73 results about "Tumor detection" patented technology

The invention provides a tumor recognition method based on multi-modal feature fusion, relates to the technical field of tumor recognition, and is used for solving the problems of missing multi-modal feature calibration, insufficient semantic association, poor generalization and insufficient clinical interpretability in the prior art. The method comprises the following steps: firstly, synchronously acquiring visual structure and electrical impedance bimodal data through an event-driven sensor array, and completing preprocessing through pulse coding, STDP rule optimization and quality verification; then, respectively extracting structured feature vectors of a tumor visual structure class and characteristic feature vectors of a numerical attribute class by a heterogeneous twin coding engine, and unifying dimensions and distribution through processes such as multi-scale dynamic perception and cross-modal calibration; then based on an attention mechanism, InfoNCE contrast loss and minority class weight gain, dynamic weighted fusion and semantic association enhancement are performed on the bimodal features, and a comprehensive feature vector is generated; and finally, through clinical logic adaptation and multi-center deviation correction, outputting a tumor benign and malignant identification result through a full-connection classifier, and synchronously generating a clinical interpretable report containing key features and weights. According to the method, the complementary advantages of bimodal information are effectively integrated, the problems of heterogeneous multi-center equipment, unbalanced samples and the like are solved, the tumor recognition accuracy and the early-stage tiny tumor detection rate are improved, the computing power consumption is reduced, edge medical equipment deployment is adapted, and the requirements for low misjudgment and traceability of clinical diagnosis are met.

The invention provides a MaskRcnn-based tumor detection method and system, and the method comprises the steps: inputting medical image data into a MaskRcnn network integrated with an edge perception module, generating an edge response graph through a Sobel operator, and enabling the edge response graph to serve as an additional channel injection feature graph, and enhancing the boundary representation; fusing multi-scale features through a feature pyramid network FPN, extracting candidate tumor area features through RoI Align, introducing non-local attention modeling global dependence in the area, extracting directional entropy, texture energy, uniformity and contrast in combination with a gray level co-occurrence matrix GLCM, and splicing to generate structure sensing features; performing classification, bounding box regression and mask segmentation based on the structure perception features, and constructing a joint loss function including classification, regression, mask cross entropy, edge alignment and structure consistency; the positioning capability of the model on the fuzzy tumor contour is effectively improved, the boundary positioning error is remarkably optimized, and the defect that over-segmentation or missing detection is likely to occur in a traditional model is overcome.

This invention relates to the field of data analysis technology, specifically to a cloud-based tumor detection data analysis and sharing system. The invention includes a cloud service platform and multiple local terminals. The local terminals communicate with the cloud service platform via a network and are deployed in various medical institutions. They digitize tumor detection samples to generate raw medical data and extract metadata. The cloud service platform includes a data receiving module for receiving raw data and metadata; a template management module for building and maintaining a feature template library, including a two-stream ensemble classifier; a data calibration module for calling standardized parameter sets from metadata and generating standardized data through a reversible neural network; a data analysis module for analyzing the standardized data; and a data sharing module for responding to requests to share data. The system solves the problem of heterogeneity in multi-center data, improves the model's generalization ability and robustness, and achieves efficient data sharing and accurate analysis.

The invention relates to a radionuclide labeled trimer probe 68Ga-TRAP-(FAPI) 3 targeting FAP and a preparation method thereof, Trap is used as a trivalent chelating platform, and three FAPI targeting units are covalently connected by clicking a chemical linking arm to form a trimer structure; the invention further relates to application of the 68Ga-TRAP-(FAPI) 3 probe in preparation of imaging agents or therapeutic drugs for diagnosing FAP positive diseases, experiments prove that the trimer probe is good in affinity and high in stability, and in cell experiments and animal experiments, the uptake rate of FAP positive cells is kept at a high level within 240 minutes; preclinical and preliminary clinical applications show that the tumor detection rate of the < 68 > Ga-TRAP-FAPI 3 in various cancer models is superior to that of < 18 > F-FDG, and compared with < 68 > Ga-FAPI-04, the < 68 > Ga-TRAP-FAPI 3 is clearer to display focuses (particularly tiny metastases), higher in uptake intensity and particularly outstanding in delayed imaging, and the < 68 > Ga-TRAP-FAPI 3 can be used for preparing a medicine for treating cancer. And a novel drug candidate with better performance is provided for precise diagnosis and staging of tumors and subsequent radionuclide targeted therapy.

