705 results about "Normal tissue" patented technology

The invention discloses a tumor radiotherapy dose calibration method, and relates to the technical field of tumor radiotherapy, and the method comprises the steps: collecting multi-modal data in real time through 4D-CT, MRI and the like, extracting dynamic features through space-time registration, calculating a risk organ dose ratio and a tumor target dose deviation rate, and constructing a comprehensive evaluation function. The method has the advantages that the multi-modal dynamic data of the tumor target area are synchronously collected in real time, the space-time joint registration algorithm and the time sequence weight factor are combined, the limitation that existing static calibration depends on fixed parameters is broken through, and the method is suitable for the multi-modal dynamic data of the tumor target area. According to the method, 95% dose coverage of a tumor target region is guaranteed, and meanwhile, organ-endangering peak value receiving amount is controlled within a certain range of a clinical tolerance threshold value, so that the risk of excessive irradiation of normal tissues is reduced, and the radiotherapy dose calibration precision is prompted based on the deviation rate of a traditional method.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

The invention discloses a skin targeted therapy laser control method and system based on a dual-wavelength synergistic effect, and the method comprises the steps: collecting the real-time morphological characteristics and pigment distribution data of skin tissues through a multispectral imaging unit, and recognizing the optical characteristic parameters of a target treatment region; generating a dual-wavelength combination scheme through a dynamic wavelength selection algorithm based on the optical characteristic parameters and the tissue structure characteristics of the target treatment area; according to a dual-wavelength combination scheme, generating a synergistic laser sequence with a specific pulse interval and energy ratio; the synergistic laser sequence is converted into a driving signal, and a dual-wavelength laser is controlled to output composite laser; and laser output parameters are dynamically adjusted through real-time thermal diffusion monitoring and an optical feedback mechanism. By utilizing the embodiment of the invention, accurate and selective heat dissipation of skin target tissues can be realized, surrounding normal tissues are effectively protected, the treatment safety and effectiveness are improved, and side effects are reduced.

The utility model discloses a tumor electric field treatment device and a treatment system, the tumor electric field treatment device is provided with a frame body and a pole piece module, the frame body forms a skeleton structure of the treatment device, the pole piece module is attached to the frame body, the pole piece module is provided with a plurality of electrode contacts, and each electrode contact can form an active contact or a passive contact. The active contact and the radio frequency generation module can form a loop; after the treatment device is implanted into a tumor excision part, all the electrode contacts form active contacts, and all the electrode contacts and the radio frequency generation module form a loop to distinguish normal tissues from pathological tissues; after the normal tissue and the lesion tissue are distinguished, the electrode contacts in the normal tissue area form passive contacts, and the electrode contacts in the lesion tissue area form active contacts, so that targeted treatment of the lesion tissue is realized, and side effects generated by electric field treatment are reduced.

Disclosed are RAS(ON) multi-selective inhibitor compositions and methods of treating RAS protein-related diseases or disorders using an intermittent dosing regimen. RAS(ON) multi-selective inhibitors having tight binding to cyclophilin A (CypA) result in high exposure levels, prolonged tissue retention, and / or slow clearance rates, thereby increasing the risk of inhibition of wild-type RAS in normal tissues. Accordingly, provided herein are methods for the safe and effective dosing of low KD1 RAS(ON) multi-selective inhibitors using an intermittent dosing regimen. Also provided are methods of selecting or identifying such RAS(ON) multi-selective inhibitors suitable for intermittent administration.

The invention discloses an ultra-high dose rate radiotherapy plan optimization system based on compensator modulation, and relates to the technical field of radiotherapy. A data acquisition module is used for acquiring medical image data of a patient and receiving a clinical target; the clinical target comprises a target region prescription dose, an organ-endangering limit and a minimum dose rate threshold value required for triggering and maintaining a FLASH effect; and the FLASH biological effect evaluation module is used for calculating FLASH biological effective dose distribution corresponding to the physical dose distribution through an embedded biological effect model based on the input physical dose distribution and dose rate distribution. According to the method, the physical dose is converted into the accurate FLASH biological effective dose through the improved linear quadratic model embedded with the FLASH correction factor, the target tumor killing effect is guaranteed, normal tissue protection is enhanced with the help of the tissue differentiation correction factor, the industrial pain point that the physical dose reaches the standard but the biological effect does not reach the expectation is effectively solved, and the application prospect is wide. And the treatment plan better meets the clinical curative effect and safety core requirements.

