3373 results about "Internal medicine" patented technology

The invention relates to a multi-frequency bioelectrical impedance-based body fat accurate measurement method and system. The method comprises the following steps: receiving a mode selection instruction sent by a user to determine a current working mode; if the mode is a rapid measurement mode, applying single reference frequency and reference current intensity excitation, obtaining a single group of bioelectrical impedance data, and calculating a rapid body fat rate result; if the measurement mode is an accurate measurement mode, acquiring bioelectrical impedance data under a plurality of preset frequencies and a plurality of preset current intensities through a complete electrode system, performing signal processing on the data, extracting impedance amplitude and phase information, constructing an impedance characteristic curve, extracting characteristic parameters related to the body fat rate from the impedance characteristic curve, and calculating the body fat rate based on the characteristic parameters. And obtaining a final body fat rate result of the accurate measurement mode. According to the method, the body fat rate can be accurately measured, and the accuracy and reliability of a measurement result are improved.

The invention discloses a system and a method for intelligently monitoring blood sugar of a patient before and after diet based on food components, and belongs to the technical field of blood sugar monitoring, and the system comprises a food component quantification module which is used for converting a food image or description into accurate macro nutrient and micronutrient data; the multi-modal data fusion module is used for preprocessing and fusing the multi-modal data of the patient; the patient blood sugar prediction module is used for constructing a patient blood sugar prediction model to analyze the multi-modal fusion data of the patient and predicting the blood sugar of the patient; and the difference analysis optimization module is used for comparing actual blood glucose with predicted blood glucose after meal and analyzing difference reasons. The problems that blood glucose of a patient before and after eating cannot be intelligently monitored, and accurate quantification and prediction of the relation between diet and blood glucose cannot be achieved are solved. The system can intelligently monitor the blood glucose of the patient before and after eating, can help the diabetic to realize accurate quantification and prediction of the relationship between diet and blood glucose, and can effectively control the blood glucose and reduce the risk of complications.

The invention relates to the technical field of Tokamak plasma diagnosis and data fusion, in particular to a multi-modal fusion Tokamak high-frequency Thomson scattering reconstruction method. According to the technical scheme, the method comprises the following steps: acquiring multi-modal diagnosis data on a Tokamak device, wherein the multi-modal diagnosis data comprises high-frequency auxiliary diagnosis data and low-frequency Thomson scattering data; and performing feature engineering and physical enhancement processing on the high-frequency auxiliary diagnosis data. According to the method, through multi-modal diagnosis data fusion and interpretable KAN network modeling, the Thomson scattering time resolution is greatly improved on the premise that hardware is not transformed, and high-fidelity reconstruction of sub-millisecond transient processes such as ELM and magnetic island evolution is achieved; the method has physical interpretability, can reveal the implicit relationship between diagnostic quantities, enhances the robustness under complex experimental conditions through strategies such as modal loss, and provides key high-frequency diagnostic support for Tokamak high-performance operation and real-time control.

The invention provides a hierarchical labeling method and device for a lung cancer pathological image, equipment and a storage medium. Relates to the technical field of medical image processing. The method comprises the following steps: carrying out digital scanning and image preprocessing on a lung cancer pathological section; labeling according to a three-stage progressive sequence of a macroscopic tissue area, a microscopic characteristic structure and a cellular level characteristic, wherein each stage adopts color coding and diagnosis priority rules exclusively corresponding to lung cancer subtypes and pathological characteristics; carrying out post-processing and standardized output on the labeling result; and training an AI model by using the standardized annotation data and applying the AI model to auxiliary diagnosis. Through a unified color coding system, a hierarchical labeling framework and a standardized data processing flow, the problems that an existing labeling system is disordered and poor in reusability are solved.