The invention provides a microsatellite site stability detection method and an electronic device thereof. The microsatellite locus stability detection method comprises the following steps: S1) utilizing high-throughput targeted sequencing data of a sample to be detected to obtain feature data in a preset microsatellite locus set, the feature data at least comprising length variation of a short tandem repeat sequence and structural variation occurring at a microsatellite locus; according to the microsatellite stability detection method and device, the problem that in the prior art, a method for detecting microsatellite instability has limitation can be solved, and the microsatellite stability detection method and device are suitable for the field of tumor detection.

The invention provides a digestive tract tumor detection method and system, relates to the field of medical image detection, and solves the technical problem of low accuracy of tumor boundary detection and automatic positioning in a digestive tract tumor image acquisition process in the prior art. The method comprises the following steps: taking a pan-tumor region image as a training sample of a binary decision tree algorithm, constructing an objective function based on the training sample to obtain a first objective function value, and obtaining a depth threshold value and an optimal classification point of the training sample; optimizing the optimal classification point based on the candidate feature sound to obtain a second objective function value; an unknown digestive tract tumor image is accessed, image points of the digestive tract tumor image are marked through a trained binary decision tree algorithm and a regression forest algorithm, the position distribution probability of the image points is generated, a position distribution thermodynamic diagram is generated based on the position distribution probability, and a tumor core area is obtained through mean shift. And completing the separation of the tumor region and the non-tumor region. The application is used in the digestive tract tumor detection process.

The invention discloses a tumor detection device and method based on a diamond quantum sensor, and belongs to the technical field of biomedical detection. The device comprises an optical fiber laser, an optical fiber beam splitter, an optical fiber attenuator, an optical fiber circulator, an optical filter, two photoelectric detectors, a differential amplifier, an electronic filter, a computer, a signal generator, two microwave antennas and an optical fiber probe. The optical fiber probe comprises an optical fiber packaged in a plastic shell, a diamond quantum sensor which is fixed at the tail end of the optical fiber and comprises an NV color center, and a first microwave antenna integrated beside the optical fiber. The laser excites the diamond to generate fluorescence, meanwhile, the signal generator drives the two microwave antennas to apply a microwave field, and the dielectric property of the sample to be detected modulates the fluorescence intensity of the diamond by influencing the microwave field. Laser noise is eliminated through a differential amplification and filtering technology, and fluorescence signal change is analyzed by a computer, so that dielectric constant distribution of a sample is reconstructed, and non-invasive and high-sensitivity detection of tumors is realized.

The invention discloses a nano antibody specifically combined with CD176 protein and application thereof, the nano antibody sequentially comprises a CDR1 region, a CDR2 region and a CDR3 region, the amino acid sequence of the CDR1 region is shown as SEQ ID No.6, the amino acid sequence of the CDR2 region is shown as SEQ ID No.7, and the amino acid sequence of the CDR3 region is shown as SEQ ID No.8. The invention further discloses a preparation method of the nano antibody specifically combined with the CD176 protein and application of the nano antibody specifically combined with the CD176 protein. The invention discovers that the nano antibody with the three complementary determining regions can be combined with CD176 protein (EC50 is 8.617 nM) with high affinity, has the characteristics of small molecular weight, small immunogenicity, better solubility and stability and the like, can effectively detect the expression of CD176 or inhibit the activity of CD176, and has potential value in the aspects of tumor detection and treatment.

The application provides a fluorescence in situ hybridization image breakage and fusion collaborative determination method and device and medium, in order to solve the problems of FISH image breakage and fusion detection fragmentation, insufficient robustness and high misjudgment rate, after separating the cell image channel, the application calculates a binary threshold value based on an adaptive extreme value ratio, combines a connected domain median area filter to extract independent fluorescent points and an equivalent diameter, generates a cell nucleus mask based on fusion morphology and appearance characteristics, performs two-way collaborative determination, globally determines that heterochromatic fluorescent points with a distance less than a fusion threshold value are fused and physically removed, and then connects the remaining points in the same cell nucleus by using a global optimal pairing algorithm, and determines that the breakage threshold value based on the equivalent diameter is greater than the distance dynamically set, and finally outputs a comprehensive classification state. The application is used for precise determination of tumor FISH detection.

