112 results about "Adenoma" patented technology

The invention discloses a septal medical image segmentation method based on deep learning, and the method comprises the steps: collecting user-defined data sets of different types of medical images, and carrying out the data cleaning, preprocessing and marking, and obtaining a sample data set; wherein the different types of medical images comprise three-dimensional CT images of patients with germ cell tumor, lymphadenoma, neuroma, teratoma and thymoma; the sample data set comprises a 2D slice generated by cutting the three-dimensional CT image and a corresponding mask file; dividing the sample data set into a training set, a verification set and a test set according to a proportion of 8: 1: 1; inputting the training set into a pre-training MedSAM model in batches, and obtaining a prediction segmentation result of voxels in the training sample through forward calculation; determining target loss based on the predicted segmentation result and a prior label corresponding to the mask file, and reversely updating parameters of the pre-trained MedSAM model until convergence to obtain a medical image semantic segmentation model; and evaluating the performance of the medical image semantic segmentation model by using the verification set and the test set.

The invention relates to a methylation biomarker for detecting colorectal cancer or colorectal adenoma. The methylation biomarker comprises any one gene of SDC2, COL4A2 and C9H9orf50 or any combination of the SDC2, the COL4A2 and the C9H9orf50. Wherein a methylation target detection region of the SDC2 gene is a full-length region of chr8: 96494012-96494130 or a partial region of the full-length region of chr8: 96494012-96494130; a methylation target detection region of the COL4A2 gene is a full-length region of chr13: 110306814-110306983 or a part of the full-length region of the chr13: 110306814-110306983; and the methylation target detection region of the C9H9orf50 gene is the full-length region of chr9: 129620291-129620374 or a part of the full-length region of chr9: 129620291-129620374. According to the kit, methylation signals related to intestinal cancer and adenoma in excrement samples can be more accurately detected through detection in different combination modes of the three markers, the early effective diagnosis rate of colorectal cancer patients can be increased, and therefore the survival rate of the patients is increased, and prognosis is improved.

The invention discloses a multi-scale adaptive lesion detection method based on mammary gland ultrasound. The method comprises the following steps: pre-processing an image; the input module is used for extracting features through convolution blocks to clearly display boundaries, and then deconvolution blocks are used for refining boundary features to complete image expression; a trunk module; in the neck network, the CA generates feature maps in two directions, the feature maps in the two directions are spliced, features are extracted through convolution operation, and attention weights in the two directions are further generated; and the detection head network performs positioning prediction, classification prediction and loss calculation, and finally outputs a result. According to the method, the precision and richness of feature extraction are improved, and the clear recognition capability of the model on different tissue boundaries is enhanced; the capacity of capturing multi-scale lesion features is improved, and the lesion features are better captured; important areas such as small calcification points, changes of cyst walls and boundaries of fibroadenoma can be highlighted, and the accuracy and specificity of detection are improved.

The invention discloses a thyroid tumor classification and recurrence risk prediction method based on deep learning, and belongs to the field of medical image processing, and the method comprises the following steps: collecting and preprocessing multi-modal data related to thyroid follicular carcinoma FTC or follicular adenoma FTA; performing feature extraction on the preprocessed multi-modal data to obtain image features and pathological features; respectively adopting different fusion and optimization methods for the combination of the image features and the pathological features and the image features to obtain a first optimized fusion feature and a second optimized fusion feature; and inputting the first optimized fusion feature into a preoperative classification model to obtain a thyroid tumor type classification probability, and inputting the second optimized fusion feature and the pathological feature into a postoperative recurrence risk prediction model to obtain a recurrence risk score. By means of the multi-mode image classification model and the recurrence risk prediction model which are specially constructed, the sensitivity and specificity of preoperative classification are improved, and accurate individualized management is provided for postoperative patients.