The invention discloses a method for preparing a methylacryloyl chitosan / gelatin-based hydrogel wound dressing through 3D printing and application, and relates to a method for preparing a hydrogel wound dressing and application. The problems that existing MXene is high in photo-thermal conversion temperature, and bacteria rapidly breed again after near-infrared irradiation is closed are solved. The ZGC composite hydrogel wound dressing integrates multiple functions, can simultaneously realize hemostasis, antibiosis, anti-inflammation, vascularization promotion and cell regeneration, and is especially suitable for infectious wounds. The antibacterial performance can be achieved under the activation of near-infrared light through the dual effects of a photothermal therapy and a photodynamic therapy, and meanwhile, the antibacterial effect can be achieved in cooperation with Zn < 2 + >. The ZGC composite hydrogel wound dressing can be accurately customized and printed through a 3D printing technology according to the shape and depth of a wound, the composite dressing can achieve accurate low-temperature photo-thermal treatment, meanwhile, the continuous release of Zn < 2 + > and the physical antibacterial function of MXene are maintained, normal tissue is protected while bacteria are killed, and the healing rate of an infected wound is remarkably increased.

The invention provides a dual-targeting carrier material, a preparation method thereof and a preparation method of oleanolic acid nanoparticles wrapped by the dual-targeting carrier material, and belongs to the technical field of medicines. The oleanolic acid targeting nanoparticles are prepared by taking a dual-targeting high-molecular copolymer as a carrier material and adopting an emulsification-solvent evaporation method, so that the oleanolic acid can be protected from being quickly degraded, and the in-vivo circulation time of the oleanolic acid can be prolonged. Meanwhile, targeted delivery of oleanolic acid is realized through a targeted group, the concentration of the drug in tumor tissues is improved, and toxic and side effects on normal tissues are reduced. An MTT method is adopted, the inhibition effect of the oleanolic acid nanoparticles on cell growth is researched, and the result shows that the oleanolic acid targeting nanoparticles have a stronger effect of inhibiting tumor cell proliferation compared with an oleanolic acid raw material medicine.

The invention belongs to the technical field of physical thermal ablation, and relates to a physical thermal ablation energy regulation and control method based on intelligent feedback, and the method comprises the steps: collecting point-shaped temperature data, impedance data and ultrasonic data, eliminating the asynchronization and heterogeneity problems through space-time alignment, and combining with focus region segmentation, thereby achieving the energy regulation and control of the physical thermal ablation. Discrete data and continuous images are integrated into a voxelization data cube containing space coordinates, physical attributes and semantic tags, then the temperature change rate and the impedance change rate are calculated based on the voxelization data cube, and then a morphological prediction network is fused with multi-modal features to output a voxel-level ablation probability graph. The method comprises the following steps: realizing prospective prediction of an ablation range, ensuring ablation accuracy by morphological deviation through a three-layer parallel regulation and control strategy, preventing and controlling local overheating by temperature gradient, avoiding tissue carbonization by an impedance change rate, and cooperatively deciding through multi-dimensional indexes to solve the contradiction that single regulation and control cannot give consideration to accurate coverage of a focus and protection of normal tissues; and misjudgment caused by abnormity of single data is avoided.

The invention proposes a voxel-level dose distribution prediction method, and the method comprises the steps: obtaining a detection image of a target object when each tracer is independently introduced under the condition that a plurality of tracers are independently introduced into the target object; performing image standardization processing on the detection image corresponding to each tracer agent to obtain a standardized image corresponding to each tracer agent; and taking the standardized images corresponding to the various tracers as input information of a deep learning model, and determining a comprehensive three-dimensional dose prediction result of the target object under the various tracers through the deep learning model. According to the technical scheme, the accuracy of dose prediction in nuclide therapy is improved, full-link automation from image acquisition to dose planning is realized, the generated dose distribution not only meets the clinical requirement of precise coverage of a target region, but also conforms to the radiotherapy principle of normal tissue protection, and reliable technical support is provided for personalized precise radiotherapy.