The invention relates to the field of image analysis, in particular to a colon cancer lymph node metastasis risk prediction method and system based on image analysis. The method comprises the following steps: acquiring a pathological section scanning image, carrying out layer-by-layer downsampling and feature reconstruction, and generating pathological section topological features; carrying out adaptive clustering analysis and multi-parameter fusion evaluation on the basis of the pathological section topological features to obtain independent migration evaluation values of individual tumor cells; performing tumor metastasis branch analysis according to the independent migration evaluation value, and constructing a metastasis branch path distribution diagram; extracting an immunohistochemical staining image of a patient to obtain immune cell space distribution data; and performing multi-region lymphatic metastasis simulation and metastasis risk situation prediction based on the immune cell spatial distribution data and the metastasis branch path distribution diagram to obtain a final metastasis risk assessment result. According to the method, the lymph node metastasis risk is accurately and efficiently analyzed, and the credibility of a pathological analysis result is improved.

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The invention relates to the technical field of lithium battery health state prediction, in particular to a lithium battery health state prediction method and system, and the method comprises the following steps: initializing a basic health state estimation value for a battery cell, and correcting the internal resistance of the battery cell in combination with real-time operation data; analyzing an independent characteristic quantity from the real-time operation data of the battery cell by combining the corrected internal resistance and a basic health state estimation value; calculating the initial health state of each battery cell based on the corrected internal resistance and the independent characteristic quantity; according to the heat distribution characteristics in the battery pack, correcting the initial health state to obtain the final health state of the battery cell, and performing fusion calculation on the overall health state of the battery pack; updating the final health state to a basic health state estimated value of the next round of prediction; and based on the overall health state, performing feedback adjustment on subsequent prediction. According to the application, the influence of the temperature gradient in the battery pack on the internal resistance and health state evaluation of each battery cell can be dynamically sensed and compensated, and the prediction precision and consistency can be improved.

The invention discloses a deformation compensation method for a 3D printing model of an oral implanting guide plate, and belongs to the technical field of computer-aided design and manufacturing. The method comprises the steps that original three-dimensional model data of the oral implanting guide plate is obtained; performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data, and generating a dynamic stress evolution field for describing the internal stress distribution and change of the model in the virtual printing process; based on the dynamic stress evolution field, generating an adaptive compensation transformation matrix for correcting geometric deformation; and performing compensation transformation on the original three-dimensional model data by using the self-adaptive compensation transformation matrix to generate high-fidelity compensation model data. A self-adaptive compensation transformation matrix is generated through dynamic hierarchical stress accumulation analysis to preprocess three-dimensional model data, complex deformation in the printing process can be accurately predicted and compensated, and therefore the forming precision of a printed piece is remarkably improved.

The invention discloses a plateau newborn rare disease multi-mode combined screening system and a storage medium, and belongs to the technical field of medical software development. The plateau adaptability newborn rare disease multi-mode combined screening system is constructed, the plateau adaptability of the screening system is enhanced, the cost is remarkably reduced, and the screening system has a good application prospect.

The invention discloses a computer-aided drug screening method, system or device based on FBXO2 and PKM2. The invention provides a brand-new method, system or equipment for screening oral squamous cell carcinoma treatment drugs based on FBXO2 and PKM2, provides a tool for new drug development and clinical application for treatment of oral squamous cell carcinoma, and has a wide application prospect.

The invention discloses a literature metrology and clinical data fusion lung cancer knowledge graph construction method and device, which can solve the problems of research and clinical disjunction, data fragmentation and AI'black box ', and realize systematic mining, verification and interpretable application of lung cancer diagnosis factors. The method comprises the following steps: (1) mining high-frequency and emerging diagnosis factors from scientific research literatures through a multi-dimensional algorithm, and generating academic prior weights; (2) enabling the literature candidate factors to correspond to real patient data, and performing structured matching to obtain an aligned clinical candidate factor set; (3) screening out a core factor having a reliable causal relationship with the lung cancer outcome from the clinical candidate factors, and providing a causal-driven skeleton for the knowledge graph; (4) establishing an initial knowledge graph based on D3 and G, and dynamically updating an edge weight based on newly added data; and (5) outputting a clinical interpretable report which comprises a puncture score Pbiopsy, driving factor contribution degree ranking, an evidence chain and a recommended path.

