22 results about "Primary lesion" patented technology

The invention discloses an HER2 expression state evaluation method and device, equipment and a storage medium. The method comprises the following steps: dividing an HE dyeing image of a target object into a plurality of first sub-images, and determining an infiltration area of the HE dyeing image through the first sub-images; dividing the IHC dyeing image of the target object into a plurality of second sub-images, and determining tumor cells of various dyeing degrees in the IHC dyeing image through the second sub-images; determining a first expression state of the target object HER2 according to the infiltration area and the tumor cells; under the condition that the first expression state is IHC < 2 + >, determining a primary focus area in the image of the target object, and obtaining image features through the primary focus area; obtaining HE features and IHC features through the infiltration area and the tumor cells, and generating multi-modal features according to the image features, the HE features and the IHC features; and inputting the multi-modal features into the first classification network to obtain a second expression state of the target object HER2.

The invention relates to the technical field of biology, in particular to a targeting peptide modified nano-micelle co-loaded with near-infrared fluorescent dye and an immunologic adjuvant, a preparation method and an application of the targeting peptide modified nano-micelle co-loaded with the near-infrared fluorescent dye and the immunologic adjuvant. A nano-carrier of the nano-micelle is a PLGA-PEG nano-carrier, a targeting peptide is a TMTP1 targeting peptide, a near-infrared fluorescent dye is indole green, and an immunologic adjuvant is MPLA. The nano-micelle can target ovarian cancer primary lesion and metastatic lesion, increase aggregation of micelles in tumor tissues, generate PTT effect through laser irradiation under the guidance of near-infrared fluorescence, cause immune death of tumor cells, release tumor-associated antigens, and promote DC cell recognition of presentation antigens and DC cell activation under the assistance of an immunologic adjuvant, so that the tumor-associated antigens can be degraded, and the tumor-associated antigens can be degraded. In addition, macrophages and NK cells can be collected, T cell immune response is induced, secretion of immunomodulatory factors is promoted, other tumor cells not irradiated by laser are recognized and killed, immune memory is generated, and tumor relapse is inhibited.

The invention relates to the technical field of biological medicine, in particular to application of a TMEM43 inhibitor in preparation of an anti-gastric cancer drug. According to the application disclosed by the invention, the expression profiles of the gastric cancer primary lesion and the peritoneal metastasis lesion are compared and analyzed through a single cell sequencing technology, and the TMEM43 is found to be abnormally high in expression in the peritoneal metastasis lesion. A further research proves that after the expression of the TMEM43 gene in the human gastric cancer cells is knocked down, the migration ability and the clone formation ability of the cells are obviously reduced; meanwhile, the expression levels of various markers related to the intracellular epithelial-mesenchymal transition (EMT) process also present different degrees of down-regulation (N-Cadherin, Snail, Twist1 and TMEM43 up-regulation) or up-regulation (E-cadherin up-regulation), and finally, the therapeutic effect of inhibiting the gastric cancer progress can be exerted. The key effect of TMEM43 in occurrence and development of gastric cancer is defined, and brand new theoretical guidance and technical direction are provided for research and development of gastric cancer treatment drugs.

This invention relates to the field of artificial intelligence in oncology medicine, specifically disclosing a system for quantifying residual tumor burden in gastric cancer, comprising: a data acquisition and interaction terminal, an AI core computing center, and a clinical precision decision support platform; the data acquisition and interaction terminal is configured to acquire in parallel high-resolution digital pathological slides of patients after surgery, continuous physiological time-series characteristic sequences after surgery, and the molecular expression abundance of POU4F1 transcription factor and TGF-β signaling pathway in tumor tissue; the AI ​​core computing center includes a three-dimensional quantification unit for pathological images, a dynamic correction unit for physiological homeostasis, and a molecular phenotypic decision unit; the three-dimensional quantification unit for pathological images is used to automatically segment and obtain the geometric mean diameter of the primary tumor bed, the proportion of residual tumor in the primary lesion, the number of positive lymph nodes, and the proportion of residual tumor in the worst-regressed lymph nodes; the technical solution of this invention can solve the problems of coarse quantification and large subjective bias.

The invention discloses a progression stage gastric cancer T stage intelligent four-classification method based on a low water filling stomach CT venous phase enhanced image, and relates to the technical field of medical imaging prediction. The method comprises the following steps: collecting an abdominal vein phase enhanced CT image of gastric cancer in a progression phase containing a hypotonic water filling stomach; the method comprises the following steps: preprocessing an abdominal vein phase enhanced CT image containing a low-tension water filling stomach to obtain sample data; a 3D-Conv-Unet network is utilized to automatically identify and segment a development stage gastric cancer primary lesion CT image area, and an image physician quickly confirms the image area to obtain an MASK image; and carrying out channel splicing on the vein phase enhanced CT full-layer abdominal image and the MASK image, processing by adopting a 3D convolution kernel to obtain an image sequence, and inputting the image sequence into a SmallFousNet network to obtain a progression phase gastric cancer T-stage classification result.