The invention relates to the cross technical field of artificial intelligence and medical image processing, in particular to a medical image segmentation system and method based on a wavelet bridge diffusion model and an efficient conditional random field. A WBDM-ECRF framework is constructed and comprises a discrete wavelet transform module, a BDM-T module, a BDM-S module and an ECRF module; decomposing the image through discrete wavelet transform, extracting a low-frequency sub-band, and enhancing the contrast ratio of a focus and normal tissues; the BDM-T takes U-Net as a backbone, integrates a FlashAttention mechanism, and optimizes a variance formula to realize efficient training; the BDM-S adopts a leapfrog sampling strategy, so that the reasoning time is greatly shortened; the ECRF introduces a multivariate potential function of a structural similarity index and smooth operation through edge expansion, and accurately optimizes edge segmentation. According to the method, the dependence of marked data is reduced, the training and reasoning efficiency is improved, the problem of fuzzy edge segmentation is solved, the Dice coefficient and intersection-union ratio performance on the ISIC data set is excellent, and reliable quantitative support is provided for disease diagnosis and treatment.

The invention provides application of lactic acid modified histone H3 in preparation of drugs and / or diagnostic kits for preventing and / or treating pancreatic ductal adenocarcinoma, and belongs to the technical field of disease diagnosis and / or treatment. The invention provides application of a lactic acid modified histone H3 as a target spot in preparation of a pancreatic ductal adenocarcinoma diagnostic kit or a medicine for preventing and / or treating pancreatic ductal adenocarcinoma. The site of lactic acid modification is 23-site lysine. The expression of H3K23la detected in clinical sample tissues in pancreatic ductal adenocarcinoma tumor tissues is obviously higher than that in para-carcinoma normal tissues, overexpression and non-expression of H3K23la in cells are regulated and controlled through in-vitro experiments in combination with overexpression and knock-down technologies, and results show that proliferation, invasion and migration capacities of pancreatic ductal adenocarcinoma cells are remarkably inhibited, so that the pancreatic ductal adenocarcinoma cells are remarkably inhibited. Therefore, the H3K23la can be used as a target spot for diagnosis and treatment of the pancreatic duct adenocarcinoma, and has wide application in diagnosis or treatment of the pancreatic duct adenocarcinoma.

The invention belongs to the technical field of nano-carriers, and particularly relates to a casein-based nano-carrier with slow release and pH / NIR dual response as well as a preparation method and application thereof, casein is combined with hollow copper sulfide nano-particles to load drugs, the nano-carrier with slow release and pH / NIR dual response is generated, and the drug loading rate is as high as 96.84%. Experiments show that the nano-carrier can accurately control drug release under specific pH and near-infrared light irradiation, so that the drug treatment efficiency is improved, and side effects on normal tissues are reduced.

The invention discloses a colorectal cancer prognosis model construction method based on fatty acid metabolism related genes, and belongs to the technical field of biomedicine, and the colorectal cancer prognosis model construction method comprises the following steps: S1, obtaining transcriptome data of a colorectal cancer diagnosis individual from a TCGA database; s2, performing difference analysis on fatty acid metabolism related gene expression data of the colorectal cancer tissue and the normal tissue, and screening out differentially expressed genes; s3, performing single-factor Cox regression analysis on the differentially expressed genes, and screening out genes related to colorectal cancer prognosis; according to the construction method of the colorectal cancer prognosis model based on the fatty acid metabolism related genes, provided by the invention, the tumor immunotherapy reactivity is evaluated by introducing TIDE scores, and the prior art is improved, so that the immune escape potential and the immunotherapy effect of tumor subtypes are analyzed. The prognosis of a colorectal cancer patient is effectively predicted, and a new molecular marker is provided for personalized treatment of the colorectal cancer.

The invention discloses a multi-modal tumor identification method based on a frequency domain attention mechanism. The method comprises the following steps: S1, acquiring multi-frequency bioelectrical impedance signals of tumor tissues and normal tissues and corresponding medical tumor pathological images; s2, extracting a frequency-time feature of the electrical impedance signal and a spatial feature of the pathological image through a multi-modal feature coding module, and generating a multi-modal fusion feature; s3, converting the multi-modal fusion features into frequency domain representation, performing weighted enhancement on the multi-modal fusion features through a frequency domain attention module, and outputting the features; and S4, fusing the features output by the frequency domain attention module and the superficial layer features in the coding stage through jump connection to obtain enhanced features, inputting the enhanced features into a decoding network, and outputting a tumor classification result. According to the method, the bioelectrical impedance signal and the multi-modal information of the pathological image are fused, and a frequency domain attention mechanism is introduced to effectively improve the expression ability of key frequency characteristics.