The invention relates to the technical field of lithium ion battery health monitoring, and discloses a lithium ion battery health state estimation method based on a physical neural network, which comprises the following steps: reconstructing an SEI membrane growth process of a lithium battery, fitting the growth process through the physical neural network, and analyzing voltage time sequence data in a lithium battery charging process to obtain a lithium ion battery health state estimation result. Calculating a health index in a 71%-90% state of charge range of a lithium battery charging interval; according to the estimation method, the health indexes are extracted by analyzing short-period data of the charging state of the battery within the range of 71%-90%, the method is successfully applied to four data sets of different chemical systems including LFP, NCM, NCA and the like and multiple charging and discharging protocols, and the good applicability of the method to different battery types and working conditions is proved.

The invention provides an alkaloid compound as well as a preparation method and an application thereof in tumor resistance. The alkaloid compound provided by the invention shows a remarkable cytotoxic effect on human non-small cell lung cancer cells A549, mouse lung adenocarcinoma cells Lewis, human triple negative breast cancer cells MDA-MB-231 and mouse breast cancer cells 4T1, and can be used for preparing medicines for treating breast cancer, lung cancer and the like.

1. Name of the product in this design: Wearable Dynamic Electrocardiogram Recorder (ECG-D01H02). 2. Purpose of this design: It is a wearable dynamic electrocardiogram recorder used to monitor and record the electrocardiogram waveform of a human body. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention relates to the technical field of data processing, in particular to a multichannel medicine collaborative intelligent infusion system for the department of cardiology. The system determines the clinical similarity according to the clinical feature index of the current object and the clinical feature index of each historical object; determining the historical cases with the clinical similarity exceeding a first threshold value as a reference historical case set; aiming at each historical case in the reference historical case set; according to the real-time physiological monitoring data in the current time period, the physiological monitoring data of the historical cases in the historical similar time period, and the fluctuation difference between the physiological monitoring data of the historical cases in the historical similar time period and the physiological monitoring data of the historical cases in the next adjacent historical time period; according to the difference between the real-time infusion parameter of the current time period and the infusion parameter of the historical case in the historical similar time period, determining an adjustment weight; weighted fusion is carried out on infusion parameters in historical similar time periods by adjusting weights, target infusion parameters are determined, and therefore it is ensured that the target infusion parameters are fused with the quintessence of historical optimal cases.

The present invention relates to methods of treating prostate cancer in a subject in need thereof, comprising administering a therapeutically effective amount of a KLK2xCD3 bispecific antibody or bispecific binding fragment thereof to the subject to treat the prostate cancer.

The disclosure relates to compositions comprising a phentermine and a unicyclic aminoketone and methods of use thereof. The methods of use include inducing weight loss or reducing weight gain in a human subject. The unicyclic aminoketone may be diethylpropion or bupropion. In some embodiments, the compositions further include tadalafil or naltrexone.

The invention discloses a multi-modal contrast learning fault diagnosis method for a small sample scene, and the method comprises the following steps: carrying out the enhancement of a one-dimensional signal and two-dimensional image fused fault data set through physical simulation for the small sample scene with scarce industrial fault data, and constructing a positive and negative sample pair through a plurality of data enhancement strategies; based on heterogeneous multi-modal fault data, designing a double-flow encoder architecture of a time sequence branch and an image branch, extracting depth features and mapping the depth features to a unified feature space through a projection head; performing supervised contrast learning pre-training based on intra-modal and inter-modal dual contrast loss; supervision fine tuning is carried out based on multiple loss functions such as physical guidance, so that accurate diagnosis of equipment faults is realized in a small sample scene. According to the fault diagnosis method under the unbalanced sample and limited labeling conditions, the problem that a traditional data driving model depends on large-scale labeling samples is effectively relieved through supervised comparative learning and cross-modal information alignment.

The invention includes methods of preventing or treating acute lung injury using a MAP3K2 / MAP3K3 inhibitor. The invention further comprises compositions, and kits comprising compositions useful within the invention.