1. The name of the product of this design: Breast tissue marker. 2. Purpose of the product of this design: used for marking primary lesions in breast tissue. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the key points of the design: Design 1 Stereoscopic drawing 1. 5. Designate Design 1 as the base design.

The invention belongs to the field of biomedical engineering, and relates to a microfluidic chip for simulating a tumor metastasis cascade process, which sequentially comprises a substrate (1), a middle-layer porous film (2) and a cover plate (3) from bottom to top, a U-shaped channel (4) is formed in one surface, close to the middle-layer porous film (2), of the substrate (1); a left side channel (5) and a right side channel (6) are formed in one surface, adjacent to the middle-layer porous film (2), of the cover plate (3); the substrate (1), the middle-layer porous film (2) and the cover plate (3) are tightly bonded, the left side channel (5) is overlapped with a left side channel of the U-shaped channel (4), and the right side channel (6) is overlapped with a right side channel of the U-shaped channel (4). The micro-fluidic chip disclosed by the invention can be used for simulating a tumor primary focus and a metastasis microenvironment, and simulating a whole tumor metastasis cascade process including a plurality of stages of tumor cell infiltration, circulation, extravasation and colonization in vitro.

The present invention discloses a multimodal breast magnetic resonance image classification method and system, which relates to the field of medical and image processing technology. The method outputs the acquired magnetic resonance image to be classified and the parameter map to be classified into a target classification network to obtain the prediction probability of the lesion category, and determines the lesion category according to the prediction probability of the lesion category; the target classification network is obtained by training based on training samples and calibration convolutional neural networks; the training samples include input data and labels; the input data includes a first image and a first parameter image corresponding to the first image; the first image is a three-dimensional dynamic enhanced magnetic resonance image with a primary lesion area; the structure of the calibration convolutional neural network includes a first attention network, a second attention network and a fusion network. The present invention combines the attention mechanism, multi-scale features and multi-modal information, so that the neural network learns more effective information that is helpful for feature extraction, thereby improving the accuracy of lesion category determination.

The application relates to the technical field of intelligent medical treatment, and discloses a schistosomiasis laboratory quality management remote consultation system and method based on artificial intelligence. The application effectively solves the problems of weak network transmission congestion, long tail impurity misjudgment and system failure caused by environmental drift that the primary medical institutions face when performing schistosomiasis remote consultation, accurately extracts primary lesion features by dynamically perceiving local optical density changes on the edge side, and under the limited network communication bandwidth, preferentially guarantees high-fidelity lossless push flow of core lesion features, simultaneously implements adaptive degradation and physical truncation on redundant background features, and combines a local cache mechanism to completely eliminate feature space tearing and diagnostic data loss under extremely deteriorated communication environment.

The invention discloses an ovarian endometriosis glandular epithelial organ and interstitial cell co-culture model and a construction method thereof. The construction method comprises the following steps: S1, constructing an ovarian endometriosis cyst glandular epithelial organ; s2, constructing a 3D culture body of the ovarian endometriosis cyst interstitial cells; and S3, digesting the glandular epithelium organ, mixing the glandular epithelium organ with the interstitial cells in proportion, and embedding the glandular epithelium organ in matrigel for three-dimensional co-culture. According to the invention, a patient-derived ovarian endometriosis glandular epithelial organ and interstitial cell co-culture system is established for the first time, the model and a primary focus are highly homologous in tissue morphology and biomarker expression, and the bionic degree is high; it is clear that the number of glandular epithelial organs is the largest when the ratio of epithelial cell clusters to interstitial cells is 1: 2, the vitality of the glandular epithelial organs in a group with the ratio of 1: 2 to 1: 4 is remarkably improved, and the technical bottlenecks that an existing co-culture model is fuzzy in proportion, and the cell viability is difficult to maintain are solved.

The invention discloses a modular intelligent breast cancer organ chip system and a use method thereof, and aims to simulate the processes of breast cancer primary lesion invasion, lymphatic / hematogenous metastasis and far-end colonization and realize tumor metastasis mechanism research, microenvironment dynamic monitoring and related drug evaluation. All the modules (such as a tumor primary focus module, a bionic blood vessel module, a lymphatic network module and a far-end organ module) have independent cell culture, fluid perfusion control and integrated sensor arrays so as to realize micro-environment dynamic monitoring. The modules are connected and subjected to material exchange through standardized reconfigurable interfaces (including direct fluid butt joint and selective permeable membrane interfaces). The modular architecture endows the system with high flexibility and expandability, can quickly customize and combine specific transfer mechanism research, microenvironment parameter monitoring or drug efficiency evaluation, and ensures the quality control and function specificity of each unit. And a foundation is laid for developing an intelligent sensing and feedback regulation organ chip system for cancer research.

