9 results about "Osteopenia" patented technology

The invention relates to the technical field of biology, in particular to application of a GGPS1 activator, a pharmaceutical composition, a kit and a method. The invention relates to bone metabolism regulation and bone regeneration. It is found that improvement of GGPS1 expression / activity can activate TGF-beta / Smad2 / 3 by down-regulating LTBP1, so that human bone marrow mesenchymal stem cells are converted from adipogenesis to osteogenesis, and bone formation and bone defect repair are promoted. The invention provides a drug containing a GGPS1 activator and a controlled release composition for implantation, which are used for osteoporosis, bone mass reduction, delayed fracture healing and the like. The invention also provides an in-vitro screening and evaluation method and a detection kit, and the indexes such as p-Smad2 / 3, alizarin red, oil red O and the like are used for judgment. The invention realizes upstream heavy balance and has the advantages of bone promotion and fat suppression.

The method of the embodiment of the present application proposes a method and a public platform for dynamically predicting the risk of osteopenia of psoriasis patients. First, high-quality data are screened from the historical medical records, bone density data and biomarker data of the patients, and multi-modal feature extraction is performed. The high-order interaction of the features of each mode is captured through a graph attention network to realize multi-modal feature fusion. Then, a deep attention neural network is constructed to realize the probabilistic prediction of the risk of osteopenia of psoriasis patients. Next, the individual prediction results are visualized and explained by using the model attention weight and SHAP value, thereby enhancing the clinical usability. Subsequently, the network is compressed and optimized through model distillation, pruning and quantization, thereby realizing the efficient deployment of the model on the edge computing device. Finally, the model parameters are continuously optimized in combination with the actual clinical results and the feedback of doctors. The present application effectively improves the accuracy of risk prediction.

The invention belongs to the technical field of osteopenia risk prediction, and discloses a method for predicting osteopenia of a perimenopausal type 2 diabetes patient, which comprises the following steps: S1, collecting blood sugar time sequence data and clinical static data of the patient; s2, preprocessing the data acquired in the step S1; s3, constructing a prediction model; the prediction model comprises an attention noise reduction module, a time sequence feature extraction module, a static feature coding module and a feature fusion and prediction module; and S4, inputting the data preprocessed in the step S2 into the prediction model constructed in the step S3, and predicting to obtain the bone mass reduction probability. The method has the following advantages that early warning is performed, and high-risk patients are identified 6-12 months earlier than a traditional method; operation is convenient and fast, existing CGM equipment of a hospital is compatible, and risk assessment is expected to be completed within 5 minutes; according to the economic benefit, 30% of osteoporotic fracture related medical expenditure is expected to be reduced.

The application discloses a eucommia ulmoides oliver particle for treating osteopenia, which is prepared from the following raw materials in a weight ratio: eucommia ulmoides oliver 25-35 parts, rehmannia 20-30 parts, yam 18-22 parts, rhizoma discontinuorum 20-30 parts, myrrh 18-22 parts, chuanxiong 20-30 parts, panax notoginseng 15-25 parts, safflower 18-22 parts, white atractylodes 20-30 parts, poria cocos 20-30 parts, astragalus 25-35 parts, ginseng 20-30 parts, polygonatum 18-22 parts, dendrobium 15-25 parts, notopterygium 18-22 parts, angelica pubescens 20-30 parts, rhizoma cimicifugae 20-30 parts, siler 18-22 parts, which is capable of tonifying liver and kidney, promoting blood circulation, invigorating spleen and stomach, and dispelling wind and dampness, so as to achieve the purpose of treating osteopenia caused by kidney deficiency and blood stasis, and a preparation method thereof.

The application discloses an osteoporosis diagnosis method and system based on multi-omics joint machine learning, relates to the field of artificial intelligence medicine, and is particularly designed for the needs of pilots. The method obtains multi-omics data of osteopenia and osteoporosis states, screens out a set of differentially expressed biomarkers, and performs gene function enrichment analysis to identify target biomarkers with diagnostic significance. Further, an osteoporosis diagnosis model is constructed based on these biomarkers, and the model is used to generate diagnosis information for target users. This method combines the physiological characteristics of pilots with multi-omics data to achieve personalized and accurate osteoporosis risk prediction and diagnosis, and is suitable for health management of high-intensity professional groups such as pilots.

FIELD: therapy; cardiology; rheumatology.SUBSTANCE: used to predict osteopenic syndrome in postmenopausal women. Vascular stiffness is determined using the augmentation index, the presence of atherosclerotic plaques in the carotid arteries and the body mass index are assessed. The predicted probability of developing osteopenic syndrome (Y) is calculated using a special formula. For a probability value of Y ≥ 0.5 predicts osteopenic syndrome.EFFECT: increasing the accuracy and accessibility of predicting the risk of developing osteopenic syndrome in postmenopausal women at the preclinical stage by taking into account complex markers of vascular changes and metabolic risk factors.1 cl, 3 tbl, 3 ex

According to the method for dynamically predicting the bone mass reduction risk of the psoriasis patient and the public platform, firstly, high-quality data are screened from historical medical records, bone mineral density data and biomarker data of the patient, and multi-modal feature extraction is carried out; high-order interaction of each modal feature is captured through a graph attention network, and multi-modal feature fusion is realized; then, constructing a deep attention neural network, and realizing probabilistic prediction of the bone mass reduction risk of the psoriasis patient; performing visual explanation on an individual prediction result by using the attention weight of the model and the SHAP value, and enhancing clinical availability; then, the network is compressed and optimized through technologies such as model distillation, pruning and quantification, and efficient deployment of the model on edge computing equipment is achieved; finally, continuously optimizing model parameters in combination with actual clinical results and doctor feedback; according to the invention, the accuracy of risk prediction is effectively improved.

The invention relates to a construction method and application of a conditional knockout mouse model for studying the influence of a CLCF1 gene on cortical bone development. GRNA, Cas9 mRNA and a targeting vector are microinjected into a fertilized egg of a C57BL / 6J mouse, the fertilized egg after injection is transplanted to a pseudo-pregnant female mouse, and an F0-generation mouse is produced; the method comprises the following steps: hybridizing a positive F0 generation mouse which is subjected to correct homologous recombination at a target site of the CLCF1 gene with a wild type mouse to obtain a flox heterozygote mouse of the CLCF1 gene; the CLCF1 gene flox heterozygote mice are subjected to mutual mating, and CLCF1 gene flox homozygote mice are obtained; a CLCF1 gene flox homozygote mouse and a Cd68-Cre tool mouse are subjected to mating, a mouse with the genotype of CLCF1flox / flox and carrying Cd68-Cre is obtained, and the mouse is the target CLCF1 conditional gene knockout mouse model. The CLCF1 conditional gene knockout mouse model disclosed by the invention is a powerful tool for researching functions of the CLCF1 gene. Meanwhile, the model also lays a solid foundation for simulating skeletal system diseases and screening prevention and treatment drugs for bone mass reduction, skeletal atrophy or dysplasia, and provides an important experimental platform.

GREM2 compositions and formulations thereof for treating or preventing one or more symptoms of a disease associated with loss of bone mineral density are described. Recombinant GREM2 has been demonstrated to be capable of reducing bone resorption, lowering bone transition markers, and increasing bone strength. Methods of using the GREM2 compositions and formulations are also provided. Preferably, the pharmaceutical formulation containing GREM2 is administered by a parenteral route to prevent or alleviate one or more symptoms of osteoporosis, bone mass reduction, bone fracture and / or bone cancer.