63 results about "Tumor Pathology" patented technology

The invention relates to the technical field of pathology analysis, in particular to a tumor pathology analysis method based on multi-modal artificial intelligence, which comprises the following steps: obtaining a multi-center and multi-type tissue full-slice image, cutting the full-slice image into a pathology image, and dividing the pathology image after standardization preprocessing into a training set and a test set; constructing a comparison language and an image model for capturing key features in the pathological image through a two-stage pre-training process; a comparison language and an image model are used for pre-training a weight as an initial weight, a task-specific full-connection layer is added, and the model is finely adjusted to obtain a pathological analysis model for realizing different classification regression tasks in tumor pathological analysis. According to the method, the type, staging and prognosis of the tumor can be evaluated more comprehensively by fusing the multi-modal information of the image text, so that the advantages of multi-source data can be fully utilized, and the method is an effective way for solving the bottleneck of the current tumor pathology analysis technology.

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 discloses a tumor pathological image segmentation method and system based on a state space model, the system comprises a state space feature encoder, a multi-scale feature decoder, a side output module and a splicing fusion module, the state space feature encoder comprises four stages of QSSBlock feature encoders, the multi-scale feature decoder comprises a multi-scale feature fusion module and a multi-scale convolution attention module. The multi-scale feature fusion module comprises an upper convolution module and a grouping attention module. According to the method, the global features in the tumor pathological image can be effectively captured, the long-range correlation between the instances can be established, the problem that the instances are mutually independent is solved, segmentation mapping is greatly simplified, and the weak supervision method is more interpretable. By introducing a deep supervision multi-stage side output mode, the hierarchical information of the multi-scale feature map is effectively utilized, high-throughput tumor pathological image segmentation can be quickly and accurately realized, and the method has wide application prospects and important practical values in the field of medical image processing.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention is suitable for the field of medical image analysis, and provides a tumor pathological image segmentation method based on a U-Net neural network, and the method comprises the steps: extracting the gray, texture and shape multi-dimensional features of tumor cells through a U-Net encoder, constructing a feature cluster, building an invasion direction probability model based on the cluster, analyzing the morphological features of a necrotic region, and speculating the growth speed. And dynamically adjusting the segmentation sensitivity threshold of the decoder, and finally outputting a three-channel segmentation map containing a tumor core region, a false envelope invasion region and a capillary invasion region. According to the scheme, through multi-dimensional feature fusion and biological behavior modeling, precise characterization of tumor heterogeneity is achieved, the adaptability of a segmentation model to different invasion active areas is improved through a dynamic sensitivity regulation mechanism, a segmentation result with anatomical positioning and biological evaluation values is provided for clinic, and the segmentation accuracy is improved. And diagnosis and treatment decision of tumors are effectively assisted. The method is easy and convenient to operate and high in automation degree and has remarkable clinical application value.

The invention provides a method for inducing HL-60 cells to be differentiated into neutrophil-like cells, and belongs to the technical field of cell culture. According to the method, the induced differentiation efficiency of the HL-60 cells to the neutrophil-like cells (D-HL-60) and the cell quality are remarkably improved, high-proportion functional D-HL-60 cells can be obtained, a good motility rate can be maintained, and a stable and reliable cell model is provided for researching a neutrophil differentiation mechanism, screening related drugs and analyzing immune functions. Particularly, the functional D-HL-60 cells obtained on the basis of the method can be used as an ideal model, deep analysis of neutrophil trapping nets (NETs) in inflammation and tumor pathological mechanisms is assisted, and an experimental basis is provided for development of therapeutic strategies of targeted NETs.

The invention provides a same-layer registration method for an MR cross section diagram and a total tumor pathological section based on 3D printing. The same-layer registration method comprises the steps of obtaining an MR image, extracting an interested volume, designing a 3D printing mold matched with a focus in shape and assisting registration of a pathological section and the MR image. MR image layering thickness information is embedded into mold design, the thickness of a mold section is consistent with the thickness of an MR scanning layer, and direction information is marked in a mold to ensure spatial alignment of a pathological section and an MR image. The manufactured pathological section is accurately matched with an MR cross section diagram through a non-rigid image registration algorithm, and layer-by-layer comparative analysis of a tumor image and pathology is achieved. The whole tumor area can be covered, and registration errors caused by needle biopsy limitation, tissue deformation and resolution difference in the prior art are overcome. The method is high in applicability and can be used for image-pathology conjoint analysis of various types of tumors, the registration precision is remarkably improved, the error is controlled within 0.1 mm, and important support is provided for tumor research and accurate diagnosis.

