208 results about "Cancer detection" patented technology

Methods for non-invasive cancer detection use a combination of genome-wide mutation and fragmentation features of cfDNA that facilitate cancer screening.

The invention discloses an electrical impedance cancer detection method based on a self-attention mechanism. The method comprises the following steps: S1, acquiring an electrical impedance signal and sending the electrical impedance signal into an input layer for preprocessing; s2, the preprocessed signal is divided into two branches, the first branch is input into a frequency domain network to extract frequency domain features of the signal, and the second branch is input into a time domain model to extract high-dimensional features of the signal; s3, inputting the frequency domain features extracted by the frequency domain network and the high-dimensional features extracted from the time domain model into a feature fusion layer for fusion; and S4, processing the fused features by using a self-attention mechanism, finally transmitting the processed features to a full connection layer and a classification output layer for cancer risk prediction, and outputting a detection result. According to the method, the frequency domain features and the high-dimensional features are extracted through the frequency domain network and the time domain model respectively and then fused, the fused features are enhanced by using a self-attention mechanism, and the accuracy and reliability of cancer detection are improved.

The invention discloses an electrical impedance cancer detection method based on frequency domain information enhancement. The method comprises the following steps: S1, acquiring an electrical impedance signal in the body of a patient through an electrical impedance imaging technology; s2, converting the signal into frequency domain representation by using discrete cosine transform, and performing frequency domain enhancement; s3, inputting the features subjected to frequency domain enhancement into the model, and processing the features subjected to frequency domain enhancement in combination with frequency domain information and a global dependency relationship to obtain a global feature matrix; and S4, inputting the global feature matrix into a classifier for cancer detection, and giving a corresponding detection result. According to the method, the time domain signals are subjected to frequency domain conversion, the features subjected to frequency domain enhancement are input into the network model, the global matrix features are obtained by combining the frequency domain information and the global dependency relationship, the global matrix features are input into the classifier for cancer detection, the robustness of the frequency domain data is improved, and the application effect of the electrical impedance signals in cancer detection is enhanced.

The invention belongs to the technical field of immunochemistry, and particularly relates to an anti-PR recombinant rabbit monoclonal antibody and application thereof.The anti-PR recombinant rabbit monoclonal antibody comprises a heavy chain variable region and a light chain variable region, and the amino acid sequence of the heavy chain variable region is shown as SEQ ID NO: 4; the amino acid sequence of the light chain variable region is as shown in SEQ ID NO: 5. The invention also relates to a nucleotide sequence for coding the anti-PR recombinant rabbit monoclonal antibody, a recombinant plasmid or an expression vector, a preparation method, application of the anti-PR recombinant rabbit monoclonal antibody in a PR protein detection method or device, and the like. The PR recombinant rabbit monoclonal antibody disclosed by the invention has the characteristics of good specificity, strong positive signal and the like, so that scoring is easier in IHC staining, and cancer detection and distinguishing are more accurate.

In implementations described herein, sequence representations are identified having at least at threshold likelihood of corresponding to a nucleic acid molecule produced by a subject in which a tumor-related biological condition is present. The identified sequence representations can be provided to a machine learning classification model to determine an indication of the tumor-related biological condition being present in subjects.

The invention provides a self-supervised cancer detection system based on subspace clustering. The system comprises a model training module, an image processing module and a result output module. According to the invention, a classification model for cancer discrimination is established and is applied to intelligent diagnosis of clinical pathology; when a to-be-detected pathological image is received, preprocessing operation is firstly carried out, and the preprocessing operation aims at extracting key features in the pathological image so as to better carry out cancer discrimination; inputting the preprocessed pathological image into a trained cancer discrimination classification model for discrimination to obtain a prediction result of the to-be-detected pathological image; the prediction result is displayed on a diagnosis and treatment interface of a pathologist, meanwhile, a diagnosis report is generated, and the pathologist makes more accurate cancer diagnosis and treatment decisions according to the prediction result and the diagnosis report, so that the accuracy and efficiency of pathological diagnosis are improved; therefore, a more accurate cancer judgment result is provided, and doctors are assisted in diagnosis and treatment decision making.

