26 results about "Nerve fiber" patented technology

The present application relates to the technical field of medical image processing, in particular to an image intelligent analysis system for corneal nerve fibers of dry eye patients, which comprises an image acquisition module, an image preprocessing module, a nerve fiber segmentation module, a nerve morphological analysis module based on differential geometry, a nerve regeneration monitoring module, a data management module and a clinical auxiliary decision module. The nerve morphological analysis module based on differential geometry is the core innovation, which contains a nerve fiber curvature analysis unit, a multi-scale differential invariant extraction unit and a nerve network topology structure quantification unit. The system introduces the theory of differential geometry, represents the nerve fiber through a parameterized curve and calculates the local curvature characteristics. A Gaussian scale space is constructed to extract multi-scale differential invariants. The graph structure representation of the nerve network is established to analyze the topological characteristics. The morphological characteristics of the corneal nerve fibers can be quantified comprehensively from the local curvature to the global topological structure, and the accurate detection of the slight changes of the nerve fibers is realized.

The application discloses a composition with soothing anti-inflammatory anti-wrinkle firming efficacy and a preparation method thereof, and the composition comprises a TRPV1 inhibitor, an extract of Glycyrrhiza inflata Bat., palmitoyl tripeptide-1 and palmitoyl tetrapeptide-7. The four raw materials are compounded to have a synergistic effect, reduce the release of pro-inflammatory mediators and inhibit the high reactivity of nerve fibers to achieve soothing anti-inflammatory and anti-irritation; promote the generation of self collagen and sodium hyaluronate, reduce the oxidative stress response to achieve the anti-wrinkle firming efficacy and delay skin aging. The composition has comprehensive effects in soothing, anti-inflammatory, anti-irritation, anti-oxidation, anti-wrinkle and firming, reduces the generation of inflammation and delays skin aging, relieves the burning and stinging sensation of the skin at the same time, and meets the requirements of safety and high efficiency pursued by sensitive skin groups.

The application belongs to the field of brain white matter fiber track prediction, and relates to a brain white matter fiber track prediction method and system based on a pre-trained basic model. The method adopts an MAE architecture, and performs self-supervised pre-training on large-scale brain fiber track data through a mask-reconstruction mechanism to learn global topology and local geometric features of neural fibers. In the training stage, a hybrid progressive mask strategy is introduced. In the initial stage of the model, continuous segment masks are mainly used, and gradually transition to random point masks, so as to realize hierarchical learning of structural patterns and robust expression of features. After pre-training is completed, the model is fine-tuned, so that only the input of the two end point coordinates of the fiber can reconstruct and predict the intermediate track, and the fiber path inference based on the end point is realized. In the fine-tuning stage, the encoder structure is kept stable, the decoder is guided by the end point features to generate complete tracks conforming to the structural distribution of the brain white matter fiber, and the technical problems existing in the prior art are solved.

The application discloses a preparation method and application of a nerve conduit with a multistage biomimetic nerve membrane structure and belongs to the technical field of tissue engineering materials. The nerve conduit with the multistage biomimetic nerve membrane structure comprises a biomimetic inner membrane support with a secondary guiding structure and a biomimetic outer membrane support with a double-layer structure. In the nerve conduit with the multistage biomimetic nerve membrane structure, the filler for playing a growth guiding role has a multistage guiding structure, the primary guiding is that cells are directionally migrated along the fiber direction in the directional fiber membrane, are elongated in shape on the fiber and are accelerated in differentiation, and the secondary guiding is that directional microchannels constrain the growth of nerve fibers, promote the maturation of bundling and the alignment of the growth direction. The application solves the problem of insufficient contact guiding efficiency existing in the prior art and overcomes the contradiction between the filling density of guiding fibers, the growth space and the guiding effect.

