509 results about "Pregnancy" patented technology

This invention relates to the field of giant panda reproductive technology, and in particular to a method for predicting true pregnancy in giant pandas. Based on the physiological characteristic that the mother's immunity gradually decreases during pregnancy due to reproductive stress, this invention predicts true pregnancy in female giant pandas. It can accurately and scientifically determine whether a mated giant panda is truly pregnant, and the prediction results can be obtained 1-2 weeks before the actual delivery date. This is significant for making early changes to the feeding and management of truly pregnant giant pandas, the formulation and implementation of newborn panda delivery procedures, and the optimization of reproductive management in captive giant pandas. Furthermore, the prediction method provided by this invention enriches the methods for distinguishing between true and false pregnancies in giant pandas by examining pregnancy metabolites. Because it uses the ELISA method, it eliminates the need for professional training in ultrasound diagnosis and the need to acclimatize mated giant pandas, saving significant manpower and resources while simplifying the operational procedures.

The invention discloses a refined health care management and control method and management and control platform for pregnancy preparation people, and relates to the technical field of health care, and the method comprises the steps: building a full-period pregnancy preparation health care system, and obtaining a plurality of health treatment and nursing nodes; constructing a pregnancy preparation health nursing patient education knowledge base, and obtaining a patient education nursing node partition knowledge base; obtaining a target health care risk parameter; and obtaining health care consultation knowledge, and performing refined health care management and control on the target user. The problems that existing pregnancy preparation health care lacks refined and personalized adjustment and risk assessment for individual health conditions, living habits and specific requirements, accurate, objective and comprehensive health care knowledge is difficult to obtain, consequently, tension and anxiety of a patient cannot be effectively relieved, the overall medical experience is poor, and the patient experience is poor are solved. The technical problems are solved, and the technical effects of improving the overall working efficiency, the medical experience and satisfaction of the patient and the treatment effect are achieved.

The invention relates to the technical field of medical health data intelligent processing, in particular to a gestational diabetes patient family doctor management method and system based on artificial intelligence, and the method comprises the steps: 1, collecting patient data through a dynamic glucometer, a fetal heart monitor and a hospital information system; 2, aligning the multi-source data according to timestamps to construct a time sequence data set; 3, generating a complication risk score containing a ketoacidosis risk and a giant child risk; 4, generating a personalized sugar control scheme based on the risk score and a pre-constructed medical knowledge graph; 5, integrating opinions of multiple parties to generate a final edition scheme; and step 6, updating deep learning model parameters based on feedback data. According to the mode, the treatment efficiency and accuracy are improved, the control scheme can be adjusted in real time, and the health and safety of the patient in the pregnancy period are ensured. In addition, a feedback mechanism can continuously optimize the system, a closed loop is formed, and the management effect is further improved.

The invention provides a premature delivery prediction method and device, electronic equipment and a readable storage medium, and relates to the technical field of data analysis. The method comprises the following steps: based on a preset model, scoring acquired comprehensive information of a pregnant woman in an early pregnancy stage; wherein the comprehensive information comprises demographic information, clinical information and biochemical index information; and predicting the premature delivery risk of the pregnant woman according to the scoring result. According to the method, the comprehensive information of the pregnant women at the early pregnancy stage is scored through the preset model, the premature delivery risk of the pregnant women is predicted according to the scoring result, quantitative evaluation of premature delivery risk prediction of the pregnant women at the early pregnancy stage is achieved, and objectivity and certain accuracy are achieved; and the difficulty and the cost of obtaining the comprehensive information of the pregnant women in the early pregnancy are relatively low, the difficulty and the cost of predicting the premature delivery risk of the pregnant women in the early pregnancy are reduced, and medical resources are saved.

