34 results about "Brain aging" patented technology

The invention belongs to the technical field of fetal brain age prediction, and relates to an earthworm cerebellar fetal brain age prediction method and system based on multi-modal feature fusion, an MST-Mamba segmentation network is adopted, and local-global aggregators are embedded in each level of an encoder, so that the cooperation of local detail capture and global semantic modeling is realized; meanwhile, a dynamic channel fusion device is deployed at the jump connection part of the encoder and the decoder, so that the problems of fuzzy boundary, missed division, wrong division and the like are avoided; through three parallel branches of a multi-granularity form-texture collaborative perception architecture, two types of explicit features of macroscopic geometry and topological form and implicit features of microscopic texture are synchronously extracted, and comprehensive characterization of the development features of the earthworm cerebellar part is realized; the explicit features are subjected to standardized calibration and then spliced and fused with the implicit features in the channel dimension, the problems that multi-modal feature fusion is insufficient and calibration lacks are solved, finally prediction is conducted through a multi-layer perceptron regression head, and the accuracy and stability of the brain age prediction result are guaranteed from the source.

The invention provides isolated Ackermansiella muciniphila, a composition containing the same and an anti-aging and / or anti-oxidation application of the Ackermansiella muciniphila. The Akkermansia muciniphila has the advantages that the average life and the longest life of the Akkermansia muciniphila in a nematode model can be prolonged, the movement activity, the swallowing function and the reproductive capacity of the Akkermansia muciniphila can be improved, the expression of insulin / IGF-1 pathway genes (daf-16, daf-2 and age-1), the expression of antioxidant genes (gst-4 and sod-3) and the expression of longevity related genes (sir-2.1) of the Akkermansia muciniphila can be up-regulated, and the antioxidant capacity and the heat stress tolerance of the Akkermansia muciniphila can be enhanced. It is shown that the Akkermansia muciniphila provided by the invention can be used for preparing or developing the anti-oxidation and anti-aging products, and the anti-oxidation and anti-aging products can be prepared from the Akkermansia muciniphila. The invention provides an efficient and safe solution for anti-aging and / or anti-oxidation.

The invention discloses a brain age prediction method, system and device and a medium, and relates to the technical field of biomedical image analys.The method comprises the steps that firstly, precise local detail features representing the cerebral cortex and brain tissue are precisely captured, then the dependency relationship between local areas of different images is obtained based on a windowed multi-head self-attention mechanism, and the brain age prediction result is obtained; a channel attention mechanism is introduced, importance weights of different feature channels are learned, and the importance weights are applied to feature fusion, so that long-distance dependency relationships and fine structure features are accurately captured; and then based on a cross attention mechanism, guiding global detail features to focus on a region with rich local features and guiding the local detail features to focus on a most relevant local region so as to mine deeper detail features, and finally dynamically adjusting the contribution proportion of the local detail features and global context detail features in a final decision. And the features are fused into final features so as to perceive local detail features and global detail features in a deeper level.

A system for estimating a subject's functional brain age using non-invasive electroencephalography (EEG), the system includes: an EEG acquisition module (101) configured to record multichannel EEG signals from the subject; a stimulation control module (102) configured to present a predefined sequence of cognitive and sensory tasks that specifically target the frontal, temporal, parietal and occipital brain regions, and to generate synchronized event markers; a data processing module (103) configured to prepare the recorded EEG signals by filtering, artifact removal, normalization and segmentation into task-oriented time windows; a machine learning module (104) comprising one or more self-monitoring contrastive learning encoders configured to transform the preprocessed EEG signals into latent feature representations; and a brain age estimation module (105) configured to process the latent feature representations to generate a BrainAge Score indicating a difference between a predicted biological brain age and the subject's chronological age.

The invention discloses a multi-parameter MRI brain age prediction method and device based on uncertainty perception, equipment and a medium, and the method comprises the steps: determining a first feature sequence of an sMRI image, a second feature sequence of a DTI image, and a third feature sequence of an fMRI image through a feature extraction module; and determining a GM sampling vector, a DTI sampling vector and an fMRI sampling vector based on the first feature sequence, the second feature sequence and the third feature sequence by using a probability distribution encoder, and performing brain age prediction by using the GM sampling vector, the DTI sampling vector and the fMRI sampling vector. According to the method and the device, the fusion feature vector determined by fusing the sMRI, DTI and fMRI information is utilized to perform brain age prediction, so that multi-dimensional information of the brain structure and function can be more comprehensively acquired, and the accuracy of brain age prediction is improved. Meanwhile, a probability distribution encoder is introduced in the process of fusing the multi-parameter MRI images, multi-parameter MRI feature mapping is converted into Gaussian distribution through the probability distribution encoder to model feature uncertainty, richer potential information in the multi-parameter MRI images is captured, and the accuracy of brain age prediction is further improved.