The invention discloses a brain image VR system developed based on unity and blender. The system comprises a nerve fiber direction sensing coloring module, a multi-brain structure model intelligent splitting and memorizing module, a double-data-set layered precise control module, an intelligent profile generation module, a double-end adaptive intelligent slicing module and a customized VR immersive interaction module. The invention further provides a brain image VR method based on unity and blender development. According to the method, the display and rendering efficiency problem of the brain image OBJ model in Unity is effectively solved; the comfort of model observation in the VR scene is improved; and the adaptation stability of the system to different brain image data is ensured.

The invention discloses a medical clinical image real-time 3D imaging method fused with artificial intelligence, and the method comprises the steps: extracting a basic feature set of original data of a medical clinical image through a real-time 3D image computing power engine, and completing hierarchical feature aggregation through a multi-scale feature aggregation segmentation model to generate a multi-dimensional aggregation feature map, the method comprises the steps of locking focus edge coordinates and drawing an initial contour by means of a focus boundary accurate positioning model, obtaining space correlation data of a focus and a nerve fiber bundle through a nerve fiber bundle three-dimensional tracking algorithm, performing parallel operation, three-dimensional reconstruction and fusion on various types of data by utilizing a real-time 3D image computing power engine, and finally outputting a 3D imaging result through dynamic rendering. And through multi-model collaboration and computing power optimization, full-process intelligentization of feature extraction, focus positioning, structure tracking and imaging output is realized, and the accuracy and real-time performance of imaging are improved.

The present invention is directed inter alia to the treatment of pain. For example, there is provided a chimeric clostridial neurotoxin for use in treating pain by inhibiting the release of a pain mediator from a neuron comprising an Aδ nerve fiber or a C nerve fiber, wherein the chimeric clostridial neurotoxin binds to the neuron comprising the Aδ nerve fiber or the C nerve fiber, respectively, and wherein the chimeric clostridial neurotoxin comprises a botulinum neurotoxin A (BoNT / A) light-chain and translocation domain (HN domain), and a BoNT / B receptor binding domain (HC domain). Also provided are methods, uses, kits, and unit dosage forms.

The present invention is directed inter alia to the treatment of pain. For example, there is provided a chimeric clostridial neurotoxin for use in treating pain by inhibiting release of a pain mediator from a neuron comprising an Aδ nerve fiber or a C nerve fiber, wherein the chimeric clostridial neurotoxin binds to the neuron comprising the Aδ nerve fiber or the C nerve fiber, respectively, and wherein the chimeric clostridial neurotoxin comprises a botulinum neurotoxin A (BoNT / A) light-chain and translocation domain (HN domain), and a BoNT / B receptor binding domain (HC domain). Also provided are methods, uses, kits, and unit dosage forms.

A computer system that computes second tractography results is described. This computer may include: a computation device (such as a processor, a graphics processing unit or GPU, etc.) that executes program instructions; and memory that stores the program instructions. During operation, the computer system receives information specifying tractography results that specify a set of neurological fibers. Then, the computer system computes, using a predetermined (e.g., pretrained) autoencoder neural network, the second tractography results that specify a second set of neurological fibers based at least in part on the tractography results and information associated with a neurological anatomical region. For example, a subset of the set of neurological fibers may be anatomically implausible and the second set of fibers may exclude the subset. Note that the predetermined autoencoder neural network may be trained using an unsupervised-learning technique.

The invention discloses a retina nerve fiber layer defect analysis method based on an eye fundus image, and relates to the technical field of retina nerve fiber layer image recognition and analysis. The technical defects that fiber defects of a retina nerve fiber layer are high in cost, analysis depends on experience, and quantization and accurate classification capacity is lacked are overcome. The method comprises the steps that 1, image data are acquired; the method comprises the following steps: acquiring an eye fundus image of a retina nerve fiber layer through an eye fundus camera, acquiring first scale image data, acquiring second scale image data of which the pixel value is greater than that of the first scale image data, and marking a nerve fiber most concentrated area on the second scale image; 2, generating a dual-scale supervision signal; step 3, data enhancement processing; 4, constructing a multi-task deep learning model; step 5, defect classification; the feature map is converted into a feature vector through a global average pooling layer, and then a classification prediction result is obtained through two full connection layers. The problem of dependence on subjective experience of doctors is effectively solved, and the diagnosis efficiency is improved.