The invention relates to the technical field of biomedicine, in particular to a gestational period premature delivery risk auxiliary prediction method and system, and the method comprises the steps: obtaining the historical body data and delivery cycle of a pregnant woman, and determining the historical premature delivery degree of the pregnant woman according to the delivery cycle; according to the premature delivery degree difference between different historical pregnant women and the deviation degree change condition difference of the same type of body data during each physical examination, determining the premature delivery correlation degree of different types of body data of the historical pregnant women; clustering the historical pregnant women according to the deviation time difference of the same type of body data among different historical pregnant women, determining the overall correlation of each type of body data according to the number of pregnant women in the cluster and the premature delivery correlation degree of the corresponding type of body data, and screening out the target type of body data; and based on the body data of the target type of the historical pregnant woman during different times of physical examination, constructing a premature delivery prediction model to perform premature delivery prediction. According to the method, the accuracy of premature delivery prediction is effectively improved.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention discloses an establishment method and a verification method of a hydrogen peroxide induced mouse autism model, and relates to the technical field of medical biologication.The establishment method of the hydrogen peroxide induced mouse autism model comprises the following steps that a hydrogen peroxide solution is injected into the abdominal cavity of a pregnant mouse, hydrogen peroxide induction is conducted, and the mouse autism model is obtained. And obtaining a hydrogen peroxide induced offspring mouse, namely the mouse autism model. Hydrogen peroxide is used for near intraperitoneal injection of a female mouse during pregnancy, the oxidative stress state of the female mouse during pregnancy and the oxidative stress state of an offspring mouse embryo are induced through independent hydrogen peroxide, and obvious immune response of the female mouse is not caused, so that an ASD mouse model specifically induced by oxidative stress is formed; the obtained ASD offspring mouse has a remarkable ASD phenotype, but does not show obvious dyskinetic disorder.

The invention discloses an obstetrical data analysis and management system based on cloud computing processing, and relates to the technical field of obstetrical data analysis and management, a data acquisition module is used for acquiring and recording current detection data of a pregnant woman and a fetus, a calculation and analysis module is used for calculating and outputting a fetus weight estimated value TE, a pregnant woman weight gain value YT and a nutrition adjustment value TQ, and the fetus weight estimated value TE, the pregnant woman weight gain value YT and the nutrition adjustment value TQ are analyzed. The regular spot check evaluation unit is used for regularly carrying out spot check on the pregnant woman subjected to current recent antenatal care, and carrying out updating calculation on a fetus weight growth reference value delta TT and a pregnant woman weight growth reference value delta YL of each week in different pregnancy periods according to age groups; large-scale data sets are efficiently processed by means of the high-performance computing power of the cloud computing platform and a computing formula, the working efficiency of obstetrical data analysis is improved, flexible adjustment is performed according to parameters corresponding to different pregnant woman age groups and pregnant periods, the corresponding parameters have a regular updating mechanism and a formed feedback mechanism, and the accuracy of the pregnant woman data analysis is improved. And the fitness and the accuracy of analysis data are ensured.

According to the big data-based debris flow disaster-pregnancy condition analysis method and system provided by the invention, the debris flow potential description event information set in the current debris flow hidden danger element information is obtained through analysis according to the important attribute of the important description content of each debris flow gully object in the hidden danger point directory data; and carrying out anomaly analysis on the debris flow potential description event information set of the current debris flow hidden danger factor information and the debris flow potential description event information set of the previous group of debris flow hidden danger factor information of the current debris flow hidden danger factor information, and determining a debris flow disaster-pregnant condition analysis set of the current debris flow hidden danger factor information. Therefore, the debris flow disaster-gestational condition analysis set of the current debris flow hidden danger element information can be accurately positioned, so that the debris flow disaster-gestational condition analysis set can be subsequently utilized to perform efficient analysis, each hidden danger can be accurately prevented and treated, and the possibility of debris flow occurrence is reduced.

