318 results about "Cancer detection" patented technology

There is described herein, a method of capturing cell-free methylated DNA from a sample having less than 100 mg of cell-free DNA, comprising the steps of: subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated; denaturing the sample; and capturing cell-free methylated DNA using a binder selective for methylated polynucleotides.

This is described herein, a method of capturing cell-free methylated DNA from a sample having less than 100 mg of cell-free DNA, comprising the steps of: subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated; denaturing the sample; and capturing cell-free methylated DNA using a binder selective for methylated polynucleotides.

The invention provides a gastric cancer intestinal microbial marker, application thereof and a method for constructing a gastric cancer detection model, and belongs to the technical field of biomedicine. The method comprises the following steps: extracting biomarkers related to gastric cancer pathogenesis and progress from metagenome data, constructing a random forest model by using a random forest algorithm, and training and testing the constructed random forest model by using a training set and a test set to obtain a gastric cancer detection model. According to the gastric cancer detection model constructed based on the gastric cancer intestinal microbial marker, the specificity is 97.2964%, the sensitivity is 94%, the accuracy is 93.40649%, the model efficiency and conformity are high, and the condition of gastric cancer can be accurately predicted and evaluated. Based on the gastric cancer intestinal microbiological marker and the gastric cancer detection model, the disease condition of a gastric cancer patient can be accurately predicted and evaluated, a doctor can make a more effective treatment scheme, the survival rate and life quality of the patient are improved, and the treatment effect is improved.

The present invention provides a method for screening for olfactory receptor proteins or combinations thereof that are useful in cancer testing. [Solution] A method for screening olfactory receptor proteins or combinations thereof, comprising testing the reactivity of a test olfactory receptor protein to cancer, and selecting at least one selected from the group consisting of: (1) a combination of olfactory receptor protein A and olfactory receptor protein C, (2) a combination of olfactory receptor protein A' and olfactory receptor protein B, and (3) olfactory receptor protein B'.

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.

Provided herein are methods and materials for detecting and / or treating subject (e.g. a human) having cancer. In some embodiments, methods and materials for identifying a subject as having cancer (e.g., a localized cancer) are provided in which the presence of member(s) of two or more classes of biomarkers are detected. In some embodiments, methods and materials for identifying a subject as having cancer (e.g. a localized cancer) are provided in which the presence of member(s) of at least one class of biomarkers and the presence of aneuploidy are detected. In some embodiments, methods described herein provide increased sensitivity and / or specificity in the detection of cancer in a subject (e.g. a human).

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.

The utility model belongs to the technical field of kits, in particular to a kit for gastric cancer detection, which comprises a kit body, a base fixedly mounted in the kit body, a fixing groove arranged in the base and a test tube arranged on the fixing groove, and is characterized in that a protective pad is arranged in the fixing groove, a sealing cover is arranged at the top of the kit body, and a sealing ring is arranged at the bottom of the sealing cover; a sealing groove is formed in the box body, a sealing plate is arranged at the bottom of the sealing cover, a lock catch is fixedly installed on one side of the box body, and a transmission mechanism is arranged on the sealing cover. The turntable, the driving wheel, the driven wheel, the connecting rod, the first gear, the second gear and the stirring mechanism are matched for use, the driving wheel and the driven wheel can be driven to rotate by rotating the turntable, and the first gear and the second gear can be conveniently driven to rotate by rotating the connecting rod; the operation and the use are relatively convenient, and the working efficiency can be improved, so that the structural practicability and the functionality of the device can be improved.

The invention provides a cancer detection method, device and equipment based on a medical image, a medium and a product, and relates to the technical field of image recognition. The method comprises the following steps: inputting a medical image into an image classification model to obtain an image classification result output by the image classification model; the image classification result is used for representing an identification result of performing cancer identification on the medical image; inputting the medical image into a semantic segmentation model to obtain a semantic segmentation result output by the semantic segmentation model; the semantic segmentation result is used for indicating whether a focus area exists in the medical image or not; based on the semantic segmentation result and the image classification result, determining whether an organ part corresponding to the medical image needs to be detected; and outputting detection prompt information under the condition that the organ part corresponding to the medical image needs to be detected. According to the invention, high-efficiency, high-sensitivity and high-accuracy cancer detection can be realized.

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.

The present invention relates to plasma metabolite markers for gastric cancer detection and their applications. Based on the metabolomics method, the present invention screened and obtained 15 metabolite markers that can distinguish between health, gastric non-cancer diseases and gastric cancer by mass spectrometry analysis of plasma samples of gastric cancer patients, gastric non-cancerous diseases and healthy controls. Multivariate multi-classification ROC curve analysis showed that the AUC value reached 0.852, and the 15 differential metabolite markers had good diagnostic efficacy in distinguishing health, gastric non-cancerous diseases and gastric cancer. Further selecting 11, 7 or 4 combinations from the 15 markers also had the same good diagnostic effect. And the above combination was further confirmed in the validation group. The metabolite combination provided by the present invention can accurately predict whether it is a healthy group, a patient with a gastric non-cancerous disease, or a patient with gastric cancer, which will help in the early prevention, treatment and prognosis of gastric cancer, etc., with good treatment effect and reduced treatment cost.

The present invention relates to methods of diagnosing cancer in a patient. Furthermore, the present invention relates to a method of monitoring the course of cancer in a patient. Furthermore, the present invention relates to a method of determining whether a patient is responsive to a therapeutic treatment of cancer. The invention also relates to a method of determining the risk of cancer recurrence in a patient. In addition, the present invention relates to a method of detecting minimal residual lesions in a patient with cancer. In addition, the invention relates to kits for carrying out these methods.

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.