Methods are provided for treating or preventing colorectal cancer, related diseases, or symptoms of any of the foregoing, e.g., familial adenomatous polyposis (FAP), in subjects of 55 years or older. The methods include administering 3-[(2R)-2,3-dihydroxypropyl]-6-fluoro-5-(2-fluoro-4-iodoanilino)-8-methylpyrido[2,3-d]pyrimidine-4, 7-dione or a pharmaceutically acceptable salt thereof.

The invention discloses a marker for early colorectal neoplastic lesion diagnosis and application, and belongs to the technical field of biological medicine. Aiming at the problems of low colonoscopy coincidence rate of colorectal cancer patients at present and limited sensitivity and specificity of existing biomarker diagnosis, LncRNA sequencing research is combined with expanded sample verification to obtain high-specificity and high-sensitivity LncRNA which is highly related to colorectal neoplastic lesions (including adenoma and colorectal cancer), the LncRNA is specifically LOC401585, and the LncRNA has the advantages of high specificity, high sensitivity, high specificity and high sensitivity and can be used for detecting colorectal neoplastic lesions (including adenoma and colorectal cancer). The diagnosis efficacy AUC of the LOC401585 on the adenoma and the colorectal cancer is 0.952 and 0.876 respectively. Therefore, the blood LOC401585 marker can be used for auxiliary diagnosis of colorectal neoplastic lesions, and provides data support for finding novel small molecule drugs with potential treatment value.

The invention discloses a colon adenoma identification method and device based on Mueller matrix polarization measurement and regularization, a storage medium and electronic equipment. The method comprises the following steps: acquiring a polarization image corresponding to a fresh medical sample containing colon adenoma and normal tissue through a light polarization system, and preprocessing the polarization image; fourier analysis is carried out on a detection signal in the light polarization system to obtain a Mueller matrix, and a Lu-Chipman polarization decomposition algorithm is used for carrying out polarization decomposition on the Mueller matrix to obtain three polarization decomposition parameters; obtaining polarization data of a plurality of channels based on the Mueller matrix, and constructing a sample set based on the polarization data; constructing a target function based on the projection function and the sample set, solving the target function to obtain an analytic solution of the optimal projection direction, and calculating a characteristic value based on the analytic solution of the optimal projection direction; and classifying the polarization images corresponding to the fresh medical samples of the colon adenoma and the normal tissue based on the feature values to obtain a classification result. The lesion recognition rate can be improved.

The invention relates to the technical field of medicine, in particular to an early evaluation method for endocrine remission of pituitary growth hormone adenoma based on low Knosp grading, which comprises the following steps: acquiring preoperative and postoperative image data and preoperative and postoperative clinical data of a patient with low Knosp grading pituitary growth hormone adenoma; extracting imaging features used for early evaluation of postoperative endocrine remission from the preoperative and postoperative image data; extracting clinical features for early evaluation of postoperative endocrine remission from the preoperative and postoperative clinical data; and performing statistical analysis on the iconography characteristics and the clinical characteristics to obtain an early evaluation result of postoperative endocrine remission. According to the method, risk factors related to whether low-Knosp graded pituitary growth hormone adenoma postoperative endocrine remission can be relieved or not are screened out, then a prediction model is established through a statistical method, and then early prediction of low-Knosp graded pituitary growth hormone adenoma postoperative endocrine remission is achieved.

A group of diagnostic biomarkers usable for diagnosis of colorectal cancer or advanced colorectal adenoma (CRC / ACRA) is provided. The method for detecting CRC / ACRA using the group of diagnostic biomarkers is also provided. For example, the method is a non-invasive approach that may utilize serum samples for detecting colorectal cancer. Moreover, the method for detecting colorectal cancer may detect colorectal cancer of different stages (e.g., pre-cancer stage, early stage, middle stage, late stage). A kit for detecting CRC / ACRA is also provided.