The present invention relates to the field of biomedical technology, and more specifically to a method and system for assisting in predicting the risk of premature birth during pregnancy. The method comprises obtaining the physical data and delivery cycles of historical pregnant women, determining the degree of premature birth of the historical pregnant women based on the delivery cycles, and determining the degree of premature birth correlation of different types of physical data of the historical pregnant women based on the differences in the degree of premature birth between different historical pregnant women and the differences in the deviation changes of the same type of physical data during each physical examination. The historical pregnant women are clustered based on the deviation time differences of the same type of physical data between different historical pregnant women, and the overall correlation of each type of physical data is determined based on the number of pregnant women in the cluster and the degree of premature birth correlation of the corresponding type of physical data, thereby screening out the physical data of the target type. A premature birth prediction model is constructed based on the physical data of the target type during different physical examinations of the historical pregnant women to perform premature birth prediction. The present invention effectively improves the accuracy of premature birth prediction.

Disclosed herein includes a method of detecting RhD incompatibility during pregnancy, comprising: a) extracting DNA from a blood sample of a pregnant mother; b) performing qPCR on the DNA to determine Ct values of at least one RhD exon and at least one internal control gene; and c) calculating a ΔCt value between the at least one RhD exon and at least one internal control gene.

The invention relates to the technical field of data feedback control, and discloses a pregnancy health care control system based on data feedback analysis, which comprises a data acquisition module, a dynamic physiological feature mapping module, an implicit feedback channel module, a self-stabilization control module and a care execution module. By constructing the dynamic physiological feature mapping matrix, the multi-dimensional physiological data and the historical trend of the pregnant woman are deeply fused, the continuous health state vector is generated, and the limitation that in the prior art, a single threshold value is relied on, and dynamic adaptive capacity is lacked is broken through. Meanwhile, an implicit feedback and self-stabilization control mechanism is introduced into the system, the nursing strategy can be automatically adjusted when data abnormity or environment change is detected, pregnancy health management with individualized, continuous and robust coexistence is achieved, and the sensitivity, the stability and the individualized nursing capacity of the monitoring system are remarkably improved.

The invention discloses a pregnancy-related monitoring system based on an electrochemical sensing technology, and the system comprises a wearable device which is used for obtaining hormone detection data of a target monitoring user; the server is connected to the wearable device and comprises a parameter analysis module and a scheme generation module, and the parameter analysis module is used for obtaining the hormone detection data and obtaining ovulation cycle parameters of the target monitoring user through analysis according to the hormone detection data; and the scheme generation module is used for generating a pregnancy-related guidance scheme corresponding to the target monitoring user according to a pregnancy-related target corresponding to the target monitoring user and the ovulation cycle parameter. Therefore, more intelligent and efficient pregnancy-related monitoring and guidance services can be realized, the monitoring accuracy of pregnancy-related indexes is improved, and better use experience is provided for users.

The present disclosure relates to a vaginal system that prevents pregnancy comprised of segesterone acetate and ethinyl estradiol and is configured for thirteen 28-day product-use cycles.

An adjustable maternity wear jacket that accommodates the changing size and shape of an individual's body during the different stages of pregnancy and postpartum. The jacket employs a main jacket portion that covers the upper body, equipped with multiple zippers. Additionally, the jacket contains a removable 3-inch front fabric panel and 6-inch side fabric panels. Through this combination of zippers and fabric panels on the front and sides, the jacket can expand or contract depending on the user's preference. This jacket has various embodiments as well such as that of a polyester puffer jacket, an insulated puffer jacket, or a waterproof shell jacket.

The invention discloses an application of alpha-ketoglutaric acid (alpha-KG) in preparation of a feed additive for relieving skeletal muscle development injury of pregnancy heat stress induced offspring calves. According to the additive, sodium alpha-ketoglutarate (C5H4O5Na2) accounting for 0.1% of dry matter is uniformly mixed into dry dairy cow total mixed ration (TMR), and the dairy cow is fed with heat stress (THIgt; 72) pregnant Holstein cows, the weight of fetal cattle is increased by 23.2%, and the weight of skeletal muscles is increased by 31.3%. Meanwhile, during pregnancy, alpha-KG is fed, so that the weight of the offspring weaned calves is increased by 10.5%, the weight of skeletal muscles is increased by 28.6%, and the fiber density of the skeletal muscles is improved. According to the method, the skeletal muscle growth and fiber density of the offspring calves are subjected to targeted cross-generation regulation and control through maternal nutrition intervention for the first time, the regulation and control bottleneck of heat stress cross-generation injury in the prior art is broken through, and a solution is provided for improving animal health and growth potential for ruminant heat stress management.