The invention belongs to the technical field of ultrasonic imaging, and particularly relates to an ultrasonic nonlinear parameter imaging method and system, a medium and a program product. The method comprises the following steps: calculating a fundamental signal and a harmonic signal based on an ultrasonic echo RF signal; extracting the amplitude of the fundamental wave signal and the amplitude of the harmonic wave signal, and calculating to obtain a fundamental wave amplitude ratio and a harmonic wave amplitude ratio; calculating an initial value of a nonlinear coefficient according to a ratio of the fundamental wave amplitude ratio to the harmonic wave amplitude ratio, and estimating a value range of the nonlinear coefficient by using a Newton iteration method; and generating an ultrasonic image based on the value range of the nonlinear coefficient. In the ultrasonic non-linear parameter imaging process, a method for solving a non-linear coefficient by adopting a Newton iteration method is further disclosed, so that the dynamic range of the estimated non-linear coefficient value is expanded, the distinguishing between normal tissues and focus tissues in an obtained ultrasonic image is more obvious, and the sensitivity is higher.

The invention provides an ion beam irradiation configuration optimization method based on a computer storage medium, and aims to improve treatment accuracy and effectiveness. According to the method, image data of a target area are obtained through three-dimensional scanning, three-dimensional discretization processing is carried out, and a voxel angle range matrix is constructed. The preference of each voxel for different ion beam energy is quantified by using a favor value function, and the energy layer of each angle is dynamically calculated and distributed in combination with the optimization of the irradiation angle. The system can monitor feedback data in real time, automatically adjust energy configuration and irradiation angle, and ensure optimal energy coverage of a target area. According to the method, the treatment effect is improved, damage to normal tissue is reduced by accurately controlling energy transmission, and the safety and efficiency of treatment are improved. Through the optimization and dynamic adjustment mechanism, a personalized treatment scheme can be provided for each patient, and efficient and accurate treatment on a complex target region is achieved.

The invention discloses an EGFR (epidermal growth factor receptor) targeted nano-drug carrier and a preparation method thereof, the EGFR targeted nano-drug carrier comprises drug-loading nanoparticles, an erythrocyte membrane coating the drug-loading nanoparticles and GE11 peptide connected to the erythrocyte membrane, and the drug-loading nanoparticles are formed by connecting tannic acid, ellagic acid and 1, 4-phenyldiboronic acid through boric acid ester bonds. The EGFR targeted nano-drug carrier disclosed by the invention can be enriched at an EGFR overexpressed tumor site in a targeted manner, and can be effectively prevented from being cleared by an immune system, and the drug loaded by the drug-loaded nanoparticles is released in a high-active oxygen environment, so that the treatment effect on tumors can be improved, and the toxic and side effects on normal tissues can be reduced; and photothermal therapy and chemotherapy effects can be simultaneously generated under NIR illumination, and the anticancer efficiency can be remarkably improved through the synergistic effect of the photothermal therapy and the chemotherapy.

The invention provides a cyclodextrin-based nano anti-tumor medicine preparation as well as a preparation method and application thereof, and belongs to the technical field of medicines. The preparation method comprises the following steps: preparing a graphene quantum dot deposition gold nanocage modified by a silane coupling agent, carrying out a Schiff base reaction on the graphene quantum dot deposition gold nanocage and aldehyde substituted-beta-cyclodextrin to prepare a composite carrier, and coating a targeted peptide-drug compound through the reaction, thereby preparing the cyclodextrin-based nano anti-tumor drug preparation. According to the cyclodextrin-based nano anti-tumor medicine preparation prepared by the invention, the anti-tumor medicine effect is obviously improved, meanwhile, the medicine resistance of the medicine can be reversed, the damage to normal tissues is reduced, the use amount of the medicine is reduced, the side effect after the medicine is used is reduced, the compliance of a patient is improved, and meanwhile, the stability and biocompatibility of the medicine can be improved; wide application prospects are realized.