The invention discloses a tumor detection marker and application thereof in medicine preparation, and belongs to the technical field of biomedicine. Clinical sample qRT-PCR (quantitative real-time polymerase chain reaction) shows that ENST00000412203.1 is obviously up-regulated in breast cancer tissues, the area (AUC) under the ROC curve reaches 0.9822, and the diagnosis efficiency is excellent. In addition, the invention further finds that the expression of the polypeptide in lymph node metastasis tissues is obviously improved, and metastasis specificity is displayed. Meanwhile, the expression of ENST00000412203.1 is knocked down through siRNA, it is found that proliferation, migration and invasion of breast cancer cells are remarkably inhibited, and therefore the siRNA can be used for preparing targeted therapy drugs for breast cancer and has important clinical application value.

We disclose a composition, comprising a compound, comprising a melanin precursor that spontaneously synthesizes a melanin when in free form in vivo, and a peptide directly covalently bonded to or indirectly linked to the melanin precursor, wherein the direct covalent bond or indirect link of the peptide to the melanin precursor blocks spontaneous synthesis of the melanin in vivo in the absence of protease activity. We also disclose methods of tumor imaging, tumor detection, diagnosis, and treatment, including methods comprising administering, to a patient suffering from a tumor, the composition; and either surgically resecting the tumor or thermally ablating the tumor, wherein the surgical resection is guided at least in part by contrast imparted by, or the thermally ablating targets cells containing, the melanin, wherein the melanin is spontaneously synthesized in the tumor after administering the composition.

Provided herein is a method for detecting a tumour that can be applied to cell-free samples, to cell-free detect circulating tumour DNA. The method utilizes detection of adjacent methylation signals within a single sequencing read as the basic positive tumour signal, thereby decreasing false positives. The method comprises extracting DNA from a cell-free sample obtained from a subject, bisulphite converting the DNA, amplifying regions methylated in cancer (CpG islands, CpG shores, and / or CpG shelves), generating sequencing reads, and detecting tumour signals. To increase sensitivity, biased primers designed based on bisulphite converted methylated sequences can be used. Target methylated regions can be selected from a pre-validated set according to the specific aim of the test. Absolute number, proportion, and / or distribution of tumour signals may be used for tumour detection or classification. The method is also useful in, predicting, prognosticating, and / or monitoring response to treatment, tumour load, relapse, cancer development, or risk.

This invention belongs to the field of tumor detection and relates to a dynamic evaluation method for the efficacy of Bragg therapy driven by multi-cycle, multimodal peripheral blood data. The method includes the following steps: acquiring peripheral blood multimodal data from patients participating in Bragg therapy, and dividing the peripheral blood multimodal data into a training dataset and a validation dataset; constructing a Bragg therapy efficacy evaluation model using the training dataset, and validating the Bragg therapy efficacy evaluation model using the validation dataset; receiving peripheral blood multimodal data from patients participating in Bragg therapy, and calculating the evaluation results using the Bragg therapy efficacy evaluation model. This invention, by combining long short-term memory networks and graph neural networks, can effectively track the temporal changes of immune cell characteristics, thereby providing a more accurate basis for developing personalized treatment plans for Bragg therapy patients, improving treatment outcomes and survival rates.

This invention provides a tumor lesion region detection system and method based on an ultrasound patch assembly, belonging to the field of ultrasound medical imaging technology. The system includes an ultrasound patch assembly for attaching to the detection area and acquiring characteristic parameters such as left-right symmetry, curvature distribution, and edge perturbation; a preprocessing module that adaptively selects a bi-symmetric, five-point centering, or three-point centering scanning mode based on the characteristic parameters; a scanning control module that dynamically activates the phased array and sets scanning path parameters for ultrasound signal acquisition; a signal processing module that processes the signals to obtain ultrasound images; and an analysis module that segments the image and identifies lesions based on a pre-trained medical image segmentation model, completing tumor boundary delineation and structure extraction. This system can intelligently adjust the scanning mode for different detection areas, improving the accuracy and adaptability of tumor detection.