The invention provides a prediction system and method for gestational hypertension risk, and belongs to the technical field of gynaecology and obstetrics. According to the system, a data list generation module is used for generating a pregnant woman personal data list; the scoring module is configured to be used for processing and analyzing a pregnant woman personal data list, matching and contrasting each item of data in the pregnant woman personal data list with items in a preset judgment list, identifying matched data, scoring according to a corresponding preset rating standard to obtain main element scores, and sending the main element scores to the pregnant woman personal data list; summarizing the main element scores corresponding to the data to obtain a total score result; and the judgment module is configured to compare the total scoring result with a preset threshold value and judge the risk level of the hypertension risk in the gestation period of the pregnant woman. According to the method, personalized comprehensive risk assessment can be provided, the limitation of a traditional method is overcome, a doctor can formulate corresponding prevention and intervention measures according to specific conditions, and therefore the intervention effectiveness is improved.

Provided herein is a method of using digital twins for medical diagnosis, prediction, and / or prognosis of a disease or condition, such as diagnosis, prediction, and / or prognosis of one or more cancers, organ-specific age prediction, predicting of female aging, predicting hospital-acquired infection, predicting dialysis time series, predicting diabetes and complications, predicting pregnancy and child outcome, predicting heart failure, predicting myopia, predicting vision in anti-VEGF treatment, or any combination thereof. A system and non-transitory computer-readable storage medium configured for performing the method is also disclosed.

The invention provides a model construction method and system for predicting the sustainable pregnancy of an embryo, electronic equipment and a storage medium, the prediction method integrates morphological information, development information and genetic information of the embryo, a model for predicting the sustainable pregnancy of the embryo is constructed based on a random forest algorithm and an XGBoost algorithm weighted average fusion method, and the model is used for predicting the sustainable pregnancy of the embryo. According to the method, embryos with higher sustainable pregnancy can be screened, and the embryo which is most likely to be successful is selected for transplantation, so that the success probability of pregnancy is increased.

The invention provides a pregnant woman gestation period disease risk prediction method based on a time sequence knowledge graph, and belongs to the technical field of medical care informatics, and the method specifically comprises the steps: obtaining the behavior data and disease pathology information data of a pregnant woman in a gestation period; based on a historical time sequence event evolution process, carrying out sub-graph event sampling on the behavior data and the disease pathology information data, and constructing a time sequence knowledge graph; obtaining graph structure features of the time sequence knowledge graph based on a graph attention mechanism, and extracting text semantic features through a text embedding model; fusing the graph structure features and the text semantic features based on a cross attention mechanism; building an entity prompt, a relation prompt and a time prompt based on the prompt project, and training the input of a pre-training language model through the fusion features; and inputting the behavior data and the disease pathology information data of the target pregnant woman in the gestation period into the prediction model to obtain a gestation period disease risk prediction result. Through the scheme of the invention, the prediction accuracy and adaptability are improved.

A data analysis method for gynaecology and obstetrics based on a knowledge graph particularly relates to the technical field of data analysis for gynaecology and obstetrics, and comprises the following steps: acquiring basic information of a current puerpera in real time by using a data acquisition module, and acquiring reference information of other puerpera in the same pregnancy period as the current puerpera and reference graph information from a knowledge graph database; based on basic information, reference information and atlas information, an analysis and diagnosis module is utilized to sequentially calculate and output a puerpera risk index FF, a complication prediction value YB and a health assessment index FJ, and based on result comparison of the health assessment index FJ and a health assessment average index FJavg, the analysis and diagnosis module is utilized to analyze and judge data. The method has the remarkable advantages of improving evaluation accuracy, achieving personalized nursing, timely finding potential risks, promoting circulation influence and optimization and the like, accurate evaluation and targeted adjustment of the health condition of the puerpera are achieved, and powerful support is provided for clinical decision making.