The invention discloses a brain age prediction method based on spatial heterogeneity and time continuity modeling. The method comprises the following steps: 1, acquiring brain magnetic resonance imaging data and a real age label thereof; 2, the brain magnetic resonance imaging data passes through a brain age prediction backbone network to obtain whole brain feature representation and an age prediction result of a brain region unit; 3, inputting the whole-brain feature representation, the age prediction result and the real age tag into a comparative learning module of regional perception to obtain comparative learning loss; 4, training the brain age prediction backbone network according to the contrast learning loss, and 5, initializing the brain age prediction backbone network into a teacher model and a student model in a model reasoning stage, and obtaining more accurate brain age through the processing of the teacher model and the student model. According to the method, spatial heterogeneity and time continuity in the brain aging process can be modeled, and the accuracy of brain age prediction is improved.

The invention discloses a plateau population brain aging comprehensive index determination method and device, a medium and a computer program product, which adopt a 3D-CNN architecture to process nerve image data, improve brain age prediction precision, apply a longitudinal comparative analysis method, realize individualized brain aging trajectory tracking, generate a saliency map, and improve brain age prediction accuracy. And visual abnormal brain region positioning information is provided. And a multi-modal weighted fusion algorithm is adopted, so that data isomerism challenges are overcome, and information complementation and result verification are realized. Brain oxygen saturation parameters are incorporated into a brain aging evaluation system, and the key problem that hypoxia affects brain aging in a plateau environment is solved. A plateau population brain aging norm is established, the problem that plain standards are not applicable is solved, and a plateau brain aging specific mode is disclosed. The integrated evaluation process simplifies the operation steps, reduces the professional threshold, improves the prevention and treatment effect through personalized intervention suggestions, and is beneficial to delaying the brain aging process.

The present invention addresses the problem of providing an agent for preventing or treating amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD), spinocerebellar ataxia (SCA), aging-related degenerative or neurological disease, brain aging, or diseases associated with brain aging, as well as a more stable antibody that exhibits an effect of preventing or treating these diseases, Alzheimer's disease (AD), or frontotemporal lobar degeneration (FTLD). A human monoclonal antibody that specifically binds to human HMGB1, wherein the human monoclonal antibody (anti-human HMGB1 antibody) comprises a heavy chain CDR1, heavy chain CDR2, and heavy chain CDR3 each consisting of a specific amino acid sequence and a light chain CDR1, light chain CDR2, and light chain CDR3 each consisting of a specific amino acid sequence, is used as an agent for preventing or treating ALS, PD, HD, SCA, aging-related degenerative or neurological disease, brain aging, or diseases associated with brain aging. An antibody in which the light chain complementarity determining region (CDR) 3 of the anti-human HMGB1 antibody has been modified is used.

This application belongs to the field of fetal brain age prediction technology, and relates to a method and system for predicting fetal brain age of the cerebellar vermis based on multimodal feature fusion. It employs the MST-Mamba segmentation network, and achieves synergy between local detail capture and global semantic modeling by embedding local-global aggregators at each level of the encoder. Simultaneously, a dynamic channel fusion unit is deployed at the jump connection between the encoder and decoder to avoid problems such as boundary ambiguity, missed classification, and misclassification. Through three parallel branches of a multi-granularity morphology-texture collaborative perception architecture, it simultaneously extracts two types of explicit features (macro-geometric and topological morphology) and two types of implicit features (micro-texture), achieving a comprehensive representation of the developmental features of the cerebellar vermis. After standardizing and calibrating the explicit features, they are spliced ​​and fused with the implicit features along the channel dimension to solve the problems of insufficient multimodal feature fusion and lack of calibration. Finally, prediction is performed using a multilayer perceptron regression head, ensuring the accuracy and stability of the brain age prediction results from the source.

The invention discloses application of an A2aR-GZMK inhibitor in preparation of an anti-aging product, and belongs to the technical field of biological medicines. The invention provides a treatment scheme for delaying brain senescence by targeting GZMK and adenosine-A2aR signal channels so as to improve body senescence for the first time. According to the present invention, the activity of GZMK is specifically inhibited by using a GZMK inhibitor (such as PPACK), and / or the immunosuppression signal of adenosine is blocked by using an adenosine A2a receptor inhibitor (such as Istradex), such that the accumulation and the activation of Gzmk + CD8 T cells in the brain can be effectively reduced, and the senescence-related neuroinflammation can be reduced so as to improve the neuronal function and delay the brain senescence process. And no matter whether the activity of the GZMK is directly inhibited or the expression of the GZMK is indirectly regulated and controlled by targeting an upstream A2a receptor, a solid biological basis and a clinical safety basis are provided.