The invention relates to a lung cancer drug resistance risk assessment system based on multi-modal data and a lightweight network, and relates to the technical field of lung cancer drug resistance risk assessment, and the system comprises a data clustering division module which is used for obtaining sample multi-modal data and sample drug resistance data of historical patients, extracting a response distribution mode for clustering division, and obtaining a plurality of response subgroups; the network library construction module is used for training a plurality of specific evaluation networks to form a specific evaluation network library; the mode matching retrieval module is used for acquiring a target response distribution mode of the target patient and performing network matching; and the drug resistance risk assessment module is used for acquiring target multi-modal data of the target patient, performing risk assessment in combination with the network matching result, and generating a drug resistance risk assessment result. The problems that a traditional evaluation method mainly depends on single-mode data, information is single, evaluation accuracy is poor, and individual differences of patients are difficult to adapt are solved.

To provide a method for selecting a subject for whom the effect of a pharmaceutical composition for treating or preventing cancer can be expected.SOLUTION: A step of determining an expression level of WT1mRNA using a sample collected from a subject, and a step of providing an indicator that the subject is a subject for whom the pharmaceutical composition is expected to be effective when the expression level of WT1mRNA is less than or equal to or less than 10000 copies / μ gRNA.SELECTED DRAWING: None

The invention relates to the technical field of breath monitoring, in particular to a big data intelligent health breath dynamic monitoring method and system and a terminal. The method comprises the following steps: acquiring a time domain interval of each respiratory cycle according to change characteristics of a respiratory pressure signal; further obtaining work doing amount corresponding to each breathing cycle, and dividing breathing modes; the newest seasonal period of the respiratory pressure signal is further extracted, and a respiratory stability coefficient is obtained according to the change characteristics of work doing amounts of respiratory periods with adjacent time sequences in the seasonal period in combination with the number distribution characteristics of the respiratory periods of the respiratory mode; furthermore, when the breathing stability coefficient is larger than or equal to the preset stability threshold value, the predicted breathing pressure signal is obtained and verified, and finally the predicted breathing mode is obtained according to the final predicted breathing pressure signal, so that the hysteresis of real-time processing is reduced, the response speed of breathing support is increased, and the comfort is improved.

The invention discloses a cerebral hemorrhage risk prediction method and system based on multi-modal learning, and relates to the technical field of medical detection. The cerebral hemorrhage risk prediction method and system based on multi-modal learning comprises the following steps: S1, collecting craniocerebral multi-modal data, and preprocessing the craniocerebral multi-modal data; s2, identifying a high-gray-scale suspicious region, quantifying bleeding features of the corresponding region, and marking a suspected hematoma region; s3, the cerebral hemorrhage risk degree of the suspected hematoma area is evaluated, the risk level is judged based on the evaluation result, and a hierarchical regulation and control strategy is generated; and S4, based on the cerebral hemorrhage risk assessment results of the current assessment period and the historical assessment period, performing quantitative assessment on the cerebral hemorrhage risk level fluctuation state, and dynamically updating the risk threshold. The problems that in early cerebral hemorrhage recognition in the prior art, tiny hemorrhage lesions are difficult to find, risk dynamic evaluation cannot be achieved, and therefore clinical judgment and intervention opportunities are affected are solved.

The invention relates to application of hydroxynidone to preparation of a medicine for preventing and / or treating radiation-induced pulmonary fibrosis. Pharmacological experiments prove that the hydroxynidone has a definite resisting effect on radioactive rat lung injury including early inflammatory exudation and pulmonary interstitial fibroblast hyperplasia, and can obviously reduce pulmonary alveolar inflammatory cell exudation and reduce the content of TGF beta 1 in inflammatory exudate; the medium and large doses have the effect of delaying the development of interstitial pneumonia towards pulmonary fibrosis; the lesion degrees of the small, medium and large dosage groups are obviously reduced and are in a dose-effect relationship.

The present disclosure provides, among other things, methods for treating cancer comprising administering an effective dose of azenosertib, or a pharmaceutically acceptable salt thereof, to subjects selected to have a homologous recombination repair deficiency (HRD).