The invention relates to the technical field of tumor biology and peptide drug research, in particular to a functional polypeptide with an ovarian cancer metastasis resisting effect and a preparation method, the functional polypeptide is a synthetic polypeptide pAHSG, the amino acid sequence of the synthetic polypeptide is MGVVSLGSPSGEVSHPRKT, the molecular weight is 1925.19 g / mol, and the isoelectric point is 10.043; according to the present invention, the iTRAQ-8 marker is combined with the LC-MS / MS peptiomics method to identify more than 2500 polypeptides in one experiment, such that the screening range and the screening efficiency of the candidate functional molecules are significantly improved, and compared with the traditional Western blot or immunohistochemical analysis of 1-2 molecules, the method can screen hundreds of polypeptides significantly different from the primary lesion at the same time, and the screening efficiency of the candidate functional molecules is significantly improved; and the molecular weight of more than 98% of polypeptide is less than 4kDa, so that the polypeptide is more suitable for being developed into drugs.

The present disclosure relates to the technical field of biological medicine, in particular to a polypeptide targeting PD-L1 and use thereof. The polypeptide provided by the present disclosure has high affinity and good stability. Based on the RDC of the polypeptide, the tumor primary lesion or metastatic lesion can be precisely targeted, and the polypeptide will not be retained in the tissues near the tumor and the tissues in non-tumor parts of the whole body, and the polypeptide can also avoid being rapidly degraded by proteases.

There are many cases of late recurrent breast cancer that recurs after 5 years or more after surgery. If a marker for determining a late recurrence risk can be found, it can lead to early detection and treatment of recurrence. The present inventors analyzed clinical specimens and found that the ELEANOR expression correlates with the late recurrence of breast cancers. The risk of late recurrence can be tested by analyzing the ELEANOR expression in a breast cancer primary lesion. It is also possible to screen therapeutic drugs for breast cancers by using ELEANOR expression as an indicator. Furthermore, siRNA and LNA that suppress ELEANOR expression can also function as therapeutic drugs for breast cancers.

The invention discloses a breast cancer axillary lymph node metastasis prediction method and device based on cross-modal data, and the method comprises the steps: determining a tumor cell region and a tumor interstitial region in a pathological image according to a plurality of sub-images of the pathological image, and determining tumor omics features and interstitial omics features; obtaining effective sub-images from the plurality of sub-images according to the tumor cell region and the tumor interstitial region, and obtaining pathological features of the effective sub-images; determining an armpit area and a primary focus area according to the image sequence of the detection object, and obtaining image features of the armpit area and the primary focus area; mapping the pathological features and the image features to a unified semantic space through a semantic alignment model to obtain semantic features, and fusing the semantic features through an attention model to obtain first multi-modal features; performing feature extraction on the gene data of the detection object to obtain genomics features; and predicting the lymph node metastasis condition according to the first multi-modal feature, the genomics feature, the neomics feature and the interstitial omics feature.

The invention belongs to the field of traditional Chinese medicines, and particularly relates to a traditional Chinese medicine composition for treating lung cancer bone metastasis and application thereof. The traditional Chinese medicine composition comprises the following main active ingredients in parts by mass: 20-40 parts of codonopsis pilosula, 20-40 parts of poria cocos, 20-40 parts of astragalus membranaceus, 10-30 parts of bighead atractylodes rhizome, 10-20 parts of liquorice, 10-30 parts of curcuma zedoary, 10-20 parts of pinellia ternate, 5-10 parts of pericarpium citri reticulatae, 20-40 parts of oldenlandia diffusa, 10-30 parts of fructus psoraleae, 10-30 parts of rhizoma drynariae, 10-30 parts of herba epimedii and 10-20 parts of rhizoma arisaematis. Animal experiments show that the traditional Chinese medicine composition not only can inhibit the growth of primary lesion tumors, but also can slow down the progression of bone metastatic lesion tumors, and when the traditional Chinese medicine composition is combined with chemotherapeutics for application, the tumor inhibition effect of the chemotherapeutics can be obviously enhanced, the survival time of mice can be obviously prolonged, and the curative effect of the mice is improved. And weight loss and visceral toxic and side effects caused by chemotherapeutic drugs are relieved.

