97 results about "Cancer detection" patented technology

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.

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 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 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.

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 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.

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.

The present invention describes a comprehensive system and method for the simultaneous early detection of multiple cancers in a single analysis. The system involves a Liquid Chromatography-Mass Spectrometry (LC-MS) device coupled with processors and AI / ML algorithms. The LC-MS device analyses metabolite ions from dried extracts of biological fluid samples, aligning and normalizing the data while minimizing errors. Quality control processes, including a neural network model and critical ion monitoring, ensure accurate detection. The system employs AI / ML processes to create two models: the Cancer Detection AI (CDAI) Model for identifying cancerous samples, and the Tissue of Origin Identification (TOOAI) Model for distinguishing specific cancer types. The models are applied to test samples, providing scores based on tissue of origin probabilities. The invention aims to revolutionize early cancer detection through advanced analytical and machine learning techniques.

The present disclosure provides compositions and methods for the detection and treatment of cancer. Specifically, the compositions of the present technology include novel compounds that may be complexed with a radioisotope. Also disclosed herein are methods of the using the DOTA-haptens of the present technology in diagnostic imaging as well as pretargeted radioimmunotherapy.

The present invention describes a comprehensive system and method for the simultaneous early detection of multiple cancers in a single analysis. The system involves a liquid chromatography-mass spectrometry (LC-MS) instrument coupled with a processor and AI / ML algorithms. The LC-MS instrument analyzes metabolite ions from dried extracts of biological fluid samples and aligns and normalizes the data while minimizing errors. Quality control processes, including neural network models and critical ion monitoring, ensure accurate detection. The system employs an AI / ML process to create two models: a Cancer Detection AI (CDAI) model for identifying cancer samples and a Tissue of Origin Identification (TOOAI) model for distinguishing specific cancer types. These models are applied to test samples and provide a score based on tissue of origin probability. The present invention aims to revolutionize early cancer detection through advanced analytical and machine learning techniques.

A method for detecting cancer or predicting a cancer prognosis is provided. The method involves the detection of Tetraspanin 10 in a sample of bodily fluid from a subject.

The invention relates to a computer-implemented method for detecting oesophageal squamous cell dysplasia (OSCD) or oesophageal squamous cell cancer (OSCC) in a cell sample from a subject. This invention also relates to a computer-implemented method for generating a trained model to detect OSCD or OSCC. This invention also relates to a computer-readable storage medium or a computer program comprising instructions which when executed by a computer, are capable of causing the computer to perform the method. This invention also relates to a kit comprising the computer program or computer-readable storage medium and a non-endoscopic cell collection device. The invention also relates to an apparatus for performing the method. The invention further relates to the use of genome wide instability scores (GWIS) as a diagnostic marker for cancer, wherein GWIS is a measure of the CNV across a plurality of autosomal arms in a cell sample from a subject.

A computer-implemented method for predicting whether a subject has a cancer, comprising providing multimodal data comprising at least two feature types, said feature types comprising a metric of methy

The present disclosure relates to an application of gene markers in multi-cancer early detection, a method for constructing an early detection model, and a detection device. In the present disclosure, low-coverage whole-genome sequencing is conducted on cell-free DNAs (cfDNAs) from a plasma sample, and according to high-throughput sequencing results, six differential features of the cfDNA fragments are analyzed for each cancer. Then the training and modeling are conducted with a convolutional neural network to allow the early detection of a plurality of cancers at a low sequencing depth. Then the training and modeling are conducted with a generalized linear model (GLM), a gradient boosting machine, a random forest model, a deep learning model, and an extreme gradient boosting model, and staking is conducted with a GLM to construct a multi-feature algorithm, to allow the tissue-of-origin-based detection of cancers.

The present application provides a marker combination for early detection of digestive system cancer, tissue localization, diagnosis, prognosis detection and identification of benign and malignant, the marker combination is selected from 1656 chromosome regions in table 1. A multi-cancer early screening and localization technology GutSeer for five high mortality digestive system cancers is also provided, which proves that using a relatively small second-generation sequencing panel can realize accurate cancer detection and organ tissue localization by using multiple dimensional characteristics including methylation, copy number variation and terminal motif.