The present application provides an eye state evaluation method and an electronic device. The method comprises: obtaining an optic disc image region from a first fundus image, and generating a plurality of optic cup disc ratio evaluation results based on the optic disc image region by a plurality of first models; obtaining a first evaluation result of the eye based on the optic cup disc ratio evaluation results; performing a plurality of data augmentation operations on the first fundus image to generate a plurality of second fundus images; generating a plurality of optic nerve fiber layer defect evaluation results based on the second fundus images by a plurality of second models; obtaining a second evaluation result of the eye based on the optic nerve fiber layer defect evaluation results; and obtaining an optic nerve evaluation result of the eye based on the first evaluation result and the second evaluation result. Thus, the present application can provide a reference for doctors when evaluating the eye state of patients, thereby helping doctors to give appropriate evaluation results for the eyes of patients.

Various aspects of this disclosure relate to a layered polymer substrate with electrodes and conductive ink embedded in the substrate. In some embodiments, a prosthetic liner may be bonded onto the polymer substrate. The substrate may include an interconnect coupled to the electrodes via the conductive ink. The system may include a controller (e.g., an electrode controller) in communication with the electrodes via the conductive ink. The electrodes may be activated such that they may stimulate nerve fibers in a user's residual limb.

The application discloses a multifunctional underwater sensing fiber bionic nerve hillock cell receptor and a preparation method, and belongs to the field of bionic artificial receptors. The bionic nerve hillock cell receptor comprises a bionic nerve fiber, a first reflecting surface, a bionic supporting cell, a second reflecting surface, a bionic sensory hair cell and a bionic gel cap. The bionic nerve fiber is used for transmitting input light signals and output light signals. The bionic supporting cell is used for supporting the bionic sensory hair cell and the bionic gel cap, and forms a Fabry-Perot cavity together with the first reflecting surface and the second reflecting surface. The bionic sensory hair cell comprises one moving cilium and a plurality of non-moving cilia. The bionic gel cap contains bionic gel material, has viscous resistance and friction, and is used for wrapping the bionic sensory hair cell. The application solves the problems of poor biocompatibility, large volume, poor directivity and single function of the bionic receptor in the prior art.

The invention relates to the technical field of visual impairment simulation, in particular to a visual impairment dynamic evolution simulation system and method. Multi-modal medical image data of a patient is collected and subjected to preprocessing and focus positioning, an individual visual pathway fiber bundle is constructed by adopting a deterministic fiber tracking algorithm based on diffusion tensor imaging data, non-rigid registration is performed on the individual fiber bundle and a standard visual pathway template, a local deformation field is calculated, and the focus positioning accuracy is improved. Based on a local deformation field, analyzing a spatial geometrical relationship between a focus mask and each section of nerve fiber to obtain an individualized three-dimensional visual pathway model and an SDI scoring matrix of each section corresponding to the individualized three-dimensional visual pathway model, and judging a damaged pathway section and a typical visual field defect mode matched with the damaged pathway section in degree; according to the method, individualized visual simulation of a patient is realized, a visual dynamic evolution prediction model is constructed by utilizing diffusion Transform, and dynamic evolution of visual impairment is realized.

Various aspects of this disclosure relate to a layered polymer substrate with electrodes and conductive ink embedded in the substrate. In some embodiments, a prosthetic liner may be bonded onto the polymer substrate. The substrate may include an interconnect coupled to the electrodes via the conductive ink. The system may include a controller (e.g., an electrode controller) in communication with the electrodes via the conductive ink. The electrodes may be activated such that they may stimulate nerve fibers in a user's residual limb.