A method for preparing a dual-mode immune structure based on a surface-enhanced Raman substrate and an up-conversion luminescent probe, and an application thereof. The dual-mode immune structure based on a surface-enhanced Raman substrate and an up-conversion luminescent probe comprises a copper / black phosphorus / silver nanoflower composite immunosubstrate and a NaGdF4:Yb3+ / Er3+ up-conversion nanoparticle immunoprobe, wherein the copper / black phosphorus / silver nanoflower composite immunosubstrate is prepared using an electrochemical reduction method, and the NaGdF4:Yb3+ / Er3+ up-conversion nanoparticle immunoprobe is prepared using a hydrothermal method. During the application to cancer detection, assembling an immunosubstrate and an immunoprobe realizes the combination of up-conversion luminescence and surface-enhanced Raman spectroscopy technology for realizing dual-mode detection of cancer biomarkers. Thus, simple operation and high detection sensitivity are achieved, thereby facilitating efficient and precise clinical screening and identification of cancer biomarkers.

The present invention discloses a method for detecting cancer by bioelectrical impedance based on deep learning, comprising the following steps: S1: obtaining the electrical signal data of benign and malignant samples of cancer tissues by using an electrical impedance imaging system and combining it with physiological data; S2: extracting global features of the electrical signals by using a matrix decomposition module; S3: taking the result of global feature extraction and the original electrical signal data together as new inputs and further extracting signal features by using data dependency; S4: integrating the extracted feature maps into a final classification result. The present invention uses an electrical impedance imaging system to obtain the electrical signal data of cancer tissues, performs global feature extraction through a matrix decomposition module, and at the same time takes the result of global feature extraction and the original electrical signal data together as new inputs, and further extracts signal features by using data dependency, which can improve the utilization rate of data and further enhance the accuracy and reliability of cancer detection.

The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for diagnosis of cancer.

Images are produced having pixel values based on molecular characteristics and genomic position data corresponding to nucleic acid molecules derived from a sample. The images can be provided as input to a convolutional neural network that analyzes the images to determine a tumor indication related to a tumor being present in one or more subjects.

The invention discloses a morphological prediction method for tissue cells and blood cells of a fluorescence in-situ hybridization image, relates to the field of data image processing, and solves the common problems of fuzzy cell boundary, heterogeneity cell morphological difference, high noise background, insufficient data labeling, low efficiency, subjectivity and the like in the existing fluorescence in-situ hybridization image analysis. The method comprises the following steps: acquiring tissue cells and blood cells of a fluorescence in-situ hybridization image, and performing multi-scale image enhancement on the tissue cells and the blood cells; extracting local maximum values and local minimum values of tissue cells and blood cells by adopting a local extreme value feature extraction method; identifying the morphological characteristics of the edge of the cell nucleus for confirming the cell nucleus morphology of the convex defect of the cell nucleus; and based on the morphological characteristics of the identified cell nucleus edge and the morphological characteristics of the cell nucleus with the convex defect, finishing the prediction of the final morphology of the cell nucleus. The invention is also applicable to the fields of biology and cancer detection.

The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Biomarkers can be assessed for a variety of uses, including screening, detection, diagnosis, prognosis, risk prediction, disease progression, recurrence, selection of treatment, therapy response, to evaluate a subject's health status, whether the subject presents with no evidence of disease, or a benign or malignant condition such as cancer. Compositions (antibodies, polypeptide and polynucleotide markers) and methods are provided herein, which find application in the early detection of cancer, in the early detection of disease relapse and in monitoring therapy response.

