to extract image convolution features, obtaining standard fetal heart features through compression regular optimization, performing morphological reconstruction to generate a fetal heart dynamic model, and finally obtaining the fetal heart dynamic model. Baseline features are identified based on historical data, then periodic variation quantification and safe floating range derivation are performed on fusion features, and finally a multi-scale discrimination framework is constructed to realize intelligent classification of fetal heart monitoring images. The fetal heart monitoring image classification method is more stable and more accurate.

Systems and methods for integrating a continuous glucose sensor 12, including a receiver 14, a medicament delivery device 16, a controller module, and optionally a single point glucose monitor 18 are provided. Integration may be manual, semi-automated and / or fully automated.

Provided herein are epinephrine spray formulations. Also provided herein are methods of treating anaphylaxis by administering epinephrine spray formulations to subjects in need of such treatment.

The invention relates to a method for operating an implanted, ventricular assist system (2), comprising the following steps: a) determine a first impedance parameter at a first point in time by means of the assist system (2), b) determine a second impedance parameter at a second point in time by means of the assist system (2), c) at least determine a change of the impedance parameter using the first impedance parameter and the second impedance parameter, or compare at least the first or second impedance parameter to a threshold value.

Systems and methods for monitoring glucose levels of a user are described. Data indicative of glucose levels of the subject is received. A count is assigned for each glucose episode based at least on an area under the curve of the each glucose episode in a dataset of time-correlated glucose data. A running sum of counts for each glucose episode in a time period is calculated. The running sum of the counts may be displayed in a numerator of a fraction and a target count goal in a denominator of the fraction. A progress indicator representative of the running sum of the counts relative to a target count goal for the time period may also be displayed. A length of a variable portion may be proportional to the running sum of the counts, and a total length of the progress indicator may be proportional to the target count goal.

Systems and methods for monitoring glucose variability in a subject are described. Data indicative of glucose levels of the subject is received from a sensor control device. A first glucose variability metric of the subject in a first time period is determined. The first glucose variability metric may be compared to a threshold. A first indicator is displayed if the first glucose variability metric does not exceed the threshold and a second indicator is displayed if the first glucose variability metric exceeds the threshold. Additional glucose variability metrics may be determined for subsequent time periods according to a rolling window, and the indicators may be displayed real time or in a report. The glucose variability metric may be a measure of variability compared to a baseline, a difference between a maximum and minimum glucose level, or time in or out of a target range.

A medical apparatus for a patient comprises an external system and an implantable system. The external system is configured to transmit one or more transmission signals, each transmission signal comprising at least power or data. The implantable system is configured to receive the one or more transmission signals from the external system, and to deliver stimulation energy to the patient. Methods of delivering stimulation energy are also provided.

The present invention relates to an early monitoring system for pancreatic cancer based on big data, including a data acquisition module, a feature extraction module, a risk assessment module and an early warning trigger module. The data acquisition module is used to obtain the user's laboratory test data, imaging test data and clinical symptom data. The feature extraction module is connected to the data acquisition module, and constructs a comprehensive feature vector by extracting biochemical indicator features, imaging features and symptom features. The risk assessment module uses a pre-trained deep learning model to analyze the feature vector and generate a pancreatic cancer risk probability. The early warning trigger module compares the risk probability with a preset threshold, generates an early warning signal when the risk probability exceeds the threshold, and pushes it to a medical service terminal to suggest further diagnosis. The present invention combines multimodal data and deep learning technology to achieve accurate early monitoring of pancreatic cancer and improve diagnostic efficiency and accuracy.

The invention relates to the technical field of child health monitoring and artificial intelligence, and provides a T1DM child blood glucose monitoring and early warning system. An existing blood glucose monitoring system is generally only based on single blood glucose data acquisition, and blood glucose fluctuation of children in different situations is difficult to accurately capture in real time. Through a multi-modal data fusion technology, multi-modal physiological features are integrated, and deep feature extraction and fusion analysis of data are realized. The system further combines a Q-learning algorithm to generate a personalized blood glucose early warning strategy, and utilizes a hidden Markov model (HMM) to identify the behavior situation of the child and optimize the early warning threshold setting. Meanwhile, data privacy is protected in a data distributed processing process through a federated learning framework and a differential privacy protection technology. The real-time performance and accuracy of blood glucose monitoring are improved, and intelligent and personalized health management and early warning services are provided for children and guardians of the children on the premise of protecting the privacy of the children.