The invention relates to the technical field of tumor screening, and provides a lung tumor early screening method and system based on deep learning, and the method comprises the steps: obtaining the screening data of the lung of a patient and the physique data of the patient, and determining the lung features corresponding to different screening data; performing risk grading on the patients according to the physique data of the patients; performing feature fusion on the lung features through a preset lung tumor recognition network to generate a screening model of lung tumor pathological features; according to the screening model, performing prediction weighting on different tumor pathological features in the lung features, and determining tumor confidence values of the different lung tumor pathological features; setting an evaluation threshold value, judging whether the tumor confidence value after the lung characteristics are combined with the evaluation coefficient is within the range of the evaluation threshold value, and when the tumor confidence value is within the range of the evaluation threshold value, judging the real-time risk value of the tumor existing in the lung of the patient through the tumor confidence value and the unique risk coefficient.

The utility model provides a bone tumor pathological specimen slice preservation device which comprises a preservation box and a placing structure, the placing structure comprises a driving box and two containing boxes, the driving box is vertically and fixedly connected to the bottom of the inner wall of the preservation box, and the two containing boxes are vertically and fixedly connected to the bottom of the inner wall of the preservation box. The two containing boxes are fixedly connected to one side and the other side of the bottom of the inner wall of the storage box correspondingly, a threaded rod is rotatably and vertically arranged in an inner cavity of the driving box, and a plurality of first threaded sleeves are connected to the surface of the threaded rod in a threaded mode. According to the bone tumor pathological specimen section preservation device, a threaded rod reversely rotates to drive a plurality of preservation boxes and a movable rod to descend, after a sponge cushion makes contact with the surface of a tumor pathological specimen section, a spring contracts to generate elastic force, the elastic force reacts on the tumor pathological specimen section, and then the tumor pathological specimen section can be clamped and fixed; and the tumor pathological specimen slice is more stable in the use process.

The present invention relates to the field of pathology analysis technology, and more specifically to a tumor pathology analysis method based on multimodal artificial intelligence, comprising the following steps: acquiring multi-center, multi-type whole-slice tissue images, cutting the whole-slice images into pathology images, and dividing the standardized pre-processed pathology images into training and test sets; constructing a contrastive language and image model for capturing key features in pathology images through a two-stage pre-training process; and fine-tuning the model using the pre-trained weights of the contrastive language and image model as initial weights and adding task-specific fully connected layers to obtain a pathology analysis model for implementing different classification and regression tasks in tumor pathology analysis. By integrating multimodal information from images and text, the present invention can more comprehensively assess tumor type, stage, and prognosis, thereby fully leveraging the advantages of multi-source data and effectively addressing the current bottlenecks in tumor pathology analysis technology.

The invention relates to the technical field of image recognition, and particularly discloses an auxiliary blood tumor pathological diagnosis system and method based on artificial intelligence, and the system extracts a pathological image group of a patient through a pathological diagnosis auxiliary platform, analyzes the data of each pathological image, and judges the effective feature value of each pathological image; the effective feature values of the pathological images are compared with a predefined effective feature threshold value to obtain a comparison result, and the pathological diagnosis auxiliary platform judges whether the effective features of the pathological images are enhanced or not based on the comparison result; and extracting cell characteristic data in each pathological image according to each pathological image, judging the characteristic complexity of each pathological image, and performing difference analysis on the pathological image group to complete auxiliary blood tumor pathological diagnosis.

The present invention discloses an intelligent virtual staining method and device for Epstein-Barr virus-related tumor pathology slice images, and relates to the field of image intelligent processing technology. The method comprises: obtaining H&E images of in vitro tissue slices of Epstein-Barr virus-positive cells, performing EBER in situ hybridization on the slices after elution and staining, and then scanning them into slice images, performing image registration on the above two images, and obtaining training images after marking Epstein-Barr virus-positive cells; then obtaining images of in vitro tissue slices of Epstein-Barr virus-negative cells as comparison images, and training a virtual staining model that can intelligently distinguish and virtually stain Epstein-Barr virus-positive cells in H&E slice images based on the training images and the comparison images. The present invention can reduce the complexity of the operation process of artificial intelligence processing slice image technology, improve the intelligence level of artificial intelligence processing slice image technology, and replace the biological marker method with intelligent virtual staining, which has great scientific significance, social value and economic benefits.