The present invention provides a method comprising the steps of: acquiring a biological tissue image and a pixel-level diagnosis result of the biological tissue image, wherein a primary lesion pattern and a secondary lesion pattern of the biological tissue image is acquired; searching for a first partial region that includes a large part of the primary lesion pattern in the pixel-level diagnosis result corresponding to a corresponding region from among partial regions of the biological tissue image, and generating the corrected pixel-level diagnosis result; and searching for a second partial region that includes a large part of the secondary lesion pattern in the corrected pixel-level diagnosis result corresponding to the corresponding region from among the partial regions of the biological tissue image, and outputting a representative lesion image of the biological tissue image.

The invention discloses a preparation method and application of a bimetallic composite nanomaterial for targeting a salivary gland adenoid cystic carcinoma tumor. The prepared bimetallic composite nanomaterial has SACC homologous targeting ability, and is used for systematically activating anti-tumor immune response of cold tumors. Under 808 nm laser irradiation, the nanometer material promotes active oxygen generation and glutathione depletion in tumor cells and aggravates oxidative stress by means of multi-enzyme activity and through photo-thermal therapy / photodynamic therapy / chemical dynamic therapy, generates oxygen to maintain PDT / CDT curative effect, effectively induces tumor cell immunogen cell death, and has a good application prospect. The adaptive anti-tumor immune response and cytotoxic T lymphocyte infiltration are obviously enhanced, so that the growth of primary lesion and metastatic lesion is inhibited. The invention provides an effective NIR-triggered space-time regulation and control system for reversing SACC cold tumor phenotype and inhibiting systemic metastasis.

The application discloses a breast cancer axillary lymph node metastasis prediction method and device based on cross-modal data, which comprises the following steps: determining tumor cell regions and tumor interstitial regions in a pathological image according to a plurality of sub-images of the pathological image, and determining tumor omics features and interstitial omics features; obtaining effective sub-images according to the tumor cell regions and the tumor interstitial regions in the plurality of sub-images, and obtaining pathological features of the effective sub-images; determining axillary regions and primary lesion regions according to an image sequence of a detection object, and obtaining image features of the axillary regions and the primary lesion regions; mapping the pathological features and the image features to a unified semantic space through a semantic alignment model to obtain semantic features, and fusing the semantic features through an attention model to obtain first multi-modal features; extracting features from gene data of the detection object to obtain genomics features; and predicting a lymph node metastasis condition according to the first multi-modal features, the genomics features, the tumor omics features and the interstitial omics features.

This invention relates to the fields of tumor biology and cell engineering, specifically disclosing the construction and application of a human prostate cancer cell line. The human prostate cancer cell line is derived from primary prostate cancer tissue, expresses prostate-specific antigen and α-methylacyl-CoA racemic enzyme, and has a 46,XY karyotype. The construction method employs an innovative strategy of co-culturing fully digested cell suspensions with incompletely digested tissue blocks, significantly improving the success rate of establishing the cell line from the primary lesion. This cell line can stably proliferate in conventional culture media supplemented with testosterone and has been successfully applied to subcutaneous tumorigenesis models in immunodeficient mice and in vivo migration models in zebrafish. The primary lesion-derived prostate cancer cell line provided by this invention offers a highly relevant, stable, and reliable new experimental tool for studying the biological characteristics of primary prostate cancer, drug screening, and personalized treatment.

The application establishes a probe sequence combination, a kit, an in vitro non-diagnostic detection system and a use thereof for preparing a diagnostic product based on a head and neck primary unknown metastatic squamous carcinoma primary focus tracing based on targeted DNA methylation sequencing. The position of the primary focus is determined by detecting the methylation modification state of the relevant sequence sites of the head and neck metastatic cancer tissue DNA. The primary tracing can be subdivided into the head and neck subclinical anatomical subareas: tongue, tongue root, oropharynx, hypopharynx, larynx, tonsil, gum and floor of mouth, etc. The tracing accuracy is high, the defects of the prior art such as high cost, time-consuming and low patient acceptance are overcome, and the application has the advantages of high cost performance, fast diagnosis speed, high patient acceptance and excellent clinical popularization prospect.

The present application relates to the technical field of diagnosis and risk assessment of thyroid papillary carcinoma, and particularly relates to a composition, application and system for preoperative diagnosis and / or risk assessment of thyroid papillary carcinoma. The composition for preoperative diagnosis and / or risk assessment of thyroid papillary carcinoma comprises reagents for measuring the expression level of CLDN16 protein and / or the mRNA expression level of the gene encoding CLDN16 protein. Through the composition, the mRNA expression level of CLDN16 in thyroid tissue can be detected preoperatively, which is used for accurate diagnosis of the primary lesion and metastatic lesion of thyroid papillary carcinoma, and simultaneously, the disease risk of thyroid papillary carcinoma is accurately predicted to guide clinical decision-making.