The application provides a brain nerve fiber bundle segmentation and reconstruction method and device and electronic equipment, relates to the technical field of image processing, and comprises the following steps: obtaining spectrometer imaging data containing brain images and corresponding brain nerve fiber bundle labels; slicing the calculated spectrometer imaging data into two-dimensional data; dividing the two-dimensional data into training data and test data according to a specified proportion; taking the training data as sample training data and the corresponding brain nerve fiber bundle labels as labels; training an initial segmentation network based on a target loss function to obtain a brain nerve fiber bundle segmentation model; inputting the test data into the brain nerve fiber bundle segmentation model to obtain a predicted brain nerve fiber bundle segmentation image; stacking the predicted brain nerve fiber bundle segmentation image to generate a three-dimensional image block; and splicing the three-dimensional image block by using the information of the overlapping area between adjacent three-dimensional image blocks to complete three-dimensional reconstruction of the brain nerve fiber bundle. The application provides an efficient and accurate brain nerve fiber bundle segmentation and three-dimensional reconstruction method.

An electrohydraulic shock wave generator includes a capacitor configured to store electrical energy and a discharge circuit configured to discharge the capacitor through an electrode assembly positioned in an electrically conductive medium to generate shock waves. A diode suppression network is positioned within the discharge circuit and configured to prevent reverse voltage transients and suppress discharge oscillations. The diode suppression network converts underdamped oscillatory discharge into critically damped discharge waveform to produce shock waves having narrow pulse widths. The generator may include low-resistance discharge circuits with conductors having greater cross-sectional areas and total circuit resistance of less than 0.025 ohms. The capacitor may have a capacitance of 25 nanofarads or less and store electrical energy at voltages between 15 kilovolts and 60 kilovolts. The critically-damped discharge waveform produces shock waves having rapid rise times that minimize activation of pain-sensing nerve fibers during therapeutic treatment.

The invention belongs to the technical field of biological medicines, and discloses a composite targeted drug for targeted alleviation of Alzheimer's disease, in a single preparation, the active components comprise 5-10 mg of TAU protein precursor and 5-20 mg of lithium orotate, and a medicinal carrier is supplemented to prepare the composite targeted drug for targeted alleviation of Alzheimer's disease. The medicinal carrier comprises a targeted delivery material TAU protein precursor, and one or more of a filler, a disintegrating agent and an adhesive. According to the medicine, by means of the blood-brain barrier penetrating capacity and targeting of TAU protein precursors and in combination with the neuron metabolism regulation effect of lithium orotate, multi-mechanism synergistic treatment is achieved, targeting and bioavailability are improved, beta-amyloid protein deposition and nerve fiber tangling can be effectively reduced, the neuron function is improved, the treatment effect is excellent, and safety is good.

A nerve fiber screening method and a related apparatus. The nerve fiber screening method comprises: acquiring medical image data of a patient, wherein the medical image data comprises nerve fiber data and nucleus data (S1); on the basis of the nerve fiber data and the nucleus data, determining an association relationship between a nucleus and nerve fibers (S2); and on the basis of a screening target and the association relationship between the nucleus and the nerve fibers, determining at least one target nerve fiber (S3).

The invention provides a uterine manipulator suite. The uterine manipulator suite comprises a uterine manipulator part, a uterine manipulator rod part, a measuring scale part and a nerve fiber drag hook part. The uterus lifting device part is used for lifting the uterus, and an LED lamp is arranged in a uterus cup of the uterus lifting device part and used for illuminating the interior of the uterus; a puncture needle is arranged in the uterus lifting rod part, the puncture needle is used for knotting meat at the position affecting an operation after threading of the threading forceps, and the uterus lifting rod part is used for lifting a knotted thread ring; the nerve fiber drag hook part is used for pulling out nerve fibers; and the measuring scale part is used for measuring the size of the uterus and the size of a to-be-cut part. The uterus lifting device component, the uterus lifting rod component, the measuring scale component and the nerve fiber drag hook component are integrated into the same set, multiple sets of instruments do not need to be prepared independently, the preparation number of the instruments and the occupied operation space are greatly reduced, meanwhile, reuse of the functions of the four kinds of instruments is achieved, and multiple kinds of operation requirements can be met through one set of pieces.