The utility model relates to the technical field of pathological specimen manufacturing, and discloses a bone tumor pathological specimen sampling adjustable fixator which comprises a base, a fixing plate is fixedly connected to the base, at least one evenly-arranged slide making groove is formed in the fixing plate, a stroke through groove is formed in the position, above the slide making groove, in the fixing plate, and the stroke through groove is communicated with the slide making groove. A long moving rod is slidably arranged in the stroke through groove, holding plates matched with the sheet making grooves are fixedly connected to the long moving rod, electric push rods are arranged in the holding plates, pressing plates matched with the upper glass sheet are fixedly connected to the lower ends of the electric push rods, and limiting moving plates are fixedly connected to the two ends of the long moving rod; and fixed limiting plates are fixedly connected to the two sides of the base correspondingly, plate body stroke grooves matched with the limiting moving plates are formed in the fixed limiting plates correspondingly, and sliding rods are fixedly connected to the fixed limiting plates correspondingly. Compared with the prior art, the upper glass fixing device has the advantage that upper glass of different sizes can be fixed.

The invention relates to a bone tumor pathological specimen sampling device which comprises a supporting rotating assembly and a clamping assembly arranged on the supporting rotating assembly, the clamping assembly is used for clamping arms and legs of a human body, a displacement assembly is arranged on the supporting rotating assembly, and a displacement plate with a round hole in the upper side face is arranged on the displacement assembly. The displacement assembly can drive the displacement plate to move, a rotating ring is rotationally connected to a circular hole of the displacement plate, a plurality of first electric telescopic rods arranged in a circular array are rotationally arranged on the upper side face of the rotating ring, the other ends of the first electric telescopic rods are elastically and rotationally connected with a hanging assembly, and a guide cylinder is vertically arranged at the circle center of the circular hole; the guide cylinder is in ball joint with a ball joint ring, connecting bases with the same number as the first electric telescopic rods are arranged on the side face of the outer edge of the ball joint ring, the connecting bases are rotationally connected with the first electric telescopic rods and the hanging assemblies, and the rotating connecting parts are located at the same position. The sampling device has the advantage of being convenient to sample.

The invention discloses a survival prediction method based on a tumor pathological image, and the method comprises the steps: obtaining a key region of the tumor pathological image through deep learning and an attention mechanism, and obtaining a cell nucleus boundary feature, a cell nucleus texture feature and / or a cell nucleus image structure feature based on the key region of the tumor pathological image; and survival prediction is carried out through the trained machine learning model. According to the survival prediction method, the accuracy of survival prediction of tumor patients is improved, and meanwhile, the defect that a traditional deep learning method is poor in interpretability is overcome.

The present patent application relates to nanoparticles for the delivery and targeting of a non-tumour-specific antigen in cancer cells, comprising a matrix support based on a biocompatible material, the non-tumour-specific antigen, and an adjuvant, for use in recalling, in cancer patients who have a specific immunity for the non-tumour- specific antigen pre-existing to the tumour pathology, the immune response specific to the non-tumour-specific antigen against the cancer cells. A further object of the application is anti-tumour pharmaceutical formulations comprising nanoparticles and kits comprising the aforementioned anti-tumour pharmaceutical formulations in combination with traditional anti-tumour vaccines.

The invention discloses an intelligent virtual staining method and device for an EB virus related tumor pathological section image, and relates to the technical field of intelligent image processing, and the method comprises the steps: obtaining the Hamp of an in-vitro tissue section of an EB virus positive cell; e image, performing EBER in-situ hybridization on the section after eluting and dyeing the section, then scanning the section into a section image, performing image registration on the two images, and marking EB virus positive cells to obtain a training image; obtaining an image of an in-vitro tissue slice of the EB virus negative cell as a comparison image, and training an intelligent Hamp according to the training image and the comparison image; and E, distinguishing EB virus positive cells in the slice image, and performing virtual staining by using a virtual staining model. According to the method, the complexity of the operation process of the artificial intelligence slice image processing technology can be reduced, the intelligent degree of the artificial intelligence slice image processing technology is improved, a biological marking method is replaced with intelligent virtual dyeing, and the method has good scientific significance, social value and economic benefits.

