12 results about "Activity index" patented technology

FIELD: clinical gastroenterology.SUBSTANCE: intended to predict the risk of relapse of autoimmune hepatitis after discontinuation of immunosuppressive therapy. The patient undergoes a clinical and biochemical examination and the relapse risk probability index is determined using the formula: PI RR = –0.394×(IGA≥ 9) + 1.252×(AT) + 0.915×(a-SLA / LP) + 0.555×(a-LKM1) – 0.692×(IgG norm) + 0.316×(obesity), where PI RR is the prognostic index of recurrence risk; HAI is the histological activity index of more than 9 according to Knodell (binary indicator: HAI ≥ 9 corresponds to the multiplier “1”; HAI < 9 corresponds to the multiplier “0”); AB – the presence of autoantibodies (binary indicator: detection of at least 1 corresponds to the multiplier “1”; absence of autoantibodies corresponds to the multiplier “0”); a-SLA / LP – antibodies to soluble liver / kidney antigen (binary indicator: presence – corresponds to the multiplier “1”; absence – corresponds to the multiplier “0”); a-LKM1 – antibodies to liver and pancreas microsomes (binary indicator: presence corresponds to the multiplier “1”; absence corresponds to the multiplier “0”); IgG – immunoglobulin G (binary indicator: normal Ig G level corresponds to the multiplier “1”; elevated IgG level corresponds to the multiplier “0”); obesity (BMI ≥ 30) – (binary indicator: presence of BMI ≥ 30 corresponds to a multiplier of “1”; BMI < 30 corresponds to the multiplier “0”). With the value of PI RR < 0.93 – low probability of relapse, PI RR > 0.93 – high probability of relapse.EFFECT: increased accuracy in predicting the risk of recurrence of autoimmune hepatitis after discontinuation of immunosuppressive therapy.1 cl, 2 ex

The invention provides a dry-mixed mortar strength evolution modeling method and system. The method comprises the following steps: acquiring synergistic excitation effect data (including an activity index and a hydration heat release curve) of the dry-mixed mortar waste residues, and analyzing reaction kinetics to generate a waste residue-cement interaction characteristic matrix; a temperature field and an acoustic emission signal in the hydration process are monitored in real time through a thermoacoustic synchronous sensing array integrating a thermocouple and piezoelectric ceramics, and a thermoacoustic coupling space-time distribution diagram is output based on space-time relevance; fusing the interaction characteristic matrix and the thermoacoustic distribution diagram by using a graph neural network, and constructing a waste residue / cement / water molecule multi-topology interaction diagram; and simulating a collaborative excitation path in the graph, dynamically mapping the topological graph into intensity evolution curves of different ages by adopting an intensity trajectory prediction algorithm, and quantifying the contribution degree of the waste residue. Accurate prediction of the waste residue resource utilization efficiency is achieved, the waste residue mixing amount is optimized, and the prediction precision is improved.

An EEG-based dementia diagnosis assistance device according to the present embodiment comprises: an EEG data preprocessing unit for preprocessing a collected EEG data set of a test subject, the collected EEG data set being divided into time intervals by setting a window length and a shift length; an Hjorth parameter extraction unit for extracting, for each time interval, Hjorth parameters including an activity index, a mobility index, and a complexity index; and a dementia classification unit for classifying the test subject into one from among a Alzheimer's disease (AD) group, a frontotemporal dementia (FTD) group, and a cognitive normal (CN) group on the basis of the extracted Hjorth parameters. Therefore, AD, FTD, and CN subjects can be classified using Hjorth parameters composed of three major indicators of activity, mobility, and complexity through time domain analysis on EEG datasets.

Disclosed are methods for managing a treatment in an adult individual having systemic lupus erythematosus (SLE) undergoing therapy for lupus nephritis. In aspects, the method comprises detecting a level of each of neutrophil gelatinase associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), monocyte chemotactic protein 1 (MCP-1), adiponectin, hemopexin, and ceruloplasmin in a urine sample obtained from the individual, the levels of which are used to determine a score at a first time point and a second time point. Whether the individual is responding to the therapy, in particular an induction phase of a therapy, is determined based on a comparison of the first and second score. The therapy may then be continued or adjusted based on that determination.

The invention provides a preparation method and application of a dental pulp stem cell preparation based on AI activity prediction, and the method comprises the following steps: collecting historical preparation parameters and corresponding activity indexes of the dental pulp stem cell preparation, constructing a historical database, and carrying out data preprocessing and core parameter screening; according to the method, an activity prediction model comprising a first sub-model and a second sub-model is constructed, bidirectional function prediction is realized through two-stage training, multiple types of activity indexes and comprehensive scores can be predicted based on preparation parameters, and the preparation parameters can be reversely recommended according to activity requirements; and preparing a compound preparation which takes the dental pulp stem cells as a core and PDD-ALN as a synergistic enhancement carrier based on model output. The method can accurately guide the preparation of the dental pulp stem cell preparation, and is especially suitable for the development of periodontitis treatment drugs.