The application discloses an AD characteristic information analysis method based on brain medical images, which comprises the following steps: data preprocessing is performed on brain MRI images to be analyzed to obtain standard images of brain regions; spatial characteristic analysis of the whole brain and key brain regions; whole brain image characteristic analysis; cognitive characteristic analysis of the whole brain and key brain regions; and aggregation analysis results are obtained to form corresponding explanation data. The application further discloses an AD characteristic information analysis system based on brain medical images. The method for analyzing cognitive index characteristics such as brain age, logical memory score, visual memory score and long-time delay memory score from brain MRI images by using deep learning technology can provide analysis results of brain MRI images and other important medical indexes for doctors, and can provide effective arguments and explanations for subsequent scientific research analysis and interpretation.

The invention belongs to the technical field of functional nutritional composition research, and relates to a nutritional composition and application thereof, in particular to a nutritional composition and non-therapeutic application thereof in retarding brain aging injury. The nutritional composition comprises lactoferrin and 3 '-sialyllactose as essential active ingredients. Experiments find that in an in-vitro model, combined treatment of lactoferrin and 3 '-SL can relieve oxidative stress aging damage of Neuro-2a nerve cells induced by H2O2, and the oxidative stress aging damage is mainly manifested as follows: the content of an oxidative stress marker MDA is decreased, the SOD activity is increased, brain aging-related secretory phenotype proinflammatory factors IL-6 and TNF-alpha are decreased, and an anti-inflammatory factor IL-10 is increased. Oxidative stress is considered to be one of the important mechanisms of brain aging and neurodegenerative diseases. The invention finds that the nutritional combination of lactoferrin and 3 '-SL slows down brain aging injury in the aspects of regulating oxidative stress and inhibiting neuroinflammation.

Calculating a brain age predicts progression from cognitively normal status to cognitive impairment. Software acquires diffusion magnetic resonance images (dMRI) and T1 weighted magnetic resonance images (T1w MRI) of the brain and calculate a pair of diffusion tensor imaging (DTI) scalar maps. The scalar maps may be a fractional anisotropy (FA) scalar map and / or a mean diffusivity (MD) scalar map. Concatenating the FA scalar map and the MD scalar map form a frame of scalar map data. The method includes a rigid transformation from scalar map data to the T1w images to store a rigid transformed image; applying an affine transformation from the rigid transformed image to a brain template; storing a rigid plus affine transformed image; and applying a non-rigid transformation to warp the rigid plus affine transformed image to the selected brain template to form a non-rigid white matter age input to a brain age calculation model.

1. Name of the product in this design: Brain Age Meter. 2. Intended use of this design: for medical instruments. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key features: the front view.

The application discloses a kind of multi-modal medical image prediction method, equipment, medium and product based on graph neural network, belong to medical image processing technical field.The application is first to each modality MRI image data preprocessing, obtain the graph data of each modality, then based on the feature extraction and fusion module of multi-modal brain age prediction model extracts the feature of the graph data of each modality, again fusion obtains multi-modal fusion feature vector, again the multi-modal fusion feature vector is input to the prediction module of multi-modal brain age prediction model, to obtain brain age prediction result, the loss function when training prediction model includes prediction loss and the reconstruction loss of each modality graph data, i.e. The application fully utilizes the MRI image information of multiple modalities of the subject, learns nonlinear features based on the GNN autoencoder model, and effectively improves the model performance.

The application discloses application of barbatusoside B in resisting liver and brain aging, and belongs to the field of medicinal chemistry. Experimental data of the application show that the barbatusoside B can significantly reduce tissue damage and inflammatory response of the aging liver, improve liver function, and prolong healthy life, and provides a new treatment strategy for resisting liver aging and related diseases; meanwhile, the barbatusoside B can also significantly improve oxidative damage and neural inflammation of the aging brain, protect neuron function, and enhance cognitive ability, and provides a new treatment strategy for resisting brain aging and related neurodegenerative diseases.