The present invention discloses the use of the biomarker UGT2B15 in the diagnosis of colorectal adenoma. The present invention provides the use of a biomarker for diagnosing or predicting colorectal adenoma or / and a detection reagent thereof in the preparation of a product for diagnosing or predicting colorectal adenoma. The present invention provides a kit for diagnosing colorectal adenoma or predicting colorectal adenoma in a subject. The present invention provides an electronic device for diagnosing colorectal adenoma or predicting colorectal adenoma. The present invention provides a screening method for a biomarker for diagnosing or predicting colorectal adenoma, a method for constructing a diagnostic model for colorectal adenoma, and a processor, a computer medium, and a device.

The invention discloses a system and equipment for predicting colorectal cancer and progression thereof. Based on the found expression level difference of AXIN2 and / or EPHB2 in colorectal cancer and the progression thereof, the invention provides a colorectal cancer and progression prediction method, system and equipment, a computer readable storage medium and a computer program product, high-risk groups can be efficiently and accurately screened out from common groups, and colonoscopy is further completed in time. While the economic burden caused by endoscopic screening is relieved, patients suffering from colorectal adenoma and early colorectal cancer are screened out, and the method has great significance in early diagnosis and treatment of colorectal tumor and further remarkable reduction of the morbidity and mortality of colorectal cancer.

The invention relates to methylation markers of colorectal polyp / adenoma and colorectal cancer as well as a screening method and application of the methylation markers, and further relates to a construction method of a three-classification model for simultaneously screening colorectal polyp / adenoma and colorectal cancer. Related methylation markers are screened by adopting a methylation co-immunoprecipitation sequencing technology, and a multi-stage classification model is constructed through a characteristic methylation difference region analysis algorithm, so that classification prediction of normal healthy people, colorectal polyp / adenoma people and colorectal cancer people can be realized, high sensitivity is achieved, precancerous lesions can be found as early as possible, and the clinical application prospect is wide. And a new technical scheme is provided for early screening of colorectal cancer.

In various aspects and embodiments, the Disclosure provides methods for evaluating subjects for the presence or absence of colorectal neoplasms, such as colorectal cancer (CRC), colorectal adenoma (CRA), and progressive colorectal adenoma (CRAA), by metagenomic and multi-omics analysis of feces or other biological samples. In other aspects, the Disclosure provides methods for generating machine learning models or “signatures” based on metagenomic and multi-omics analysis of feces or other biological samples to evaluate subjects for the presence or absence of colorectal diseases, including but not limited to CRC, CRA, and CRAA.

The present invention relates to a method for identifying one or more caudal midbrain (cVM) progenitor cells by measuring the presence and / or amount of adenomatous colonic polyposis down-regulating 1 (APCDD1) expressed by one or more cells. The invention also provides a method for enriching cVM cells, application of the cells in disease treatment, application of APCDD1 as a biomarker and a kit.

The invention relates to the use of a microRNA or a set of microRNAs in the in vitro differential diagnosis of forms of primary aldosteronism, wherein the microRNA is at least one microRNA selected from the following or wherein the set of microRNAs comprises at least two microRNAs selected from the following: hsa-miR-146a-5p, hsa-miR-361-5p, hsa-miR-24-3p, hsa-miR-130b-3p, hsa-miR-99b-5p, hsa-miR-151a-3p, hsa-miR-199a-3p, hsa-miR-128-3p and hsa-miR-28-3p. Preferably, the forms of primary aldosteronism are unilateral and bilateral forms, preferably unilateral aldosterone-producing adenoma (APA) and bilateral adrenal hyperplasia (BAH). The invention also relates to a method for the in vitro differential diagnosis of forms of primary aldosteronism in a subject.

The invention relates to the technical field of data prediction, and discloses a colorectal progression stage adenoma data prediction method and system based on machine learning, a terminal and a storage medium, and the method comprises the steps: screening out a corresponding candidate prediction factor according to the colorectal progression stage adenoma data of each target sample; and dividing the candidate prediction factors into a training set and a verification set, constructing a target machine learning model by using the training set, inputting the verification set into a plurality of decision trees of the target machine learning model, outputting an original prediction probability, and converting the original prediction probability into a classification probability to obtain a prediction result. According to the method, an efficient, explainable and visual machine learning early warning model is utilized, a visual decision path is generated, conventional non-invasive indexes can be effectively integrated, and individualized prediction of colorectal progression stage adenoma data is achieved.

