20 results about "Acute stage" patented technology

The invention provides a clinical diagnosis and treatment path generation method and system for tracking a chest pain patient. Wherein a dynamic spatio-temporal data atlas is constructed by synchronously collecting respiratory rhythm phase difference, coronary artery calcification integral and hypersensitive troponin curve slope, and risk degree transition signals are generated by coupling analysis of ST segment elevation amplitude asymmetric growth, left ventricular ejection fraction attenuation gradient and B-type natriuretic peptide release rate. In the acute stage, the constraint relation between a vascular recanalization time window and thrombolysis taboo is dynamically bound, in the subacute stage, a phase synchronization rule for enhancing CT scanning and anticoagulant drug dosage is established through a multidisciplinary interface, intravascular ultrasonic plaque morphological mutation characteristics and extracorporeal circulation parameters are fused, the blood supply reconstruction time sequence is dynamically adjusted based on a myocardial oxygen consumption balance equation, and the thrombolysis time sequence is determined. And path self-optimization is realized through space-time superposition of an interventional therapy delay parameter and a drug metabolism curve. According to the technical scheme provided by the invention, chest pain diagnosis and treatment path dynamic optimization and multi-modal parameter collaborative decision-making are realized, and the risk layering precision and the treatment timeliness are improved.

The invention relates to the technical field of markers, in particular to application of a biomarker for identifying acute stage and chronic stage of brucellosis. A series of biomarkers related to acute and chronic stages of brucellosis are screened and found, and the biomarkers have obvious difference between acute and chronic brucellosis patients. The biomarkers can realize acute and chronic staging of brucellosis, are high in sensitivity and specificity, and have relatively good diagnosis efficiency. A novel molecular diagnosis tool is provided for acute and chronic staging of brucellosis, the problems that existing serological detection is low in specificity, and clinical staging cannot be accurately judged are solved, and wide application prospects are achieved.

The present disclosure provides a method for treating post-acute sequelae of severe acute respiratory syndrome (SARS)-CoV-2 infection (PASC) or one or more symptoms associated with said PASC, said method comprising administering to a subject in need thereof or at risk thereof a pharmaceutical composition comprising a therapeutically effective amount of cyclobenzaprine or a pharmaceutically acceptable salt thereof and a pharmaceutically acceptable carrier. The present disclosure provides, in some embodiments, a method and a pharmaceutical composition for treating post-acute sequelae of (SARS)-CoV-2 infection (PASC) or one or more symptoms associated therewith in a subject in need thereof or at risk thereof, wherein said pharmaceutical composition comprises a therapeutically effective amount of cyclobenzaprine or a pharmaceutically acceptable salt thereof and a pharmaceutically acceptable carrier.

A method and system for differentiating acute and chronic pain based on the functional state of specific neural circuits is proposed. This method utilizes in vivo two-photon / endoscopic calcium imaging and ex vivo brain slice patch-clamp techniques to longitudinally monitor the activity dynamics of neurons in the DRN and ZI brain regions during the acute and chronic phases of a neuropathic pain model. By extracting and analyzing calcium and electrophysiological signal parameter sets, distinguishing criteria are established: if the activity of 5-HTergic neurons in the DRN is specifically enhanced in the acute phase, the indication is acute pain; if the activity of GABAergic neurons in the ZI is specifically enhanced in the chronic phase, the indication is chronic pain. This invention achieves an objective and precise differentiation of the transition from acute to chronic pain at the level of specific neural circuits, providing a new strategy and tool for pain mechanism research and clinical diagnosis and treatment.

The invention relates to the technical field of physiological data monitoring, in particular to an orthopedic postoperative patient physiological data analysis method and system.The orthopedic postoperative patient physiological data analysis method comprises the following steps that heart rate blood oxygen and limb activity data are collected, a heart rate fluctuation value, a blood oxygen change value and an activity range are extracted, and a multi-dimensional physiological feature sequence is constructed; and calculating a data change range in the sliding window of each dimension and a frequency change value of adjacent windows. According to the method, fine quantitative analysis of heart rate fluctuation, oxyhemoglobin saturation and limb movement amplitude is achieved by dynamically adjusting data time span and calculating indexes in a segmented mode, change characteristics of different recovery stages are adapted, data cleaning accuracy is improved through high-density region statistics and noise elimination, scientific induction of dynamic characteristics is ensured, and the method has good application prospects. In combination with feature extraction and trend analysis of an acute period, a functional reconstruction period and a stable period, comprehensive evaluation of inter-stage features is formed, and based on an offset rate, a prediction deviation value and abnormal signal trajectory labeling, the anomaly identification and real-time tracking capability is enhanced.