Disclosed is a compound having the following structure: TIFF2023537627000024.tif61169X is an anion (e.g., a biologically relevant anion such as chloride, iodide, etc.); Y is NH, NR 10 , or CR 11 R 12 where Z is a heteroatom (e.g., O, S, or Se), and R and R 1 are independently selected from methyl, ethyl, propyl (e.g., n-propyl, isopropyl), butyl (e.g., n-butyl, isobutyl, tert-butyl), and the like, and combinations thereof. In various examples, R and R 1 At least one of the atoms is not an oxygen atom (both cannot be oxygen atoms to form -NO2). 2 and R 3 R is independently selected from methyl, ethyl, propyl (e.g., n-propyl, isopropyl), butyl (e.g., n-butyl, isobutyl, tert-butyl), etc., and combinations thereof. 4 , R 5 , R 6 , R 7 , R 8 , R 9 , R 10 , R 11 , R 12 are independently selected from hydrogen, alkyl groups (e.g., methyl, ethyl, propyl (e.g., n-propyl, isopropyl), butyl (e.g., n-butyl, isobutyl, tert-butyl), etc.), and combinations thereof. Also disclosed are compositions and methods of using the compounds and compositions. The compounds or compositions can be used to visualize or identify tumors.

Described herein are methods and compositions related to using genomic and / or methylation-based TF from a genomic, epigenomic detection platform to predict clinical benefit to therapies, including through evaluation of real-world patients. Tumor detection, methylation-based tumor fraction, and methylation-based molecular response are prognostic and predictive of outcomes, Prolonged treatment of patients not deriving benefit risks increased toxicity, may reduce chance of success for targeted treatments, and may increase clonal hematopoietic burden, wherein methylation based detection largely eliminates noise in determining genomic molecular response (gMR), thereby demonstrating advantageous use of methylation based molecular response (mMR).

The application relates to a multifunctional contrast agent for capturing plasma cell free DNA and a preparation method and application thereof, the preparation method of the multifunctional contrast agent comprises the following steps: S1, activating a contrast agent surface carboxyl; S2, modifying a DNA binding protein on the contrast agent surface; and S3, sealing unreacted sites on the contrast agent surface. Compared with the prior art, the multifunctional contrast agent prepared by the application has good biocompatibility, not only has an imaging examination function, but also can be applied to capturing cell free DNA in circulating blood, and is further applied to tumor detection and tumor prognosis monitoring.

This invention provides a method, system, and device for constructing a 3D-printed liver model based on image data, comprising: acquiring CT and MRI image data of an individual patient; performing multimodal image segmentation on the CT and MRI image data to obtain structural segmentation images; fusing the structural segmentation images to obtain a liver and tumor anatomical structure model; performing three-dimensional reconstruction on the liver and tumor anatomical structure model to obtain a target three-dimensional visualization model; and 3D printing the target three-dimensional visualization model. This application integrates CT and MRI imaging modalities to obtain more comprehensive and accurate liver anatomical information. CT provides clear images of the liver parenchyma and blood vessels, while MRI provides more sensitive soft tissue contrast and tumor detection. The combination of the two can compensate for the shortcomings of a single imaging technology, improving the realism and information content of the model.

The invention provides a parotid gland tumor processing method and device based on multi-modal medical data, and can relate to the artificial intelligence technology, and the method comprises the steps: obtaining a parotid gland tumor detection image of a patient; obtaining parotid gland tumor related problems of the text mode of the related patient; inputting the parotid tumor detection image into an image description model to obtain description information of the parotid tumor detection image; and inputting the description information serving as prompt information and parotid gland tumor related questions into a large language model to obtain parotid gland tumor related answers. The image description model and the large language model are both single-mode models, the training data of the image description model and the training data of the large language model can be easily obtained through an open source community at present, and the manual annotation cost is low when the large language model is finely adjusted, so that a serial large model architecture formed by the two models can be applied to parotid tumor treatment scenes, and the method is suitable for popularization and application. Furthermore, parotid gland tumor related answers, such as parotid gland tumor diagnosis or treatment schemes, can be given based on the multi-modal medical data.