The invention discloses a colorectal adenoma screening method based on serum metabolites, and belongs to the field of biomedical detection. The method comprises the steps of serum sample pretreatment, instrument analysis, quantitative analysis and prediction model construction, after 15-25 [mu] L of a serum sample is treated, 41 serum metabolites, age, gender and BMI are combined, and a LightGBM model is used for predicting the disease probability and performing layered suggestion. The kit fills up related gaps, has the advantages of non-invasiveness, high sensitivity and specificity, portability, high efficiency, low cost and the like, and is suitable for large-scale screening.

The present invention relates to novel protein arginine methyltransferase 5 (PRMT5) inhibitor compounds and methods for treating adenomatous polyposis coli (APC) loss-of-function driven adenoma diseases in a human in need thereof.

The present invention relates to improved methods for screening, diagnosing and / or monitoring colorectal advanced neoplasia (AN), advanced adenoma (AA) and / or colorectal cancer (CRC), wherein AN includes CRC and AA. In particular, the method provides increased specificity due to a reduction in false positive results in fecal occult blood tests (FOBTs) using characteristics based on bacterial markers. The present invention further relates to the use of the method in selecting subjects for exploratory testing (e.g., colonoscopy) or for treatment with anticancer therapy.

The utility model belongs to the technical field of mechanical arm shells for treating breast fibroadenoma, and discloses a mechanical arm shell for treating breast fibroadenoma. The manipulator shell for breast fibroadenoma treatment comprises a fixing plate and a shell assembly, a penetrating groove is formed in the fixing plate, the shell assembly comprises a shell body and a manipulator inspection window, and the shell body is detachably connected to the fixing plate in a sleeving mode. The fixing plate and the shell assembly play a role in protecting and fixing the manipulator; the inner cavity of the shell body is matched with the peripheral shape of the manipulator, so that the position of the ultrasonic probe mounted on the manipulator relative to the shell body is accurate; the shell main body is sleeved on the fixing plate, so that the position of the ultrasonic probe relative to the fixing plate is accurate; the penetrating groove of the fixing plate directly faces the notch of the mammary gland coil, it is guaranteed that the position of the ultrasonic probe relative to the notch of the mammary gland coil is accurate, and therefore the accuracy of the treatment range of the ultrasonic probe is guaranteed. And the manipulator is convenient to inspect and maintain by disassembling the manipulator inspection window.

Disclosed herein systems, processors, or computer-readable media configured with instructions to: receive transmission and / or reflection images of a tissue of a subject, wherein the images are generated from acoustic signals derived from acoustic waveforms transmitted through the tissue; provide a set of prognostic parameters associated with a user selected region of interest; wherein the set of prognostic parameters comprises sound propagation metrics characterizing sound propagation within a tissue; wherein the set of prognostic parameters corresponds to inputs into a tissue classifier model; wherein the set of prognostic parameters comprises a plurality of subsets of related feature groupings; and determine a type of tissue of the subject based on said plurality of subsets of related feature groupings using the classifier model, wherein the type of tissue is a cancerous tumor, a fibroadenoma, a cyst, a nonspecific benign mass, and an unidentifiable mass.

The present invention discloses a traditional Chinese medicine composition for preventing recurrence of colorectal adenoma after endoscopic treatment. The traditional Chinese medicine composition is prepared from the following raw materials in the following weight ratios: 25-35 parts of astragalus, 8-15 parts of black plum, 6-12 parts of white mustard seeds, 5-10 parts of zedoaria, 8-15 parts of bombyx batryticatus, 25-35 parts of coix seed, and 25-35 parts of oyster. The traditional Chinese medicine compound composition for preventing recurrence of colorectal adenoma after endoscopic treatment provided by the present invention has been shown, through a large number of clinical trials, to be effective in preventing recurrence of colorectal adenoma after endoscopic treatment, and has achieved excellent technical results.