The application discloses an intelligent first-aid evaluation system for fall injury of the elderly, which comprises a data acquisition module for acquiring fall impact parameters, bone metabolism markers, bone density historical data and patient clinical characteristics; a joint feature space construction module for generating a mechanical impact feature vector, a bone metabolism state feature vector and a six-dimensional cross-coupling feature, which are spliced into a joint feature space after being weighted by part-specific conditional features; a probability prediction module adopting a multi-task integrated learning model composed of a shared bottom layer and five part-specific task-specific layers to output the probability of occult fracture of each part; a risk stratification module for dividing into four risk grades according to a preset threshold; and an image examination recommendation module for generating a differentiated image examination strategy. Through cross-fusion of mechanical parameters and bone metabolism data, the problems of long-term separation of the two types of data, lack of an acute phase prediction model and lack of part-specific evaluation in the prior art are solved, and early quantitative prediction of occult fracture after falling and accurate image examination guidance are realized.

Provided is a therapeutic agent for spinal cord injury from acute to subacute phases, particularly a therapeutic agent for severe spinal cord injury, which is difficult to treat in the past. The invention discloses a therapeutic agent for spinal cord injury from an acute stage to a subacute stage. The present invention is characterized by comprising: (1) (a) a hepatocyte growth factor protein or a substance having a c-Met phosphorylation effect equivalent to that of a hepatocyte growth factor protein, or (b) a gene encoding a hepatocyte growth factor protein or a gene encoding a substance having a c-Met phosphorylation effect equivalent to that of a hepatocyte growth factor protein; and (2) pluripotent stem cells.

The invention relates to medicine and can be used in neurosurgery, traumatology, neurology, rehabilitation. The claimed invention is aimed to create the implant suitable for regeneration of injured nerve tissues of various types, in any period of the nerve tissue severe injury, in particular, of the spinal cord, immediately after relief of disturbed vital functions for the early and stable restoration of nerve tissue conduction in the acute period, prevention from or reduction of the demyelination processes. The implant is made of porous polytetrafluoroethylene (PTFE) with potentialities to maintain the histoenzymatic activity of neurons and restore the injured nerve tissue histostructure, such implant to contain at least a flat cylinder of 0.5 mm to 1. 5 mm high for placement in the injured nerve tissue area, with the flat cylinder's foot edges to contact undamaged nerve tissue areas, wherein the porous PTFE material has a three-dimensional structure containing open through pores and dead-end pores, evenly distributed over inner surface of the open through pores;, wherein the open through pores sizes and the dead-end pores sizes are randomly distributed within the range of 150-300 m.

The application relates to the technical field of markers, in particular to application of biomarkers for identifying acute and chronic stages of brucellosis. A series of biomarkers related to acute and chronic stages of brucellosis are screened and found, and the biomarkers are significantly different between patients with acute and chronic brucellosis. The biomarkers can be used to realize acute and chronic staging of brucellosis, and have high sensitivity and specificity and good diagnostic efficiency. The application provides a new molecular diagnostic tool for acute and chronic staging of brucellosis, solves the problems of low specificity of existing serological detection and incapability of accurately judging clinical stages, and has a wide application prospect.