Embodiments relate to device, system and methods for personalised drug dosing via a device, the device comprising a first chamber comprising a drug reservoir unit; a second chamber comprising an electronic unit comprising a control component; a third chamber comprising a drug delivery unit and a flow control component controlled via the electronic unit; wherein the device is fully implanted in a subject during its intended operational modes, and wherein the device releases a body temperature stable drug formulation into the subject at a flow rate such that a variation in the flow rate is within ±15% by volume.

The invention relates to a blood oxygen and heart rate monitoring system and method based on PPG signals and motion data, and relates to the technical field of medical health and intelligent wearable equipment. The system comprises a PPG signal acquisition module, a motion sensor module, a signal preprocessing module, a dynamic signal segmentation and quality evaluation module, an oxyhemoglobin saturation calculation module, a heart rate calculation module, a motion state recognition module, a self-adaptive Kalman filtering module and an output display module. The method and the device have the effects of realizing a function of identifying the motion state by the wearable equipment, improving the signal interference suppression capability and widening the application scene.

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) ecosystem, and, in some cases, for implementing extended reality (“XR”) for cardiac arrhythmia procedures. In various embodiments, a computing system might receive device data associated with a device(s) configured to perform a cardiac arrhythmia procedure to provide effective heart rhythm, might receive imaging data associated with an imaging device(s) configured to generate images of a portion(s) of a patient. The computing system might analyze the received device data and imaging data (collectively “received data”), might map the received data to a 3D or 4D representation of the portion(s) of the patient based on the analysis, and might generate and present (using a user experience (“UX”) device) one or more XR images or experiences based on the mapping.

The invention relates to the technical field of drug concentration detection, and relates to a clinical pharmaceutical blood concentration detection method, which comprises the following steps: capturing drug molecules in a blood sample based on a micro-fluidic chip, enabling a sensor to detect the drug molecules and combine with the surface of the drug molecules, and collecting high-sensitivity blood reaction data. According to the method, main variables in data are identified and utilized by adopting a principal component analysis method, so that the complexity of spectral data is effectively reduced, meanwhile, the most critical information for calculating the drug concentration is reserved, the data processing flow is optimized, the analysis speed and efficiency are improved, and the prediction of the drug concentration is more accurate; according to the method, more reliable data support is provided for clinic, the data is smoothed through the moving average algorithm, random fluctuation caused by sampling errors or external variables is reduced, the overall quality and credibility of the data are improved, errors can be obviously reduced, and therefore doctors are supported to conduct more accurate medicine dosage adjustment and curative effect evaluation.

A method for determining one or more of selection, positioning or placement of a surgical implant, the method includes:predicting function of an impaired anatomical structure in an unimpaired condition;predicting post-operative function of the structure for one or more implants;selecting one or more of the implant, the implant position, or the implant location to improve the predicted post-operative function.

The invention belongs to the technical field of medical signal processing, and particularly relates to a heart sound and electrocardio combined coronary heart disease detection method based on time-frequency fusion, which comprises the following steps: S1, performing data preprocessing on PCG and ECG original signals, including noise reduction, baseline drift elimination, power line interference elimination and the like, and performing normalization processing on the PCG and ECG signals to obtain a normalized PCG and ECG signal; a Welch method is adopted to generate power spectrum signals of heart sound and electrocardio, and time domain signals and power spectrum signals of PCG and ECG are segmented into equal-length fragments. In order to effectively solve the problem of inaccurate diagnosis caused by poor single-modal data quality, a heart sound and electrocardio dynamic fusion module is constructed based on a time-frequency fusion result of two signals, dynamic weight distribution is carried out, a proper classifier is selected for classification, and the diagnosis accuracy is improved by supplementing frequency domain information. And the weight is distributed according to the contribution of the heart sound and the electrocardiosignal, so that the detection and classification result of the coronary heart disease is more accurate.