The invention relates to a dog early pregnancy RLN detection kit and a detection method thereof. The kit comprises a magnetic bead working solution, an enzyme working solution, a cleaning solution, a chemiluminescent substrate, a calibration product and a quality control product. Wherein the magnetic bead working solution contains toluenesulfonyl magnetic beads labeled by canine RLN monoclonal antibodies, and the enzyme working solution contains canine RLN monoclonal antibodies labeled by alkaline phosphatase and an antibody blocking agent. When the canine early pregnancy RLN detection kit provided by the invention is used for determining the canine RLN concentration in serum, the RLN antibody on the magnetic bead and the RLN antibody on the enzyme marker are simultaneously and rapidly combined with different sites on the RLN antigen to form an antibody-antigen-antibody structure, the combination is more sufficient, the detection sensitivity is high, the antibody blocking agent shortens the window period of detection, and the detection time is shortened. The false positive of the detection result is reduced, and the detection accuracy is improved.

The invention relates to the technical field of image classification, in particular to a gestational hypertension traditional Chinese medicine tongue diagnosis image classification method based on transfer learning. Firstly, a tongue diagnosis image of a gestational hypertensive patient is obtained, a tongue body area is extracted, and an indentation degree value is calculated based on shape and pixel position features to reflect the shape of fat tongue with indentation; further extracting a tongue middle area by considering that physiological water retention during pregnancy may cause tooth marks, determining a reflective connected domain by analyzing pixel reflective characteristics before and after filtering, and extracting shape characteristics of the reflective connected domain to obtain a dispersion degree value and characterize a greasy moss degree; the tongue diagnosis image purity is obtained by combining the color fluctuation and gray scale confusion conditions of the tongue middle area and integrating the indentation degree value and the dispersion degree value, so that the greasy coating state and the spleen deficiency and liver-yang hyperactivity conditions are reflected; and finally, training a neural network by using the purity of the tongue diagnosis image and the original visual features, classifying the tongue diagnosis image, obtaining a more accurate classification result, and assisting a doctor in performing accurate judgment.

The invention provides an optimal administration method for promoting cervical maturation by misoprostol based on a PopPK model, and belongs to the technical field of drug administration scheme optimization, the method comprises the following steps: constructing the PopPK model for oral administration of misoprostol by a pregnant woman in late pregnancy, the PopPK model is a model structure containing absorption lag time zero-order-first-order absorption parallel, one-atrioventricular distribution and first-order elimination, and the PopPK model is a model structure containing absorption lag time zero-order-first-order absorption parallel, one-atrioventricular distribution and first-order elimination; the PopPK model comprises two absorption chambers and one central chamber; and based on the PopPK model, determining a staged administration scheme through simulation optimization. The limitation of a traditional fixing scheme is effectively overcome, and the safety, effectiveness and clinical practicability of drug administration are considered.

Provided herein are methods and kits for identifying a pregnancy-associated risk or condition in a subject. In embodiments, the methods include detecting levels of at least one biomarker in a substantially cell-free cervical fluid sample obtained from the subject. In embodiments, the methods include detecting levels of biomarkers in a cervical fluid sample including extravillous trophoblasts (EVT) obtained from the subject.

The invention relates to the technical field of medical health information, in particular to a pregnant and lying-in woman health record digital management system which comprises a user management and data safety unit, a record management unit, a monitoring and evaluation unit, a data analysis unit, a reservation registration unit and a health education unit. According to the invention, data access dynamic management and control and privacy protection are realized through a pregnancy stage adaptive permission system and a lightweight block chain technology in combination with zero-knowledge proof, and physiological parameter real-time acquisition and analysis and sub-second early warning are realized through medical IoT equipment and edge computing nodes. And a multi-party collaborative risk prediction model is constructed by virtue of federal learning and an interpretable AI technology, and a clinical decision basis is provided, so that full-cycle safety management and control, real-time monitoring analysis and accurate risk prediction of pregnant and lying-in woman health data are realized, and technical support is provided for improving pregnant health management efficiency and quality.