The invention provides a tumor medical image classification method of zero sample evolution NAS based on double-index collaborative evaluation, which comprises the following steps: collecting tumor medical image data, and constructing a data set containing a tumor focus image and a normal tissue image; constructing an extended search space based on a cell structure, wherein the search space comprises a basic operation set, the cell structure and a network overall structure; steady-state evolution algorithm parameters are set, and steady-state evolution initialization is carried out to obtain an initial candidate architecture; taking a double-index comprehensive score calculated by the information rate and the FIM stability as a comprehensive index for evaluating the current candidate architecture, updating the candidate architecture through a steady-state evolutionary algorithm based on a dynamic optimization mechanism until the maximum evolutionary algebra is reached, and obtaining an optimal architecture; and training the optimal architecture by using the data set, and carrying out tumor medical image classification by using the trained optimal architecture. According to the method, the dependence on the annotated data is reduced, the consumption of computing resources is reduced, and meanwhile, the diagnosis efficiency and accuracy are improved.

Radiotherapy is the most widely used and effective anti-tumor therapy, however it can damage healthy tissues surrounding the tumor. One approach to reducing damage to healthy tissue involves irradiation at dose rates far exceeding those currently used in clinical contexts reduces radiation-induced toxicities while maintaining an equivalent tumor response. This is known as the FLASH effect. The mechanism responsible for reduced tissue toxicity following FLASH radiotherapy (FLASH-RT) is still undetermined; multiple hypotheses have been suggested by linking the high dose rate to rapid oxygen depletion, immune response, reduction of peroxyl radical lifetime, preservation of normal tissue stem cells, etc. While effective, proton therapy is underutilized, contributing to fewer than 2% of all external beam radiation treatments. To overcome this shortcoming, provided are methods which utilize the highest energy layer and a universal range shifter (URS) to facilitate rapid spot arc therapy (RAPIDSPARC™).

The invention provides an Olaparib derivative as well as a preparation method and application thereof, and relates to the technical field of biological medicines. The Olaparib derivative is a compound as shown in a formula (A) or a derivative thereof, and the structural formula of the compound as shown in the formula (A) is # imgabs0 #; wherein R is equal to # imgabs 1 # or # imgabs 2 #. The compound, the preparation method and the application have the advantages that the designed compound is high in tumor uptake, low in normal tissue uptake and low in target-to-non-target ratio, so that the property of [68Ga] DOTA-Olaparib is greatly improved, and accurate outlining of tumors is facilitated. Meanwhile, the compound provided by the invention has strong chelating ability, and can form a stable chelate with most radioactive metals, thereby facilitating the integration of tumor diagnosis and treatment.

The invention discloses an ablation therapy auxiliary device for the interventional department, and relates to the technical field of medical instruments, the ablation therapy auxiliary device comprises two sets of multi-axis arms, a multi-layer annular gradient magnetic field generator is arranged at the tail end of the other set of multi-axis arms, and a spherical magnetorheological fluid cavity, a laminated electroactive polymer film and a flexible supporting layer structure are adopted through an ablation needle assembly; according to the composite driving mode combining the magnetorheological fluid and the pneumatic micro push rod, the viscosity of the magnetorheological fluid can be rapidly changed under the action of a magnetic field, large-angle flexible rotation of the joint is achieved, the electroactive polymer film can be accurately and finely adjusted at a small angle, and the pneumatic micro push rod can push away surrounding tissues and is matched with the multi-layer annular gradient magnetic field generator at the tail end to achieve large-angle flexible rotation of the joint. The multi-dimensional precise positioning of the ablation needle assembly in the space is achieved, it is ensured that the ablation electrode can be precisely aligned with a tiny focus or a focus in a special position, the flexibility and precision of operation are greatly improved, damage to surrounding normal tissue is effectively avoided, the operation safety is improved, and the ablation success rate of the focus is increased.

The invention discloses an accurate X-ray radiation dose measurement system based on artificial intelligence, which comprises the following steps: acquiring CT image data of a patient and pulse irradiation parameters of radiotherapy equipment through terminal equipment, and calculating a patient specific anatomical structure dynamic influence factor set, a pulse waveform time domain characteristic factor and a tissue interface scattering correction factor; all the parameters are substituted into a dose measurement formula to obtain an accurate X-ray radiation dose value; through a deep fusion image recognition technology (such as a high-resolution anatomical structure, tissue density gradient and heterogeneous region distribution provided by CT / MRI), a secondary particle scattering effect of a junction region of a tumor and a normal tissue can be accurately represented; and dynamic anatomical information (such as respiratory movement and organ displacement) in the image is combined to correct the time characteristic (such as dynamic change under an instantaneous high dose rate) of the pulse radiation field in real time, so that the dose calculation accuracy of a fast algorithm in a complex scene is remarkably improved.