The invention relates to the technical field of biomedical signal processing and intelligent health monitoring, and particularly discloses an intelligent heart rate monitoring method and system based on heart sound and electrocardiosignals, heart sound signals and electrocardiosignals of a user are collected in real time, quality evaluation is conducted on the collected signals through signal quality indexes, and accurate signals are extracted; extracting time domain and frequency domain features of the first heart sound and the second heart sound of the heart sound signal, fusing multiple features by adopting wavelet transform and a main frequency analysis algorithm, and calculating a heart sound abnormal coefficient to evaluate the abnormal degree of the heart sound signal; the vibration amplitude and frequency characteristics of P waves in the electrocardiosignals are extracted, and an electrocardiograph abnormal coefficient is calculated through a vibration amplitude abnormal coefficient and a vibration frequency fluctuation coefficient and used for evaluating the stability of the electrocardiosignals; based on the heart sound abnormal coefficient and the electrocardiogram abnormal coefficient, a decision tree model is constructed, a heart rate abnormal index is dynamically calculated, dynamic monitoring of the heart rate is achieved, a dynamic early warning mechanism is conducted, and heart rate abnormal information is output.

The invention discloses a method and equipment for predicting a tumor position under respiratory movement based on LSTM (Long Short Term Memory), and the method comprises the steps: collecting a 4DCT (4 Discrete Cosine Transform) image sequence of a tumor part of a plurality of patients under free respiration; based on the acquired 4DCT image sequence, extracting a movement track of a tumor centroid point position along with time change; sequentially inputting all tumor motion data points in each fixed time window on the motion trail into a CNN-Attention-LSTM model for offline training, wherein the CNN-Attention-LSTM model is used for predicting the tumor position at the next time; and in a radiotherapy stage, performing online real-time updating on the trained CNN-Attention-LSTM model by using the latest respiratory movement data of the current patient, and predicting the tumor position at the next time. The real-time position of the tumor is provided for the radiotherapy robot, the time delay of the whole system is compensated, and the tumor position tracking is accurately realized in real time.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention provides a tumor immunotherapy curative effect prediction system based on polarization characteristics of neutrophil, and relates to the technical field of biology, the system comprises a neutrophil detection module used for determining neutrophil information; the patient information module is used for acquiring tumor information and immune information of a patient; the immunotherapy scheme information module is used for acquiring immunotherapy scheme information of the patient; the historical data acquisition module is used for acquiring historical patient information; the immune state coefficient module is used for determining the immune state coefficient of the patient; the prediction model module is used for obtaining the predicted tumor size through the trained tumor size prediction model; the immune state prediction module is used for determining a predicted immune state coefficient; and the curative effect prediction module is used for determining a predicted tumor immunotherapy curative effect score. According to the invention, the predicted tumor immunotherapy curative effect can be evaluated according to two aspects of predicting the tumor size and predicting the immune state coefficient.

The invention provides a real-time intervention method and system based on blood glucose prediction of a diabetic patient, and the method comprises the steps: obtaining blood glucose monitoring data and multi-dimensional life monitoring data based on blood glucose monitoring equipment and intelligent equipment, and carrying out the timestamp alignment of the multi-dimensional life monitoring data and the blood glucose monitoring data; inputting the aligned multi-dimensional life monitoring data and blood glucose monitoring data into a pre-trained blood glucose prediction model, outputting a first blood glucose level prediction value and a second blood glucose level prediction value, and generating a real-time intervention suggestion according to the first blood glucose level prediction value and the second blood glucose level prediction value; if the first blood glucose level predicted value or the second blood glucose level predicted value is lower than a threshold value, graded early warning is carried out, corresponding intelligent equipment is triggered to execute corresponding response measures, and the early warning mechanism can achieve early warning through a real-time, normalized and highly-personalized intervention strategy in combination with intelligent operation of the intelligent equipment. The life quality of a diabetic patient is obviously improved; and the blood sugar control is optimized.

The invention relates to the technical field of medical data monitoring, in particular to a data monitoring system for chronic cardiovascular diseases. The system comprises a patient physiological index data flow acquisition module, a physiological index change feature vector conversion module, a physiological index feature collapse anomaly quantification module and a patient physiological index anomaly response early warning module. Different physiological index data can be collected in real time through intelligent wearable equipment, stream data fusion processing and baseline correction processing are carried out, time sequence synchronization processing and physiological feature vector conversion processing are further carried out, and real-time physiological index change feature vectors of a patient are obtained; and acquiring the physiological index data of the patient in the historical chronic cardiovascular case, performing feature collapse anomaly quantitative calculation and anomaly response processing, generating a physiological index change anomaly response signal of the patient, and acting on the intelligent wearable device to execute the corresponding patient physiological index anomaly early warning prompt work. According to the invention, comprehensive and real-time monitoring management of chronic cardiovascular disease patients can be realized.