The invention relates to the technical field of biomedical detection, in particular to a urinary tract epithelium cancer detection marker composition based on ONECUT2 and DMRTA2 gene methylation, which comprises a CpG island region of an ONECUT2 gene, a CpG island region of a DMRTA2 gene and a kit for detecting urinary tract epithelium cancer. The CpG island region of the ONECUT2 gene is located in chr18: 57441548-57441674, and the CpG island region of the ONECUT2 gene is located in The CpG island region of the DMRTA2 gene is located in chr1: 50418714-50418823, and the CpG island region of the DMRTA2 gene is located in chr1: The kit for detecting urothelial carcinoma comprises a first primer pair and a first probe which are used for specifically detecting the methylation state of the CpG island region of the ONECUT2 gene in the claim 1, and a second primer pair and a second probe which are used for specifically detecting the methylation state of the CpG island region of the DMRTA2 gene. The urinary tract epithelium cancer detection marker composition based on ONECUT2 and DMRTA2 gene methylation has high sensitivity and high specificity, the sensitivity and the specificity are both 90% or above, early-stage and low-grade urinary tract epithelium cancer can be effectively detected, comprehensive coverage is achieved, and the urinary tract epithelium cancer detection marker composition is not only suitable for bladder cancer, but also suitable for upper urinary tract epithelium cancer such as renal pelvis cancer and ureter cancer.

The present invention discloses a method for cancer pre-screening of a subject. Genetic risks associated with genetic characteristics are determined by sequencing and analyzing cell free DNA ("cfDNA") fragments present in a blood sample of the subject. The clinical risk is determined based on factors such as age, gender, and race. In some cases, clinical factors specific to certain cancers, such as smoking conditions, are combined. By incorporating clinical risks into genomic risk analysis, improved lung cancer pre-screening results are provided, enabling the same number of positive cancer detections to be achieved using a small number of LDCT lung cancer screening times.

The present invention relates to an automatic real-time label-free holography-activated sorting of the cell's technique. The technique provides high-discriminative power on the level of the individual cell. The technique includes rapid automated cell processing during cell visualization and flow, with high discriminative power on the level of the individual cell. The technique may be useful in detection of cancer and to identify different stages of oncogenesis.

This disclosure relates to a technique for identifying tumor-specific mutations through integrated analysis of next-generation sequencing data using machine learning models. In certain embodiments, a computer implementation method is provided, comprising: generating sequence reads from one or more samples collected from the same patient; generating variant calling files by analyzing the sequence reads corresponding to each of the one or more samples; generating a list of candidate somatic variants by comparing the variant calling files; generating a score for each of the candidate somatic variants in the list of candidate somatic variants using a classification machine learning model, wherein the score is generated based on a plurality of classifications generated by the classification machine learning model; determining the ctDNA status for the patient based on the score, wherein the ctDNA status is either positive or negative; and generating a report providing the ctDNA status for the patient.

The present invention addresses the problem of providing a cancer inspection technology in which an olfactory receptor is used. The problem is solved by a cancer test agent containing an insect olfactory receptor protein B' which demonstrates reactivity to multiple types of cancer with the same reactivity direction.

The invention discloses a bioelectrical impedance cancer detection method based on deep learning, and the method comprises the following steps: S1, obtaining electrical signal data of a benign sample and a malignant sample of a cancer tissue through an electrical impedance imaging system, and combining the electrical signal data with physiological data; s2, performing global feature extraction on the electric signal by using a matrix decomposition module; s3, taking a global feature extraction result and original electric signal data as new input, and further extracting signal features by using a data dependency relationship; and S4, mapping and integrating the extracted features into a final classification result. The electrical signal data of the cancer tissue is acquired by using the electrical impedance imaging system, global feature extraction is performed through the matrix decomposition module, the result of global feature extraction and the original electrical signal data are used as new input, and the signal features are further extracted by using the data dependency relationship, so that the utilization rate of the data can be improved; and the accuracy and reliability of cancer detection are further improved.