The invention discloses a stroke prognosis prediction method and device, a medium and equipment. The method comprises the following steps: acquiring resting-state electroencephalogram data of an acute-stage acute ischemic stroke patient, calculating a power spectrum and a micro-state index of the resting-state electroencephalogram data, combining baseline information and clinical examination data of the patient to construct a stroke prognosis prediction feature set, inputting the stroke prognosis prediction feature set into a model obtained by training through Lasso regression and a plurality of machine learning algorithms, and obtaining the stroke prognosis prediction feature set. And outputting a functional prognosis result of the patient. In the process, the advantage that electroencephalogram data reflect brain function changes is fully utilized, the defect that the brain function value is neglected in the prior art is overcome, interference of human factors is reduced through machine learning, and the objectivity and accuracy of prognosis evaluation are improved.

The invention provides an aortic dissection acute and chronic stage classification method and device based on deep learning, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring two adjacent aorta cross-section interface ultrasonic images of a subject at the same position of at least two different time points; extracting aorta wall features and inner diaphragm features in the ultrasonic image; constructing a coordinate system comprising an x axis and a y axis by taking an intersection point of the aorta wall and the inner diaphragm features as an original point, and indexing areas where the inner diaphragm features in the two ultrasonic images are located by taking the x axis and the y axis as references to obtain two groups of coordinate point sets; the difference value of the inner diaphragm feature points at the same position on the x axis and / or the y axis is calculated; and outputting an auxiliary prediction result that the subject is acute or chronic according to the difference value. According to the method, by introducing the deep learning technology, automatic classification of the acute stage and the chronic stage of the aortic dissection is achieved, rapid and objective diagnosis and evaluation are expected to be achieved, and clinical decision making is assisted.

The invention relates to the technical field of signal processing, and provides a bio-electricity signal adjusting method and device for hemorrhoid rehabilitation and a storage medium. The bio-electricity signal adjusting method for hemorrhoid rehabilitation comprises the steps that an acute stage symptom score of a patient is obtained, and a parameter set of a bio-electricity signal is matched based on the symptom score; outputting a corresponding bio-electricity signal based on the parameter group, and monitoring tissue parameters of the target area in real time; based on the change of the tissue parameters, judging the tissue state of the target area; and dynamically adjusting the parameter group according to a preset step length based on the organization state. The parameter set of the bio-electricity signal is adaptively adjusted in real time according to the change of the tissue state of the hemorrhoid patient in the acute stage, and the problems that the rehabilitation period of the hemorrhoid patient in the acute stage is long, pain relieving is slow, and local stimulation is prone to aggravating are solved.

This invention relates to the fields of biomedicine and nanomaterials technology, and provides a bilayer core-shell nanoparticle composite thermosensitive gel for postoperative analgesia, its preparation method, and its applications. The bilayer core-shell nanoparticles comprise: a core of PLGA nanoparticles loaded with magnesium, and a shell of a lipid bilayer loaded with bupivacaine. The composite thermosensitive gel is made by uniformly dispersing the bilayer core-shell nanoparticles in a thermosensitive gel material. By constructing a structure with a lipid shell loaded with bupivacaine and a PLGA core loaded with magnesium, and combining it with a thermosensitive gel, this invention achieves rapid release of bupivacaine for acute postoperative analgesia, and sustained slow release of magnesium ions for long-term anti-inflammatory and repairing effects. This perfectly meets the temporal requirements of postoperative pain management and has broad clinical application prospects.

The invention relates to the field of medical monitoring, in particular to a blood pressure dynamic measurement frequency adjusting method and system based on patient condition staging, and the method comprises the steps: obtaining clinical data of a patient, judging that the patient is in an acute stage, a subacute stage or a stable stage according to a preset standard, matching a corresponding initial blood pressure measurement frequency according to a staging result, and executing monitoring. Blood pressure data are recorded and analyzed in real time, if abnormal changes of the blood pressure are found, the measurement frequency is dynamically increased according to the abnormal severity degree, an acousto-optic-electric alarm is triggered to prompt medical staff, meanwhile, the system regularly judges the disease stage of a patient again, if the stage changes, the monitoring frequency is automatically adjusted according to the new stage, and data in the whole process are stored. Closed-loop management integrating staging judgment, dynamic monitoring, intelligent alarm and self-adaptive adjustment is formed, and therefore optimal configuration of monitoring resources and timely intervention of patient risks are achieved.