The invention relates to negative functional modulators of at least one variant of the non-erythrogenic erythropoietin (V-EPO) and pharmaceutical compositions or kits containing them. Such functional negative modulators of V-EPO may be a mono- or multi-specific antibody anti-EV3, anti-EV4, anti-EV1-4, anti-EV1-5, anti-EV1-1, or EV2-1, or anti-Epo receptor (EpoR) anti-EPHB4, anti-CSF2RB, an antisense oligonucleotide, DNA decoy, RNA decoy, a ribozyme, an antagomiR, a shRNA, LNA or siRNA.Several uses of these functional modulators are described, which have been advantageously employed as a medicament and for the treatment of an oncological pathology, a proliferative pathology, chronic inflammatory diseases on an autoimmune and non-autoimmune basis, of neurodegenerative diseases, and in the treatment of patients undergoing organ or tissue transplantation.The invention also describes variants of EPO for use in the diagnosis and in the treatment of an oncological pathology, a proliferative pathology, chronic inflammatory diseases on an autoimmune and non-autoimmune basis, of neurodegenerative diseases, and in the treatment of patients undergoing an organ or tissue transplantation and as a diagnostic agent.According to another aspect, a monoclonal antibody to at least one of the variants of the erythropoietin is described.According to yet another aspect, the use of at least one alternative splicing variant of non-erythrogenic EPO and the measurement thereof at tissue and / or systemic level is described, as well as the study of the methylation status of the promoters of the genes involved in the EPO signalling pathway (by way of example EPO, EPOR, EPHB4, CSF2RB), as diagnostic, prognostic and predictive markers of an oncological pathology, a proliferative pathology, neurodegenerative or inflammatory pathology.

The invention belongs to the technical field of tumor medicine, and discloses a tumor pathological specimen storage device which comprises a bottom plate, and a rotating device, a temperature control sealing device and a linkage device are arranged above the bottom plate. Through organic combination of a plurality of devices, a storage system which is compact in structure and comprehensive in function is formed. The temperature control pipe accurately controls the temperature, especially independent temperature adjustment can be carried out according to the characteristics of different tumor pathological specimens, a personalized and stable storage environment is provided for the specimens, the quality of different types of specimens is effectively guaranteed, diversified storage requirements are met, the device is more practical and scientific, and actual application and operation are facilitated.

The utility model relates to the technical field of orthopaedic specimen sampling, and discloses a bone tumor pathological specimen sampler which comprises a sleeve shell and a sleeve, one end of the sleeve shell is connected with one end of the sleeve, the other end of the sleeve shell is provided with a cover body, a rotating shaft is rotatably installed at the axis of the sleeve shell, and an arc-shaped scraper is installed on the outer side of the rotating shaft. The sleeve is provided with an opening matched with the arc-shaped scraper to penetrate out, two side walls of the opening of the sleeve are stacked, a plurality of groups of barbs are arranged on the inner wall of the arc-shaped scraper, and an inclined silica gel sheet is arranged on one side, close to the inner wall of the arc-shaped scraper, of the opening of the sleeve. Compared with the prior art, the bone specimen taking device has the advantages that the arc-shaped scraper extends out of the sleeve shell by rotating the rotating shaft, a bone specimen is taken down from a bone through the arc-shaped scraper, the taken-down specimen is placed between the silica gel sheet and the inner wall of the arc-shaped scraper, and when the arc-shaped scraper retracts towards the sleeve shell, the specimen is driven to move towards the interior of the sleeve shell through the barbs; the specimen is prevented from falling off and is convenient to take out.

The invention relates to the technical field of tumor pathological specimen fixing and preservation, in particular to a tumor pathological specimen fixer which comprises a fixing box, a storage drawer is inserted into the fixing box, and a locking mechanism for conveniently limiting the drawing position of the storage drawer is arranged in the fixing box. An identifying and fixing mechanism which is convenient for fixing and identifying specimens is arranged in the storage drawer, and a taking and placing mechanism which is convenient for quickly taking and placing the specimens in a non-contact manner and is convenient for taking and placing the specimens is arranged above the storage drawer. According to the tumor pathological specimen fixing device, a tumor pathological slide specimen can be conveniently clamped, stored and fixed through a movable cavity formed in a storage drawer and a lifting rod, a fixing sleeve and a buffering cushion strip arranged in a lifting groove, and a sliding seat and a second magnet are slidably connected to a sliding rail fixedly connected to the bottom of an inner cavity of the movable cavity; and the second magnet slides below the first magnet, so that the first magnet and the second magnet are conveniently utilized to jack up the fixed sleeve and the taking and placing mechanism for observation and taking.