Disclosed herein are methods of treating an individual at risk for incidence or recurrence of cancer in need thereof. Methods can include the steps of identifying an individual at risk for incidence or recurrence of cancer based on a risk stratification parameter; analyzing a biological sample from using a multi-cancer detection (MCD) test to yield an MCD test result; and based on the MCD test result and optionally the risk stratification parameter, administering a neoantigen immunogenic composition to the individual in need thereof. Methods can include the steps of sequencing and analyzing of the biological sample from the individual or a new biological sample from the individual to identify neoantigens to be included in the neoantigen immunogenic composition; and analyzing a second biological sample from the individual at risk for incidence or recurrence of cancer using a second multi-cancer detection (MCD) test to determine efficacy of the neoantigen immunogenic composition.

The invention relates to a detection marker and a detection device for identifying primary and intrawall metastasis of esophageal multifocal squamous carcinoma, and belongs to the technical field of cancer detection markers. According to the invention, a detection site set composed of 20 genes is constructed, and the gene diagnosis purpose of distinguishing primary and intrawall metastasis of esophageal multi-lesion squamous cell carcinoma is realized by analyzing the difference of mutation types on lesions.

Described herein are methods and compositions related to methylation-based determination and characterization of subjects afflicted with various stages of cancer, including lung cancer and non-small cell lung cancer (NSCLC). Osimertinib is one of the most common precision therapies in non-small cell lung cancer but nearly all patients eventually progress due to somatic mutations and cellular plasticity that bypass the treatment. Defining the cellular plasticity state with epigenetics will help physicians know when to switch therapies.

The invention provides an application of a perovskite nanocrystalline probe in preparation of a cancer detection reagent. The perovskite nanocrystalline probe is formed by a biological material targeting a Survivin gene and a water-soluble perovskite nanocrystalline through electrostatic interaction or covalent coupling. The water-soluble perovskite nanocrystal is formed by in-situ coating and packaging of perovskite quantum dots and a polymer packaging material. The high-molecular packaging material comprises a lipidosome for modifying a functional group; the liposome is selected from one or more than two of dipalmitoyl phosphatidylcholine, dipalmitoyl phosphatidylethanolamine, distearoyl phosphatidylcholine and distearoyl phosphatidylethanolamine; the functional group is selected from carboxyl, amino and sulfydryl; and the perovskite quantum dots are selected from CsPbBr3. The universal perovskite nanocrystalline probe provided by the invention can be used for high-sensitivity rapid detection of various cancer tissues or cells.

The invention provides methods for detecting single nucleotide variants in breast cancer, bladder cancer, or colorectal cancer. Additional methods and compositions, such as reaction mixtures and solid supports comprising clonal populations of nucleic acids, are provided. For example, provided here is a method for monitoring and detection of early relapse or metastasis of breast cancer, bladder cancer, or colorectal cancer, comprising generating a set of amplicons by performing a multiplex amplification reaction on nucleic acids isolated from a sample of blood or urine or a fraction thereof from a patient who has been treated for a breast cancer, bladder cancer, or colorectal cancer, wherein each amplicon of the set of amplicons spans at least one single nucleotide variant locus of a set of patient-specific single nucleotide variant loci associated with the breast cancer, bladder cancer, or colorectal cancer; and determining the sequence of at least a segment of each amplicon of the set of amplicons that comprises a patient-specific single nucleotide variant locus, wherein detection of one or more patient-specific single nucleotide variants is indicative of early relapse or metastasis of breast cancer, bladder cancer, or colorectal cancer.

