11 results about "Cancer screening" patented technology

Controls for the identification of various genotypes and / or for the identification or characterization of a disease or condition are described. These controls can be especially useful for diagnostic tests that use circulating cell-free DNA or microRNA. A control may consist of a mixture of nucleic acids. In some formulations, a control comprises liposomes, for example, where the nucleic acids of the control are associated with the liposomes. For example, controls are useful for determining whether a fetus has aneuploidy. Methods for using these controls are also described.

This invention discloses a method for detecting 5-hydroxymethylcytosine based on triple cascade signal amplification, belonging to the field of biochemical detection technology. The method includes the following steps: 1) preparation of 5hmC-specific glycosylation and azide labeling; 2) magnetic bead capture and enrichment separation of target DNA; 3) TdT-mediated nucleic acid end polymerization and extension; 4) APE1-triggered primer release and rolling circle amplification; 5) DNase-catalyzed cleavage and fluorescence signal detection. This invention is bisulfite-free and sequence-independent, achieving extremely low background and ultra-high sensitivity detection with a detection limit of 4.41 fM. It can be widely applied to early clinical cancer screening, biomarker detection for neurological or cardiovascular diseases, and provides a powerful platform for low-abundance epigenetic analysis in clinical diagnosis and biomedical research.

This invention relates to a cancer signal identification method based on a cfDNA sequence language model and its application in cancer screening and MRD monitoring. The deep learning-based sequence language model analyzes whole-genome sequencing data of cfDNA to automatically extract and identify cancer-related signals, making it suitable for clinical scenarios such as early cancer screening and minimal residual disease (MRD) monitoring. This technology overcomes the limitations of targeting specific known mutations or manually labeled biomarkers, enabling end-to-end deep modeling of high-throughput whole-genome sequences of cfDNA. This significantly improves the sensitivity, specificity, and generalization ability of early cancer screening and MRD monitoring, providing an innovative solution for non-invasive screening of multiple cancer types and longitudinal disease monitoring.

The application relates to the technical field of thyroid nodule cancer screening, and specifically discloses a marker for thyroid cancer screening, a primer probe composition and application thereof. The corresponding genes of the marker are PDCD1 genes and / or RARG genes. A composition for thyroid cancer screening comprises nucleic acids for detecting target gene methylation states, wherein the target gene methylation states are characterized by methylation of target sequences of the target genes, and wherein the target genes comprise PDCD1 genes and / or RARG genes. When the marker in the application is used to screen thyroid cancer, the screening can be based on peripheral blood PBMCs without using cfDNA, and the detection method in the application has significantly improved specificity and sensitivity.

A cancer screening method takes advantage of the Warburg Effect to provide an early detection method and system. The cancer screening method includes administering a glucose solution, such as a C-13 glucose solution, measuring a metabolized lactate level in the blood of the individual after a period of time following the administration of the glucose solution, and comparing the measured lactate level to a baseline lactate level representative of noncancerous activity, and making a determination as to whether or not the measured lactate level is potentially indicative of cancer activity in the individual. The period of time is selected to be an amount of time following the glucose administration after which metabolized lactate from cancerous cells would be present in the blood and before which the metabolized lactate would be cleared or substantial amounts of non-cancerous metabolized lactate would be present in the blood of the individual.

This invention provides methods, systems, and computer-readable storage media for cancer screening. The cancer screening methods include: acquiring multi-source heterogeneous medical data from patients; preprocessing the multi-source heterogeneous medical data to generate structured medical data; constructing an atomic variable fact layer based on the structured medical data, which is used to categorize atomic variables according to preset medical classifications; constructing a patient early screening label system based on the atomic variable fact layer using preset label mapping rules, the patient early screening label system including label data for different cancer types, different medical classifications, and different label levels; using a risk assessment intelligent engine, performing risk assessment on the patient early screening label system using screening rule matching, generating risk assessment results and screening recommendations; and constructing a risk assessment knowledge graph based on the patient early screening label system, risk assessment results, and screening recommendations, the risk assessment knowledge graph including a positive early warning knowledge graph and a false negative identification knowledge graph.

The present invention describes an integrated acoustic spectroscopy system and method for the analysis, characterization, and classification of organic, inorganic, and biological matter, assisted by supervised artificial intelligence, through the analysis of samples obtained from specific entities. The system integrates a computing unit (2) with a graphical interface (1), a function generator (3), a signal processing device (4), and an acoustic coupling clamp (5) with coaxial transducers. As an application example, glass samples (from inorganic entities), culture media (from organic entities), and cell culture lines (from biological entities) were successfully characterized and classified.The method involves capturing a digitized acoustic signature from a sample, generating a multidimensional data hierarchy that ranges from time-domain signals to high-density spectrograms obtained by Continuous Wavelet Transform (CWT) and post-processing vectors for classification. Using a 2% rescaling, standardization, and dimensionality reduction (UMAP) chain, an acoustic signature is extracted and fed into a Support Vector Machine (SVM) model optimized by Bayesian inference. The invention is notable for a management and synchronization module that allows for incremental retraining of the model through manual labeling in the dynamic repository. The system enables the differentiation of healthy and pathological cell phenotypes with a Matthews Correlation Coefficient (MCC) greater than 0.94 in less than a minute, optimizing the analysis, characterization and classification of matter without dependence on reagents or complex infrastructures, with outstanding application in cancer screening.

Exemplary systems, methods and Computer-accessible medium according to the exemplary embodiments of the present disclosure can provide a Multi-modal Transformer (MMT), a neural network that synergistically utilizes mammography and ultrasound to identify existing cancers and estimate future cancer risk. MMT aggregates multi-modal data through self-attention and modeling temporal tissue changes by comparing current exams to prior imaging. Thus, exemplary method, system and computer-accessible medium can be provided for detecting cancer. with which it possible to receive, with an artificial intelligence (AI) procedure, a plurality of scanning images associated with multiple modalities for at least one portion of a body, train the AI procedure on a multi-modal image dataset based on the plurality of scanning images, and predict, by the trained AI procedure, an existence of the cancer based on the multiple modalities of the plurality of scanning images.

This invention discloses a method and system for cancer screening. The method includes: sequencing a sample to obtain a first sequence set; aligning the first sequence set with a human reference genome to obtain a second sequence set; identifying copy number variations in the second sequence set and calculating a first and a second characteristic value; analyzing the second sequence set to obtain a third characteristic value; and determining whether the sample is cancerous based on the second and third characteristic values. This method enables the detection of methylation and tumor fraction to predict cancer patients, significantly reducing the cost of cancer screening and improving screening accuracy.

This disclosure relates to machine learning techniques for pan-cancer screening in whole-genome sequencing from non-invasive prenatal testing (NIPT) procedures. In particular, aspects include accessing NIPT sequence read data for a sample, wherein the NIPT sequence read data is generated as part of performing a whole-genome sequencing assay on an NIPT sample, and the NIPT sequence read data includes a bin count profile, which includes sequence read counts for each bin associated with a segment of the reference genome; determining indicators of systematic abnormalities for the sample based on the sequence read data; classifying the sample as negative or positive for cancer based on the sequence read data and indicators of systematic abnormalities using a machine learning model; and outputting the negative or positive class for cancer using a machine learning model.