The invention discloses a kit for detecting circulating tumor DNA (deoxyribonucleic acid) of portal vein and branch vein blood of portal vein. The kit comprises: a nucleic acid extraction assembly comprising an optimized lysate and selectively bound magnetic beads; the library construction component comprises a terminal repair / tailing fusion enzyme and a Y-shaped linker with a molecular identifier; the targeted enrichment assembly comprises a specific primer group and a probe group and is used for enriching related gene targets of the abdominal organ tumors; and necessary reaction components. Through an optimized nucleic acid extraction method, an efficient library construction system and a precise targeted enrichment technology, high-sensitivity detection of low-abundance ctDNA in portal venous blood is achieved, and the lower detection limit reaches 0.1% of mutation allele frequency. The kit is particularly suitable for early diagnosis, curative effect monitoring and prognosis evaluation of abdominal organ tumors such as intestinal cancer, liver cancer and pancreatic cancer.

The application discloses a tumor detection device and method based on a diamond quantum sensor, and belongs to the technical field of biomedical detection. The device comprises a fiber laser, a fiber beam splitter, a fiber attenuator, a fiber ring, a filter, two photodetectors, a differential amplifier, an electronic filter, a computer, a signal generator, two microwave antennas and a fiber probe. The fiber probe comprises a fiber packaged in a plastic shell, a diamond quantum sensor containing NV color centers fixed at the end of the fiber, and a first microwave antenna integrated beside the fiber. The laser excites the diamond to generate fluorescence, and the signal generator drives the two microwave antennas to apply a microwave field. The dielectric properties of the sample to be measured will modulate the fluorescence intensity of the diamond by affecting the microwave field. The laser noise is eliminated through differential amplification and filtering technology, and the computer analyzes the fluorescence signal change to reconstruct the dielectric constant distribution of the sample, realizing non-invasive and high-sensitivity detection of tumors.

The invention relates to the technical field of tumor detection, in particular to a CRISPR-Cas12a system for one-step detection of a tumor marker APE1 based on an autocatalytic strand displacement reaction. According to the system, the trans-cleavage activity of Cas12a and the dynamic characteristic of a nucleic acid strand displacement reaction are coupled to form a self-sustaining positive feedback cycle. A template probe is constructed by designing a blocking chain containing an AP site and an activation chain, and crRNA invasion, Cas12a activation and feedback cutting of the blocking chain are realized under the triggering of APE1, so that a binding domain is continuously regenerated, multiple rounds of chain replacement and Cas12a reactivation are driven, and a self-circulation amplification detection platform is established. According to the technical scheme, the technical problem that in the prior art, a novel detection system which does not need exogenous amplification, does not depend on additional enzymes, has self-driven and positive feedback amplification capabilities and is used for accurate, rapid and on-site detection of APE1 is lacked can be solved, and the method has ideal application prospects.

Novel approaches for training medical foundation models are provided, in conjunction with novel techniques for fine-tuning models based on ordinally labeled data, in order to fully leverage expert knowledge and advances in AI technology in addressing a wide range of critically important clinical tasks such as, e.g., residual tumor detection. In addition, in order to ensure that the clinically actionable information obtained using the models of the disclosure is safe, reliable, and trustworthy, methods for generating interpretable visualizations via the trained and / or fine-tuned foundational models are also provided. The methods and systems of the disclosure may be used to rapidly, accurately, and verifiably identify tumor infiltration across a wide range of clinically significant settings.

A micropeptide HMMW of a new structure and an application thereof, and relates to the field of biomedical research and development is provided. The micropeptide HMMW is obtained by encoding human lncRNA, and a recombinant vector is constructed so that objective cells perform high expression on the micropeptide HMMW, which can inhibit proliferation and migration of multiple solid tumors including the head and neck cancer, thyroid cancer, lung cancer, esophageal squamous cell carcinoma, stomach cancer, breast cancer, kidney cancer, skin cancer and the like, and growth of tumors in the body. The micropeptide HMMW has potential value for new drug development, important tumor detection and treatment value.