The invention provides a method, device and system for evaluating the effect of intervening small molecules by tumors and a storage medium, and relates to the technical field of bioinformatics, the method comprises the following steps: obtaining a first transcriptome representation of a tumor cell population before target small molecule intervention and a second transcriptome representation of a normal tissue cell population corresponding to the tumors; inputting the first transcriptome representation and the condition vector into a prediction model to obtain a third transcriptome representation; calculating a first distance and a second distance; an intervention effect is evaluated based on a comparison of the first distance and the second distance. According to the method, the influence of the drug on the transcriptome of the whole cell population is efficiently predicted, and the distance between the transcriptome and the health state is quantitatively compared, so that a set of brand new standard for improving the evaluation target from the traditional'killing cell 'to'reversing disease state' is established, and an objective decision basis is provided for developing more accurate drugs.

The invention relates to a tumor target volume segmentation method and device and a storage medium. The method comprises the following steps: acquiring a medical image containing a target tumor; segmenting the target tumor in a spatial dimension according to the medical image to obtain a plurality of first target volumes which are located in the target tumor and are not overlapped; grouping the plurality of first target volumes to obtain a plurality of target volume groups; wherein each target volume group comprises at least one first target volume, and the adjacent first target volumes in the plurality of first target volumes are located in different target volume groups. According to the tumor target volume segmentation method and device and the storage medium provided by the invention, continuous irradiation of adjacent areas can be avoided through differential irradiation of different target volume groups, so that accumulated damage to surrounding normal tissues is reduced.

The invention relates to the technical field of bioinformatics, in particular to an esophageal cancer RNA-protein interaction space prediction method and device based on a graph neural network, and can solve the problem that it is difficult to accurately distinguish the difference of cancer tissue and normal tissue on RPI in a traditional method to a certain extent. The method comprises the steps of obtaining multi-stage space transcriptome data related to esophageal cancer, and performing preprocessing to obtain a multi-modal feature tensor and a canceration stage tag; using the multi-modal feature tensor and the canceration stage tag, using RNA and protein as nodes, defining a static edge based on sequence complementarity and structure matching degree, defining a dynamic edge based on spatial co-expression correlation, and constructing a space-time dynamic graph updated along with the canceration stage; the space-time dynamic graph serves as input, interaction existence, site coordinates and binding energy are predicted through space-time double-branch coding and attention mechanism fusion features, and an interaction prediction result is output.

The utility model relates to a manual planer suitable for an endoscope, which comprises a shell, the front end of the shell is connected with an outer tube assembly through an adjusting knob, an inner tube assembly is arranged in the outer tube assembly, the inner tube assembly is provided with a rotation driving assembly, a rotation guiding mechanism is arranged in the shell, and the rotation driving assembly is in movable contact with the rotation guiding mechanism. An elastic driving assembly is arranged at the position, located on the rear side of the rotary guiding mechanism, of the shell, a collector is arranged on the rear side of the elastic driving assembly, the tail end of the inner pipe assembly is connected with the collector, and the elastic driving assembly is further movably connected with a pressing trigger. Therefore, a motor and the like do not need to be adopted as a power source, operation interference caused by vibration of the motor is avoided, and accurate cutting can be achieved. Cold knife operation can be achieved, tissue separation is achieved through rotary excision, additional cutting edges do not need to be arranged, the front side space of the outer tube assembly is not occupied, other normal tissue cannot be cut, and the endometrium cutting device is particularly suitable for endometrium excision.

The invention relates to the technical field of ablation path planning, in particular to a liver cancer thermal ablation path planning system based on deep reinforcement learning. The system comprises a target and dangerous area mask generation module, a pipeline distance field construction module, a thermal field prediction module, a breathing deformation prediction module, a multi-scale feature fusion module, a behavior preference reward module, a composite reward function module and a decision output module. Clinical pain points such as breathing interference, dangerous area avoidance and individualized adaptation are broken, the precision and safety of the liver cancer thermal ablation operation are improved, normal tissue damage and complications are reduced, intelligent decision support is provided for interventional therapy, and liver cancer minimally invasive therapy is promoted to be upgraded to precision and intelligence.