The application discloses a nerve target regulation and triggering device based on temperature closed-loop feedback. The device comprises a temperature acquisition module and an interactive control unit, a data processing unit. The temperature acquisition module acquires the temperature of the stimulation target area in real time at a high sampling rate. The interactive control unit dynamically issues instruction closed loop to the display unit and the chemical control unit based on the first or second high temperature trigger temperature corresponding to different nerve fiber types (C fiber or A delta fiber). The data processing unit generates multi-source collaborative virtual stimulation source parameters based on the pain perception category to be induced. The application ensures the efficiency and targeting of the visual nerve coupling through high-precision temperature closed-loop feedback and customized parameter generation.

The invention relates to the technical field of biomedicine, in particular to a thalamus / interbrain organ and neuron culture method. The invention provides a method for efficiently differentiating thalamic / interbrain organs and neurons by using human embryonic stem cells or induced pluripotent stem cells. It is found that in the early stage of cell differentiation, the c-JUN gene plays an important role in promoting nerve maturation and maintaining tight junction, the growth rate and the size of thalamus / interbrain organs can be remarkably increased by overexpressing c-JUN or adding c-JUN protein in thalamus / interbrain differentiation, the obtained organs have high-abundance neurons and tight junction protein, and the cell differentiation effect is good. And high expression of thalamus / interbrain characteristic signal protein TCF7L2 is realized. The neurons have abundant nerve fibers and adherent growth ability under the 2D culture condition, and the lifetime is long. The invention provides an efficient 2D neuron and 3D organoid differentiation technology for researching related development and pathology of thalamus / interbrain.

The invention relates to the technical field of medical image processing, and provides a fundus photo region texture feature extraction method and system for glaucoma diagnosis. The method comprises the following steps: step 1, acquiring a target fundus photo and preprocessing the target fundus photo to obtain a grayscale image; 2, carrying out multi-scale decomposition based on wavelet transform on the grayscale image to obtain a sub-band set; 3, flame bleeding feature extraction and detection are carried out based on the sub-band set, and a flame bleeding area is obtained; step 4, performing RNFL change feature extraction and detection on the retina nerve fiber layer based on the sub-band set to obtain an RNFL change region; and step 5, mapping the detected flame-like bleeding area and the RNFL change area back to a spatial position corresponding to the target fundus photo according to a scale relation of multi-scale decomposition, generating an abnormal area marking frame, superposing the abnormal area marking frame to the target fundus photo, and outputting the target fundus photo with a mark.

The application provides an image feature extraction method for brain microstructure changes, and relates to the technical field of medical image processing.The application extracts structure gradient related parameters such as gray gradient, nerve fiber direction and direction stability by collecting the tissue structure image of a subject; then, a microstructure gradient anomaly index is constructed to preliminarily screen the abnormal structure units; a local structure topology network is established in the candidate abnormal area, and a microstructure topology complexity index is calculated to evaluate the structure reconstruction degree; finally, threshold determination is used to realize abnormal area recognition and grading.The application realizes quantitative analysis of tissue microstructure abnormalities by fusing gray gradient change, fiber direction deviation and topology structure features, improves the accuracy and stability of abnormal detection, and can provide technical support for early pathological change recognition and auxiliary diagnosis.

The application discloses a brain tumor potential recurrence risk area determination device and a model training method, relates to the fields of medical technology and artificial intelligence. The device comprises a delineation determination unit configured to determine a brain tumor delineation area based on a target medical image of a target object; a nerve fiber reconstruction unit configured to simulate and reconstruct image information of N target nerve fibers of the target object according to the target medical image; and a risk area prediction unit configured to input the target medical image and the image information of the N target nerve fibers into a target model, perform feature extraction on the received information by the target model, and determine a recurrence risk area of the brain tumor of the target object according to the extracted features by using prior knowledge in a model training stage. The application solves the technical problem of low accuracy in determining the potential recurrence risk area of the brain tumor caused by only observing the risk diffusion of the brain tumor by imaging or judging the risk diffusion of the brain tumor by expert experience in the prior art.