Methods and compositions are provided for preparing a library of target nucleic acid sequences useful for evaluating genetic mutations for tumor biomarker profiling of samples.In particular, a target-specific primer panel is provided that allows selective amplification of tumor biomarker target sequences in a sample.In one aspect, the present invention relates to target-specific primers that are useful for selectively amplifying one or more target sequences associated with tumor biomarkers from two or more sample types.In some aspects, the amplified target sequences obtained using the disclosed methods and compositions can be used in various processes, including nucleic acid sequencing, and can be used to detect the presence of genetic variants of one or more target sequences associated with tumors.

The present application belongs to the technical field of tumor detection and cell separation, and particularly relates to a kit for separating and recovering after immunocapture of circulating tumor cells, a preparation method and application. The kit comprises an immunocapture module, a dissociation and recovery module, a cell protection module and elution and supplement factor solutions. The immunocapture module adopts magnetic microspheres modified by double antibody fusion proteins, can recognize multiple markers such as EpCAM and Vimentin, and realizes efficient capture of heterogeneous CTCs. The dissociation and recovery module uses a low-temperature buffer system and a cleavable cross-linking agent to release cells gently. The cell protection module maintains the integrity and activity of the cell membrane during the separation process. The method of the present application can ensure the capture efficiency while obtaining high-activity CTCs, and is suitable for single-cell sequencing, drug sensitivity analysis, organoid culture and patient-derived cell line construction and other preclinical research.

There is disclosed a method and system for detection of a tumor in a breast and / or for calculating breast fibroglandular density. The method includes obtaining a sequence of images of an exterior surface of a breast supported by a bra having optical elements thereon, during movements of the breast, obtaining a three-dimensional model of the breast, isolating the movements of the breast relative to a thoracic reference, processing the images to identify surfacic vibrations of the breast of the exterior surface, as a function of the isolated movements, analyzing the surfacic vibrations and deformation as a function of the three-dimensional model of the breast, defining a three- dimensional distribution of internal stiffness of the breast using the analyzed surfacic vibrations and deformation with elastography and locating a rigid mass in the breast using the three-dimensional distribution.

This invention relates to the field of image recognition technology, specifically to a tumor detection and recognition method and system based on MRI images. The method includes the following steps: screening candidate lesion regions based on a gradient orientation stability matrix; constructing a spatial projection displacement map; establishing a dynamic boundary framework by combining boundary variation rate; dividing into levels according to a sequence morphological consistency matrix; and performing differential reshaping to generate recognition results. This invention, by constructing a gradient orientation stability matrix and an anisotropy scoring mechanism, quantitatively evaluates the distribution of pixel vector angles; utilizes spatial projection displacement maps and boundary variation rate analysis to dynamically capture structural continuity features between slices; automatically eliminates artifact interference and constructs a high-fidelity dynamic boundary framework; divides into differential levels by combining a sequence morphological consistency matrix; and performs mean adjustment and edge reshaping for regions with different confidence levels. This improves the geometric accuracy and structural integrity of tumor lesion recognition under complex imaging conditions.

The invention belongs to the technical field of artificial intelligence and medical image analysis, and particularly relates to a full-slide image target detection method based on deep learning and a tumor detection system applying the method. The method comprises the following steps: preprocessing a full-slide image to obtain a patch-level image; constructing a single-stage target detection model by taking patch-level tumor images as input and taking classification and regression results of lesion tumors as output; lS convolution is introduced to simulate size seeing and focusing, so that the tumor detection efficiency is improved; the improved GD multi-scale fusion module is used for collecting and distributing cross-level information in the C2f multi-scale feature fusion network; and outputting a detection result of the diseased tumor in the patch-level tumor image. The lesion tumor can be automatically, efficiently and accurately detected from the full-slide image, and a contribution is made to disease screening.

A novel micropeptide, MP36, and its applications are provided. This MP36 micropeptide can be used to prepare reagents or drugs for the detection, prevention, or treatment of tumors, including one or more of lung cancer, breast cancer, colon cancer, glioma, bladder cancer, gastric cancer, head and neck cancer, and sarcoma. In tumors, the MP36 micropeptide can inhibit tumor cell growth and / or metastasis by specifically inhibiting MP36 expression via small interfering RNA; or by overexpressing MP36 to inhibit tumor cell growth and / or metastasis; or by conjugating a membrane-penetrating peptide to inhibit tumor growth and / or metastasis. It has significant value for new drug development and tumor detection.