The application relates to the technical field of nanomaterials, in particular to an EpCAM aptamer modified nanomaterial and a preparation method and application thereof, wherein the nanomaterial takes carboxylated mesoporous silica nanoparticles as a carrier, and an EpCAM specific DNA aptamer is covalently coupled to the surface of the nanomaterial; the sequence of the EpCAM specific DNA aptamer is shown as SEQ ID No. 1. The nanomaterial can specifically capture tumor-derived exosomes (EVs) in blood, and can realize sensitive detection of EVs in the 7-day (early stage) and 21-day (late stage) stages of an HT29 colon cancer model and the 7-day stage of an A549 / H460 lung cancer model, thereby providing a minimally invasive liquid biopsy new tool for early diagnosis of colon cancer and lung cancer, and solving the inherent limitation problems of sensitivity and specificity of existing diagnosis means in cancer detection.

The invention belongs to the technical field of immunochemistry, and particularly relates to an anti-CD20 recombinant rabbit monoclonal antibody and application thereof.The anti-CD20 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-CD20 recombinant rabbit monoclonal antibody, a recombinant plasmid or an expression vector, a preparation method, an application of the anti-CD20 recombinant rabbit monoclonal antibody in a CD20 protein detection method or device, and the like. The problems that the binding specificity and sensitivity of the current commercially available CD20 antibody and CD20 protein molecules need to be improved, the identification degree in IHC dyeing is poor, and cancer detection and distinguishing are not facilitated are solved.

A method for performing in vivo imaging or detection of cancer in a subject in need thereof, the method comprising: - administering to the subject an agent for binding to CAIX expressed by the cancer, wherein the agent comprises a detectable moiety for enabling in vivo detection of the agent in the subject; and - detecting the agent in the subject, wherein the cancer is not renal cell carcinoma.

Provided herein are methods and systems for cancer detection and monitoring. The methods may include minimal residual disease analysis. The methods may include using nucleic acids from a urine sample or a urine cell pellet sample. The methods may include assaying nucleic acids in urine to detect a set of biomarkers from the sample. The methods may include monitoring a subject after transurethral bladder tumor resection.

The present invention provides a computer-implemented method for predicting whether a subject has a cancer. The method comprises providing multimodal data comprising at least two feature types, said feature types comprising a metric of methylation of each of at least 10 differentially methylated blocks of homogeneous methylation ("DMBs") in a cell-free DNA (cfDNA)-containing sample obtained from the subject and a metric of somatic copy number alterations of at least 10 non-overlapping bins of the genome of the subject in a cell-free DNA (cfDNA)-containing sample obtained from the subject; inputting the multimodal data into a multimodal machine learning classifier that has been trained on a labelled training data set comprising corresponding multimodal data for each of: (i) a plurality of subjects known to have had the cancer the time of sample collection ("cases"); and (ii) a plurality of subjects known to not have the cancer at the time of sample collection ("controls"); and causing the multimodal machine learning classifier to classify the subject into the cases class and thereby as having cancer or the controls class and thereby not having cancer based on at least the inputted multimodal data. Also provided are related methods and systems for cancer detection.

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 application discloses a kind of multi-organ segmentation systems and colon cancer detection systems based on deep learning, multi-organ segmentation system includes: image processing module, for converting CT image into NIFTI three-dimensional image format, carries out data standardization processing;Again divide several sub three-dimensional images;Multi-organ detection segmentation module is used to the spleen, right kidney, left kidney, liver, aorta, inferior vena cava region in CT image is segmented.The colon cancer detection system includes: image processing module;Detection segmentation module is used to the colon cancer region in PET-CT image is segmented;Quantitative analysis module is used to the standard uptake value of colon cancer region is quantitatively analyzed.The application realizes the segmentation of each region in CT image;While the colon cancer lesion in PET-CT image is segmented, the standard uptake value of lesion region is quantitatively analyzed;Give consideration to the simplicity of operation, the accuracy of segmentation result and quantitative analysis function, realize the accurate segmentation and automatic analysis function of colon cancer region.