The invention discloses application of C1INH protein in preparation of drugs for preventing and treating brain aging, Alzheimer's disease and vascular dementia, and relates to the field of biomedicine. In particular relates to a C1INH protein or a coding gene SERPING1 of the C1INH protein as a plasma biomarker and a therapeutic target for distinguishing the Alzheimer's disease from physiological brain aging, and application of the C1INH protein or the coding gene SERPING1 in preparation of products for evaluating brain aging and / or related states of the Alzheimer's disease and / or application of the C1INH protein or the coding gene SERPING1 in preparation of drugs for preventing and treating the brain aging, the Alzheimer's disease and vascular dementia. The C1INH protein has an amino acid sequence as shown in SEQ ID NO: 1, and the SERPING1 gene has a nucleic acid sequence as shown in SEQ ID NO: 2.

The invention discloses a modal decoupling brain age prediction system based on graph reinforcement learning, and relates to the technical field of medical image processing and artificial intelligence crossing. The method aims at solving the problem that an existing brain age prediction method is low in prediction accuracy. The method comprises the following steps: obtaining a multi-source data set, and preprocessing the multi-source data set to obtain a preprocessed multi-source data set; constructing fMRI graph structure data and sMRI graph structure data by using the preprocessed multi-source data set; training and verifying the brain age prediction model by using a training set and a verification set composed of the fMRI graph structure data and the sMRI graph structure data to obtain a final brain age prediction model; obtaining an fMRI image and an sMRI image of a patient to be detected, obtaining fMRI image structure data to be detected by using the fMRI image of the patient to be detected, obtaining sMRI image structure data to be detected by using the sMRI image of the patient to be detected, and inputting the fMRI image structure data to be detected and the sMRI image structure data to be detected into the final brain age prediction model to obtain a final brain age prediction result of the patient to be detected. The method is used for brain age prediction.

This invention discloses a graph convolutional neural network (GCN) disease prediction system based on multimodal brain MRI images, belonging to the field of medical image analysis and disease prediction technology. The system includes: an image preprocessing module that acquires and standardizes raw functional and structural magnetic resonance imaging (fMRI) data to generate a corresponding dataset; a dynamic supernet construction module that extracts spatiotemporal fusion variability features of brain regions; a multimodal fusion module that fuses features and constructs an adjacency matrix; a GCN prediction module that calculates prediction results such as disease progression grading; and a results visualization module that locates abnormal brain regions and generates a clinical reference report. This invention integrates multimodal brain MRI data from functional and structural magnetic resonance imaging to build a full-process disease prediction system. Through various algorithms, it mines the spatiotemporal dynamic characteristics of brain regions and integrates multimodal features to achieve dual-task prediction of Alzheimer's disease progression grading and brain age. Furthermore, through standardization processing and feature screening, it generates clinical reports to assist in disease diagnosis and treatment.

This invention discloses a brain age prediction system, device, and storage medium. The system includes a processor configured to execute a brain age prediction method comprising: extracting brain region volume features of at least two target brain regions from a subject's structural magnetic resonance imaging (MRI) image to obtain a target brain region volume feature set; and inputting the target brain region volume feature set into a trained width learning system model to obtain a brain age prediction result for the subject. This invention solves the problem of low accuracy in brain age prediction results in existing technologies.

The application discloses a kind of based on fine-grained brain age interpretation method and interpretation device, belong to neural image analysis field, method includes the pre-segmentation of neural image and the calculation of fine-grained brain age depth model, wherein the calculation method of fine-grained brain age depth model includes local area feature extraction step, global feature extraction step and information fusion step;By neural image pre-segmentation can output the regional segmentation result sequence of neural image, then input each fine-grained local feature sequence of fine-grained depth brain age model in local area feature extraction, then carry out global feature extraction to obtain the global feature sequence that the context information between features is fused, finally local and global feature are fused, obtain the fine-grained brain age sequence corresponding to each region of brain;Due to the sequence generation of multi-scale information fusion and the brain age of all regions once output, this scheme has the advantages of high interpretability and light calculation, can meet the fine-grained interpretation needs of clinical and medical research.

The invention belongs to the technical field of brain age estimation, and particularly relates to a brain age estimation method and system based on multilayer weighted modeling. The method comprises the following steps: acquiring three-dimensional brain nuclear magnetic resonance image data, and preprocessing the three-dimensional brain nuclear magnetic resonance image data; based on the preprocessed three-dimensional brain nuclear magnetic resonance image data, core senescence related feature screening is carried out; establishing a plurality of nuclei and brain region mixed function models based on the screened core senescence-related characteristics, and fitting a mapping relation between the core senescence-related characteristics and physiological brain ages; and integrating the mixed function models of the nuclei and the brain region, and generating a global brain age prediction model through deep learning iterative optimization and dynamic adjustment of weight parameters so as to predict the brain age.