The invention provides a tumor pathological image typing and grading method and device based on deep learning and a residual network technology, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a tumor pathological image, segmenting a tissue region, carrying out the color normalization, and dividing the tumor pathological image into image blocks; constructing a deep convolutional feature extraction network based on a residual network, and extracting shallow and deep feature maps; performing up-sampling on the deep feature map and performing weighted fusion on the deep feature map and the shallow feature map, and inputting the deep feature map and the shallow feature map into parallel convolution branches to obtain typing and grading feature maps; calculating a feature deviation degree based on the hierarchical feature map to generate a space attention map, and determining a region focusing degree; enhancing the typing feature map by using the attention weight; respectively carrying out global pooling and classification on the enhanced feature map and the graded feature map, and outputting a typing and grading result; screening high-focus points according to an area focusing degree set threshold value, and generating a key area thermodynamic diagram; according to the invention, typing and grading cooperative processing is realized, and the diagnosis efficiency and accuracy are improved.

The present invention discloses a bone tumor image enhancement method and system, comprising: step 1: acquiring a bone tumor pathology image, and performing fuzzy processing on the bone tumor pathology image, using the fuzzy processed bone tumor pathology image as an input data set, and using the original bone tumor pathology image as an output data set; step 2: constructing an image super-resolution enhancement model, and training the image super-resolution enhancement model using the input data set and the output data set until the loss function value of the image super-resolution enhancement model tends to converge; step 3: acquiring the bone tumor pathology image to be enhanced in real time, inputting the trained image super-resolution enhancement model, and generating a bone tumor pathology image with completed image super-resolution enhancement. The present invention solves the problem of image distortion of subtle pathological tissue parts in pathology images caused by conventional image super-resolution enhancement methods.

The present invention discloses a tumor pathology image segmentation method and system based on a graph convolutional contrastive learning network, which relates to the field of image segmentation technology. The method includes: obtaining a tumor pathology image and a segmentation label corresponding to the tumor pathology image, generating a tumor pathology image training set based on the tumor pathology image and the segmentation label corresponding to the tumor pathology image; preprocessing the tumor pathology image training set, inputting the preprocessed tumor pathology image training set into a pre-established offset graph convolutional contrastive learning network model for training, and obtaining a trained offset graph convolutional contrastive learning network model; receiving a tumor pathology image to be segmented, inputting the tumor pathology image to be segmented into the trained offset graph convolutional contrastive learning network model, and outputting a tumor pathology image segmentation result to achieve accurate image segmentation.

The invention relates to the technical field of medical instruments, and discloses a bone tumor pathological tissue sampling device integrated with a cutting function, which comprises a left side shell and a right side shell which are connected through a screw, and also comprises a cutting assembly and a tumor sampling assembly, the front ends of the left side shell and the right side shell are connected with end covers through screws, the end faces of the end covers are fixedly connected with sleeves, and the outer walls of the sleeves are fixedly connected with lug blocks and side blocks. Sampling and cutting functions are ingeniously combined, after cutting is completed, a buckle plate is pulled to drive a string, a micro winch and other parts, so that a biting block rotates around a vertical shaft to be close to a cutting blade, the biting block and the cutting blade are matched to bite tumor pathological tissue, and sawteeth on the side edge of the biting block facilitate easy sampling; by means of the integrated design, the sampling process of bone tumor pathological tissue is effectively simplified, the tedious step of switching multiple tools in traditional operation is avoided, and the sampling efficiency is greatly improved.