The present application relates to the technical field of cancer detection, in particular to a kind of tumor imaging guided photodynamic therapy reagent.The reagent is named ADS254BE / H2TPP NPs, which is composed of hydrophobic conjugated polymer ADS254BE and tetraphenylporphyrin H2TPP doped polymer microspheres, and the surface of the polymer microspheres also has PSMA modification layer.The reagent is prepared by uniformly mixing ADS254BE, H2TPP and PSMA solution, ultrasonic treatment, room temperature standing, and then evaporating solvent in the solution.The photodynamic diagnosis and treatment reagent provided by the present application shows bright red emission centered at 650nm, relatively large stoke shift, higher singlet oxygen generation rate, and good light stability and biocompatibility.The present application provides a feasible strategy for constructing new optical diagnosis and treatment integrated reagent, and is expected to promote the development of imaging guided photodynamic therapy.

The application finds that SDF4 in a body fluid sample can become a marker for detecting cancer patients with good sensitivity and specificity. By using a reagent for specifically detecting SDF4, gastric cancer, breast cancer, colorectal cancer, pancreatic cancer, esophageal cancer, liver cancer, liposarcoma, bladder cancer, brain tumor, head and neck cancer, gallbladder cancer, ovarian cancer, tongue cancer, or uterine cancer can be detected. The measurement of SDF4 in a body fluid sample is a detection method capable of detecting various cancers by a single test, and can be used for large-scale screening of cancers. In addition, early-stage cancers can be detected with good sensitivity and specificity, so that cancers can be found and treated in an early stage.

The present invention is directed to methods for detecting a colon cancer, methods for determining whether a colon cancer is stable or progressive, methods for determining a risk for disease relapse, and methods for determining a response by a subject having a colon cancer to a therapy.

A method for determining whether a subject is at risk for having a progressing or high-grade pre-invasive lesion, nodule or small mass, or having a solid malignant tumour is described the method comprising: (i) determining a ratio of activated and / or exhausted T cells:naive and / or resting T cells in a sample of blood obtained from the subject, wherein the determining comprises analysing T cells using cytometry to detect the presence or absence of a panel of biomarkers comprising Ki67 and CD39, or (ii) determining a ratio of activated and / or exhausted T cells:T cells which are not activated and / or exhausted T cells in a sample of blood obtained from the subject, wherein the determining comprises analysing T cells using cytometry to detect the presence or absence of a panel of biomarkers comprising Ki67 and CD39, (iii) determining a proportion of activated and / or exhausted T cells as a percentage of T cells in a sample of blood obtained from the subject, wherein the determining comprises analysing T cells using cytometry to detect the presence of a panel of biomarkers comprising Ki67 and CD39, and / or (iv) determining a proportion of activated and / or exhausted T cells as a percentage of T cells in a sample of blood obtained from the subject, wherein the T cells are CD4 T cells, and wherein the determining comprises analysing T cells using cytometry to detect the presence of a panel of biomarkers comprising FoxP3.

Disclosed is a method for diagnosing a cancer health status, or a change in cancer health status, or for diagnosing a risk of cancer change or presence in a patient, comprising determining in a plasma sample from the patient one or more biomarker values corresponding to a complex structure containing alpha-1 antitrypsin (A1AT), and based on the biomarker values, determining the patient as suffering or not suffering from cancer, or having or not having a change in cancer health status, or having or not having a risk of cancer, wherein the cancer is selected from the group consisting of hepatocellular carcinoma (HCC), ovarian cancer (OC), and breast cancer (BC).

The present application relates to the medical technical field, especially to the application of FYB1 gene as a marker in preparation of gastric cancer diagnosis or prognosis judging reagent. The present application research finds that FYB1 gene is highly expressed in gastric cancer tissue and negatively correlated with the prognosis of patients, and can be used as a gastric cancer marker for gastric cancer detection and efficacy evaluation. In addition, FYB1 is positively correlated with the expression of Treg cell marker FOXP3 and co-localized in tissues, suggesting that FYB1 may promote gastric cancer tumor immune escape; the expression of FYB1 gene can significantly inhibit the proliferation, migration and invasion ability of gastric cancer cells, and inhibit tumor growth, indicating that FYB1 gene can be used as a gastric cancer treatment target and play an important role in the treatment of gastric cancer patients.