This product provides an agent that inhibits the decrease of MITOL or promotes the production of MITOL, and addresses symptoms of cardiac aging such as decreased cardiac function, graying hair, and hair loss. Medical treatments that can prevent or improve skin aging symptoms such as hair loss, and brain aging symptoms such as Alzheimer's disease. To provide pharmaceuticals, quasi-drugs, cosmetics, or food and beverages. [Solution] From the group consisting of peony extract, arnica extract, and paeoniflorin or its derivatives An agent for inhibiting the reduction of MITOL or promoting its production, comprising at least one selected substance as an active ingredient.

The application belongs to the field of medical image classification, and provides a Tri-UNet brain age prediction method based on MRI different scale features, and proposes a network model Tri-UNet based on 3D ResNet and 3DU-Net for a brain age prediction task, wherein the U-Net multi-layer encoding and decoding structure can combine deep features and shallow features, so as to better learn the context semantic information of input features, and learn fine features while improving the prediction accuracy; the residual structure in the ResNet can convert the end-to-end feature mapping into residual mapping, so as to better learn the feature information of the front and rear layers of the network and improve the prediction accuracy.

This invention provides a method, system, medium, and electronic device for predicting brain age. The method includes the following steps: acquiring brain MRI data, gender, and true age of a healthy human body; reconstructing a quantitative magnetic susceptibility image based on the brain MRI data; constructing a brain age prediction model; training the brain age prediction model based on the quantitative magnetic susceptibility image, the gender, and the true age, and then using the trained brain age prediction model to achieve brain age prediction. The brain age prediction method, system, medium, and electronic device of this invention, based on quantitative magnetic susceptibility imaging, uses a deep learning algorithm to predict brain age, achieving excellent brain age prediction performance and possessing good generalization and clinical translational potential.

The present application relates to the technical field of traditional Chinese medicine. The present application provides a traditional Chinese medicine composition for preventing and / or treating Alzheimer's disease, which comprises red ginseng and prepared radix falcaliae multiflorae. The present application also provides a preparation method of the traditional Chinese medicine composition for preventing and / or treating Alzheimer's disease. The traditional Chinese medicine composition has a protective effect on HT22 cells induced by A beta 1-42, can improve the anti-aging effect of HT22 cells, can significantly improve the learning and memory impairment of dementia model mice, can prolong the lifespan of N2 wild type Caenorhabditis elegans, can significantly inhibit the production and deposition of beta-amyloid protein closely related to Alzheimer's disease, can significantly activate the expression of hippocampus and cortex SIRT1, can significantly inhibit the expression of hippocampus and cortex aging marker genes of mice, can inhibit brain aging, and thus can effectively prevent and / or treat Alzheimer's disease.

A pupillometry system (100) for computer-implemented assessment of brain aging and presymptomatic Alzheimer's risk in cognitively unimpaired individuals through computer-human interaction with a user (5), wherein the pupillary moment-by-moment dynamics of the user on a sensory stimulation decision task with continuous or—at least two—discrete selection options and a subsequent reward / feedback choice is analyzed.

The application discloses a method and device for evaluating brain age based on deep learning intelligent analysis of brain myelin development, equipment, storage medium and products, relates to the technical field of medical image processing, and the method comprises the following steps: acquiring a brain multi-modal magnetic resonance image; preprocessing the brain multi-modal magnetic resonance image to obtain a target brain image; measuring the correlation of the target brain image and the standardized features of each age template in a preset continuous age brain template library; and selecting the age corresponding to the age template with the highest correlation measurement result as the brain age prediction result based on the calculated correlation measurement results. When processing the brain multi-modal magnetic resonance image, the data inconsistency is solved by registration to the continuous age template space, thereby realizing accurate brain age prediction.

The invention discloses a brain age prediction method and device based on multi-parameter MRI, equipment and a medium, and the method comprises the steps: obtaining a craniocerebral multi-parameter MRI image, and determining a plurality of parameter maps based on the multi-parameter MRI image; performing feature extraction on the various parameter maps through the feature extraction module to obtain a target feature map; and performing brain age prediction based on the target feature map through the brain age prediction module to obtain a brain age prediction result corresponding to the multi-parameter MRI image. According to the method, information mining is carried out on the multi-parameter MRI influence through the brain age prediction model, the feature information of the brain structure and function contained in the multi-parameter MRI image can be effectively mined, fine abnormal signals are accurately captured, and the brain age prediction accuracy is improved.