The invention belongs to the technical field of disease medicine preparation, and particularly relates to nanocellulose, a preparation method thereof and application of the nanocellulose to reduction of bile acid level. The nano-crystalline cellulose is nano-crystalline cellulose fiber or nano-crystalline cellulose crystal; wherein the nano-crystalline cellulose fibers are in a slender whisker shape, the average length of the nano-crystalline cellulose fibers is 250-400 nm, and the nano-crystalline cellulose fibers are partially gathered and wound; the nanocellulose crystal is in a short rod crystal shape, the surface of the nanocellulose crystal is connected with a sulfuric acid half ester group, and the length of the nanocellulose crystal is 50-200 nm. The nanocellulose provided by the invention can achieve the purpose of treating intrahepatic cholestasis in the gestation period by improving the TBA level and improving the filial generation live birth rate; the traditional Chinese medicine composition has no obvious influence on liver and kidney functions, blood sugar, blood fat and the like, and is high in safety; in addition, the nanocellulose does not have potential target organ poison or accumulative effect, the biological safety performance is improved, and the nanocellulose can be used for a long time and is suitable for special crowds during pregnancy.

The invention relates to the technical field of intelligent medical treatment, in particular to a pregnancy management system and method based on big data, and the system comprises an abnormal segment recognition module, a deviation feature extraction module, a risk type recognition module, an abnormal trend prediction module and a physiological data management module. According to the method, the abnormal fluctuation of the data sequence is recognized by calculating the variability of the physiological data in the cycle, the method is not limited to isolated single-point measurement any more, the dynamic stability of the index in a period of time is concerned, and then quantitative extraction is conducted on the variation trend of abnormal data segments such as body weight, blood sugar and blood pressure; for example, the change of body weight along with time is fitted into a slope, original data is converted into a structured trend feature, then the dynamic change trend of an individual is compared with a normal evolution benchmark extracted from a large-scale healthy pregnant woman group, and the severity that the current physiological state deviates from a normal track is accurately quantified.

To make an electronic device perform appropriate information processing in response to an action of a user.SOLUTION: An information processing system disclosed herein comprises an input unit for receiving user input, a processing unit for performing specific processing using a sentence generation model for generating a sentence according to an input sentence, and an output unit configured to control an action of an electronic device to output a result of the specific processing. The processing unit determines whether predetermined corresponding trigger conditions regarding pregnancy and child rearing are met or not and, when affirmative, acquires a result of the specific processing using an output of the sentence generation model that uses information obtained from the user input regarding pregnancy and child rearing as the input sentence corresponding to the information regrading pregnancy and child rearing of the user herself.SELECTED DRAWING: Figure 2A

The present application discloses an analysis method, device and storage medium for sequencing data of non-invasive prenatal genetic testing. The method of the present application includes obtaining the original sequencing results of samples with two or more chromosomal aneuploidies from NIPT sequencing data, aligning them with the reference genome, and removing mismatched or duplicate reads; calculating the Z values ​​of chromosomes other than Y and chromosome 19 according to the NIFTY algorithm, and calculating the average of the top five absolute values ​​of the Z values. If it is greater than the threshold, the subject to be tested is judged to be a high-risk population for tumors. The present application is the first to use the average of the top five absolute values ​​of the chromosome Z values ​​of MCA samples in NIPT testing to evaluate the tumor situation during pregnancy. The analysis method of the present application can directly use NIPT sequencing data, which is simple and convenient, and there is no additional cost other than NIPT sequencing, which further expands the use of NIPT in detecting occult maternal cancers during pregnancy.