Apparatus and associated methods relate to management of therapeutic digital assets in a non-medical environment. In an illustrative example, non-medical digital asset(s) (NMDAs) may be evaluated relative to one or more therapeutic modalities. A corresponding therapeutic digital asset (TDA) may, for example, be generated for each therapeutically associated NMDA. The TDA may, for example, be traceably associated with corresponding risks and / or risk mitigations. Each TDA may be associated, for example, with a test profile corresponding to a risk and / or risk mitigation. A TDA may be released into a non-medical digital content delivery environment (DCDE), for example. The TDA may, for example, be monitored in the DCDE to maintain verification and / or validation (VV). A change in the DCDE affecting VV of the TDA may, for example, result in deactivation of the TDA in the DCDE. Various embodiments may advantageously permit VV of selected digital assets in a non-medical environment.

The invention belongs to the field of biomedical engineering, and relates to a nutritional composition for delaying muscle attenuation and bone mass reduction based on muscle-bone axis interaction and a preparation method of the nutritional composition. In particular to a nutritional composition which comprises or consists of beta-hydroxy-beta-methylbutyric acid or salt thereof and milk-derived basic protein; wherein the mass ratio of the beta-hydroxy-beta-methylbutyric acid or the salt thereof to the milk-derived basic protein is (120: 1)-(5: 1). According to the nutritional composition disclosed by the invention, through the synergistic effect of the beta-hydroxy-beta-methylbutyric acid or the salt thereof and the milk-derived basic protein, the muscle-bone axis is jointly regulated and controlled, and the comprehensive improvement effect of muscle-bone health which cannot be achieved by a single component is achieved.

The invention provides a diabetic patient life management method and system in combination with traditional Chinese medicine knowledge, and relates to the field of medical health data processing, the method comprises the following steps: obtaining multiple groups of sample data, the sample data at least comprising personal information of sample patients and diabetic traditional Chinese medicine syndromes, and the personal information of the sample patients at least comprising diabetes index data; based on the multiple groups of sample data, multiple key traditional Chinese medicine syndrome discrimination units are determined, and a diabetes traditional Chinese medicine syndrome diagnosis scale is generated; based on the multiple groups of sample data, determining music types matched with different diabetes mellitus traditional Chinese medicine syndromes; determining the diabetic traditional Chinese medicine syndrome of the current user based on the personal information of the current user and the filling information of the diabetic traditional Chinese medicine syndrome diagnosis scale; based on the diabetic traditional Chinese medicine syndromes of the current user and the music types matched with different diabetic traditional Chinese medicine syndromes, music, traditional Chinese medicine health maintenance skills and traditional Chinese medicine recipes are recommended, and the method has the advantage of improving the quality of life management of diabetic patients.

The invention discloses an infusion state and vital sign intelligent monitoring method based on multi-modal fusion, and relates to the technical field of sign monitoring, and the method comprises the following steps: respectively collecting infusion dripping speed, pipeline liquid state, infusion pressure and breathing and heart rate signals of a patient; filtering the original signal of each sensor; key features are extracted from the processed signals, wherein the key features comprise parameters including the average dripping speed, the liquid existence proportion, the pressure change rate, the respiratory rate and the heart rate; carrying out normalization and confidence weighted fusion on the extracted infusion and vital sign characteristics, calculating an infusion state index and a vital sign index, and further obtaining a combined abnormal index; performing three-level anomaly judgment according to the fused index; monitoring data and alarm information are uploaded to a hospital information system in real time. By introducing a judgment algorithm based on multi-modal data fusion and confidence coefficient weighting, monitoring and graded alarming of the infusion state and the vital signs of the patient can be achieved.

A trace element supplement consisting of lutein, zeaxanthin, astaxanthin, anthocyanins, and proanthocyanidins is designed to improve retinal health in children and adolescents, relieve eye strain, and slow the progression of myopia. The daily dose is 1–20 mg lutein, 0.2–4 mg zeaxanthin, 0.5–8 mg astaxanthin, 10–700 mg taurine, and 0.5–8.5 mg zinc; the nighttime dose is 1–100 mg eyebright extract, 10–300 mg chamomile extract, 1–200 mg anthocyanins, and 1–500 mg proanthocyanidins.