The invention discloses a microscopic image enhancement and restoration method and system for tumor pathology, and relates to the technical field of medical imaging. Comprising the following steps: S1, acquiring a normalized value of a microscopic image, comparing the normalized value with a preset normalized value, and determining a final microscopic image; s2, determining an artifact area and an artifact type corresponding to the final microscopic image, and processing the artifact area; and S3, optimizing the microscopic image after the artifact region processing, and reconstructing a three-dimensional blood vessel structure in the microscopic image to obtain an optimized microscopic image. According to the invention, artifact identification is carried out on the preprocessed microscopic image through the artifact detection model, so that tiny noise existing in the microscopic image can be reduced, and the identification efficiency and the identification result can be improved, so that the artifact area in the microscopic image can be better identified, and the identification accuracy of the microscopic image is improved. And the recovery efficiency of the artifact region is further improved.

The invention provides a pathology convolutional neural network visualization method and system, and the method comprises the steps: transmitting various tumor pathology images into a target convolutional layer of a CNN model through forward propagation, recording and extracting the output information of a feature map, and obtaining a multi-channel matrix feature map; the activation value of each channel of the multi-channel matrix feature map is linearly zoomed and visualized into a color image, channels are selected or multiple channels are combined, and a pathological convolutional neural network program penetration visualization model is created through the internal process of a program penetration visualization convolutional neural network; inputting a to-be-predicted gland pathology image data set to the pathology convolutional neural network program to penetrate through the visualization model, and automatically outputting a target convolutional layer feature map; and performing target convolutional layer-by-layer identification on the target convolutional layer feature map by using a multi-class neoplasia intelligent identification model, exploring the internal process of the multi-class neoplasia intelligent identification model through program penetration, and intelligently predicting the neoplasia trend of the gland pathological image.

The present invention discloses a construction method and an evaluation system for evaluating a model for evaluating internal heterogeneity of a tumor and its application. The construction method includes reading the average nuclear radius, median staining depth and average staining intensity of each tumor cell in a tumor pathology slice image, represented by Xi; sorting Xi to generate a median Xm, and substituting it into the formula MAD=median(|Xi‑Xm|) to obtain MAD; the number of tumor pathology slice images is N, then N MADs are obtained correspondingly, N≥50, and the tumor pathology slice images are from the same tumor cohort; sorting the N MADs to obtain the ranks corresponding to the tumor pathology slice images, dividing the ranks by N to obtain S, so the value range of S is 0‑1, and the tumor internal heterogeneity evaluation model is obtained. The present invention avoids the need for a large amount of sequencing for genomic heterogeneity, reduces costs, and has a high correlation coefficient with genomic heterogeneity.

The invention discloses an intelligent closestool control system which comprises a negative pressure suction module, a deodorization module, a tumor disease detection module, a urination function health state evaluation module and an interaction module. An intelligent closestool control method comprises the following steps that S1, the working mode of a negative pressure pump is adjusted; s2, starting a deodorization function of the intelligent closestool and a ventilation system of the toilet; s3, collecting part of the excreta diluent as a sample liquid, obtaining and analyzing genome sequence information of cells in the sample liquid, and analyzing and predicting the correlation of the genome sequence information and tumor pathology by using bioinformatics; s4, judging whether the user has urination dysfunction or not through continuous monitoring and analysis of the urine flow rate data and the urine volume data; and S5, obtaining a tumor disease analysis result and a urination function health state analysis result, and sending the analysis results to the user side APP and the intelligent closestool health detection display screen. The invention further discloses a closestool intelligent control method corresponding to the system and a closestool applying the intelligent control system.

The present invention belongs to the field of medical device technology and discloses a tumor pathology section dehydration device, comprising a workbench, a mounting block mounted on the workbench via a locking assembly; a dehydration cylinder detachably connected to the mounting block, a dehydration box for containing tissue samples disposed in the dehydration cylinder; an emptying assembly comprising an emptying pipe disposed in an assembly slot, the top end of the emptying pipe being connected to the dehydration cylinder, and the bottom end of the emptying pipe being respectively connected to a waste liquid cylinder and an exhaust gas cylinder disposed in the workbench; a centrifugal assembly disposed on the workbench, the output end of the centrifugal assembly being transmission-connected to the dehydration box to drive the dehydration box for centrifugal dehydration; and a liquid inlet assembly disposed on the workbench and connected to the dehydration box. The present invention has a compact structure, a high degree of automation, and organically combines physical dehydration and chemical dehydration, thereby overcoming the shortcomings of physical dehydration and chemical dehydration, improving the dehydration efficiency and quality of tissue samples, reducing dehydration costs, and accelerating the efficiency and accuracy of determining tumor properties.