The application provides a heart ex vivo perfusion and static preservation system based on a donor heart transport box, which comprises the following modules connected with each other: a data acquisition module for box basic state inspection, box environment detection, environment adjustment, and heart detection and positioning; an intelligent perfusion module for automatic drug perfusion and monitoring; an activity monitoring module for calculating a heart electrophysiological activity index, an epicardial injury parameter, and a lactate uptake ratio to obtain a heart activity index; an intelligent evaluation module for constructing a coupling model, outputting a donor heart instant quality score and an injury trend prediction, and performing heart abnormality monitoring and alarm; an energy consumption management module for built-in solid high-energy lithium battery pack and energy consumption prediction and optimization; and a safety monitoring module for data compression, hierarchical uploading, and remote encryption management and control. The application can standardize the heart preservation process operation, greatly reduce human errors, improve the quality and availability of remote heart transport and preservation, reduce the risk of organ rejection, and alleviate the contradiction between supply and demand.

The invention discloses a rice seedling blast disease intelligent analysis system based on remote sensing data analysis, and relates to the technical field of agricultural remote sensing and plant disease monitoring, the system comprises a physiological data acquisition module, a physiological feature extraction module and a disease severity calculation and severity grading module; wherein the physiological data acquisition module realizes in-situ non-invasive acquisition of the spectral reflectivity of rice seedling leaves, the maximum photochemical efficiency of a light system II and the water transport rate of stalks through various sensors such as a hyperspectral sensor and a chlorophyll luminoscope; the physiological feature extraction module fuses multiple parameters to calculate a leaf surface structure health index and a vascular function activity index; the disease grading module obtains an in-vivo disease load index through weighting, and outputs different disease grades in combination with a K-means clustering threshold value; the system solves the problems that traditional rice seedling blast diagnosis is high in subjectivity and hysteresis quality, and early-stage accurate recognition of diseases is achieved.

The invention relates to the field of biological waste treatment, and discloses a hermetia illucens age identification and intelligent feeding linkage kitchen waste treatment system, which comprises a data acquisition module, a data processing and activity index calculation module, a data processing and activity index calculation module, a data processing and activity index calculation module, a data processing and activity index calculation module, a data processing and activity index calculation module, and a data processing and activity index calculation module, the activity index is used for representing the activity state of the insect population and the adaptive capacity to the environment; the insect age identification module is based on a Transform model detected in real time and introduces the activity index to serve as an attention guiding signal so as to output insect age distribution, individual counting and health state labels; a feeding linkage control module; and a cloud management and self-learning module. By introducing multiple environmental factors such as temperature, humidity, gas concentration and the like and insect group activity information and combining with the improved deep learning model, the insect age can be identified more accurately, so that identification errors caused by factors such as illumination and insect body overlapping in a traditional method are effectively reduced.

The invention provides a system for evaluating lower limb strength and walking gait influence of a patient, which relates to the technical field of gait evaluation and comprises a data acquisition module, a synchronous control module and a data processing and analyzing module. The data acquisition module comprises a constant-speed muscle force test unit, a surface electromyogram signal acquisition unit and an optical motion capture unit; the synchronous control module is in communication connection with the data acquisition module; the data processing and analyzing module is configured to receive and time align the lower limb muscle strength data, the lower limb muscle electrophysiological data and the lower limb kinematics data; extracting a lower limb strength index, a myoelectricity activity index and a gait parameter from the aligned data; and based on the extracted indexes and parameters, establishing a correlation model among the lower limb strength indexes, the myoelectricity activity indexes and the gait parameters, and outputting an evaluation result. The method can generate remarkable effects from data islands to association fusion, from subjective experience to objective quantification, and from phenomenon description to pathogenesis revelation.

The application provides a biomarker combination model for evaluating endoscopic activity of pediatric Crohn's disease and a construction method and application thereof, and belongs to the technical field of biological medicine. + CD69 + The biomarker combination model of the proportion of T cells combined with the level of C-reactive protein can effectively evaluate the endoscopic activity of pediatric Crohn's disease patients through simple peripheral blood detection, avoids the invasiveness, high cost and potential risk of endoscopy, and is particularly suitable for pediatric patients who need frequent monitoring. The model has good diagnostic efficiency and can accurately distinguish patients in the endoscopic active phase from patients in the remission phase, thereby making up for the inconsistency between the clinical activity index and the endoscopic activity. The model provides a simple and objective non-invasive evaluation tool for the treatment decision, efficacy monitoring and long-term follow-up of pediatric Crohn's disease, and has important clinical application value and popularization prospect.

The application belongs to the technical field of medical rehabilitation, and discloses a rehabilitation treatment monitoring system based on multi-modal sensing and AI evaluation; three types of sensing data, namely visual skeleton tracking, inertial measurement unit and pressure distribution, are integrated to establish a multi-dimensional skeleton key point confidence evaluation mechanism, and potential tracking interruption risk frames and error recognition candidate frames are recognized in real time. A multi-modal fusion correction model is used to accurately compensate for the error recognition, and the missing data is reconstructed by combining a skeleton motion mode memory bank and a bidirectional time sequence prediction algorithm to generate a continuous and complete all-day skeleton motion sequence. Based on this, the patient's time period activity index and rehabilitation action completion score are calculated to provide objective and quantitative rehabilitation evaluation basis. The application improves the stability and continuity of skeleton tracking in a complex rehabilitation environment, enables the medical team to obtain the real and complete all-day activity image of the patient, accurately locates the rehabilitation bottleneck, formulates a personalized treatment plan, and promotes the functional recovery of the patient.