The invention discloses an acute ischemic stroke prognosis prediction model and a construction method, and relates to the technical field of disease prediction, and the key points of the technical scheme are that the method comprises the following steps: S1, obtaining a data set which comprises baseline information, prognosis information and meteorological data; S2, processing the data: firstly, randomly dividing the data set into a training set and a test set, filling missing data, and balancing patient distribution; s3, constructing a model: constructing the model by using six ML algorithms, and training the model by using the training set; and S4, evaluation and screening: performing performance evaluation on all the models by using the test set, and screening out an optimal model. According to the method, the ML model fusing weather and clinical characteristics is constructed, so that the accuracy of predicting AI S prognosis is improved, and decision support is provided for a patient diagnosis and treatment strategy.

The invention provides a group of biomarkers for identifying acute stage and intermittent stage of gout and application of the biomarkers, and relates to the technical field of biomedicine. The invention provides a new biomarker for identifying the acute stage and the intermittent stage of gout and a distinguishing system constructed by applying the biomarker, so that the acute attack stage and the intermittent stage can be distinguished, timely symptomatic treatment is facilitated, and the treatment process is accelerated.

The invention provides a marker for monitoring acute stage of myasthenia gravis and application of the marker. The marker comprises mononuclear / macrophages, caspase-1, IL-1beta, PFKFB3 and PDK3. Experimental research proves that after an individual enters an acute stage of myasthenia gravis, the proportion of mononuclear / macrophage and the expression levels of caspase-1, IL-1beta, PFKFB3 and PDK3 are remarkably increased, and different expression changes are shown in different development stages (early stage, middle stage and late stage) of myasthenia gravis. Through clinical verification, the mononuclear / macrophage, the caspase-1, the IL-1beta, the PFKFB3 and the PDK3 can be used as remarkable markers for monitoring the acute stage of the myasthenia gravis, and a new target spot is provided for treatment of the myasthenia gravis.

The application discloses a kind of small stroke patient family caregiver burden risk prediction method and system, comprising: constructing research cohort and collecting the baseline clinical data of patient acute phase and the baseline information of family caregiver;Through statistical analysis, the independent correlation of the prediction variable of caregiver medium-severe burden is screened out;Based on the variable and its regression coefficient screened out, a nomogram prediction model is constructed, the score of each variable value is converted, the individual risk probability is predicted by accumulating total score;And ROC curve and Bootstrap resampling method are used to verify the model.The system includes data input, prediction calculation and result output module, and can be integrated into clinical information system.The application can realize individualization, quantification early warning of future burden risk of family caregiver of small stroke patient in acute phase, provide key decision support for implementing precise nursing intervention, effectively improve nursing resource efficiency and family care quality.

[Problem] To suggest the possibility of olfactory / gustatory abnormality (impairment) by means of a numerical value based on the metabolism of tryptophan in the kynurenine pathway. [Solution] A system 1 for evaluating a person suffering from COVID-19 comprises: an acquisition means 10 for acquiring a numerical value based on the metabolism of tryptophan in the kynurenine pathway obtained by analyzing serum S during an acute phase of COVID-19 in a person suffering from COVID-19; and an evaluation means 30 that indicates the possibility of olfactory / gustatory abnormality (impairment) in the person suffering from COVID-19 on the basis of the numerical value (for example, a KTR value or a blood concentration value for quinolinic acid).

The invention relates to the field of cardiac magnetic resonance image analysis, and discloses an STEMI patient two-year MACE event prediction method based on multi-sequence CMR, and the method comprises the following steps: obtaining a plurality of CMR sequence images and clinical data of an acute stage of an STEMI patient; segmenting and extracting the CMR sequence image by using a heart ROI extraction model to obtain a heart region CMR image, and preprocessing the heart region image; establishing a prediction model by using a heart area CMR image and clinical data, randomly initializing model parameters, and performing model training by using a training set sample; and using the trained prediction model to predict the two-year MACE event occurrence probability of the patient. According to the two-year MACE event prediction model, a plurality of CMR sequences and clinical data are adopted to predict the two-year MACE event, and meanwhile, the reliability of a prediction result is improved by using a multi-sequence image and multi-modal data.