The present disclosure concerns a computer-implemented method for transforming stool sample data into a diagnostic code, the method including: a) providing a stool sample from a subject; b) determining, as the stool sample data, expression levels of at least two distinct genes from a plurality of mRNA segments in the stool sample; and c) determining, by a computing system executing a machine-learning model and based on the stool sample data, a diagnostic code, wherein the machine-learning model was trained using stool diagnostic training data including a plurality of stool sample data labeled with at least one of two or more corresponding diagnostic codes.

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality in men and women. Uncontrolled proliferation of CRC cells is pivotal in colorectal tumorigenesis and cyclooxygenase-2 (COX-2) is an important regulatory enzyme in this process. Timely diagnosis is the key to life-saving management of CRC, which is under-diagnosed because colorectal aberrant crypt foci, hyper plastic polyps, micro edemas are often missed with conventional colonoscopy. Disclosed herein are imaging agents tagged to nanobodies specific for COX-2 protein that can selectively accumulate and retain in COX-2 expressing cells and colorectal adenomas, resulting in specific and sensitive of early detection, while reducing background and off-target systemic toxicity. The prospect of combining structural and functional imaging readouts obtained with fluorescence-aided colonoscopy measurements would provide powerful diagnostic information for improved management of colorectal carcinogenesis.

Provided herein are pharmaceutical compositions, therapeutic combinations, devices and methods for treating, ameliorating, reversing, causing the remission of, and / or preventing (acting as a prophylaxis, or preventing the initiation of) an inflammatory bowel disorder (IBD) or inflammatory bowel disease (IBD), Ulcerative Colitis; Crohn's disease; J-pouch; fistulising Crohn's disease; a Colitis which can be microscopic, lymphocytic or collagenous; an eosinophilic colitis; indeterminate colitis; idiopathic colitis; diverticulosis and diverticulitis; relapsing diverticulitis; constipation associated inflammatory bowel disease and / or small intestinal bacterial overgrowth; Irritable Bowel Syndrome (IBS) with or without diarrhoea, constipation or pain predominant IBS; periodontitis; rheumatoid arthritis; respiratory infections, appendicitis, vascular disorders such as thrombophlebitis; bacteremia; osteomyelitis; septic shock; Alzheimer's disease; Lemierre syndrome (postanginal sepsis); colonic polyps or adenomas (optionally hyperplastic, adenomatous or serrated adenomas) or preventing the growth of colonic polyps or adenomas, bowl cancer, or metastases (optionally preventing the initiation or promotion of bowl cancer or metastasis); pharyngitis; otitis; sinusitis; and any disease, symptom or condition caused or exacerbated by a Fusobacteria (optionally, a F. nucleatum or F. varium) infection. In alternative embodiments, pharmaceutical compositions comprise rifaximin alone or in combination with other antibiotics or drugs.

This invention discloses a high-performance computing-based system and method for evaluating colorectal cancer screening strategies, belonging to the field of medical information processing technology. It includes: a dual-pathway disease model module supporting both adenoma-carcinoma pathways and serrated adenoma pathways, modeled using age-dependent normal distribution progression time; an individualized risk scoring module integrating risk factors such as family history, inflammatory bowel disease, obesity, and diabetes; a screening strategy simulation module supporting combined screening strategies using different tools, employing compliance parameters specific to the Chinese urban population; a high-performance computing module using Numba JIT compilation technology for acceleration, multi-process parallel processing for further acceleration, and data structure optimization such as integer age to reduce memory usage; and a health economics evaluation module calculating indicators such as QALY, LYG, and ICER. This invention achieves significant performance improvements while maintaining model accuracy, greatly reducing simulation time for large-scale populations.

The present invention provides a composition comprising fosfomycin for reducing or removing bacterial promoting factors of colorectal cancer from colorectal adenomas, polyps, and adenocarcinomas, as well as from associated bacterial biofilms, by local administration into the intestinal lumen.

110 ABSTRACT METHODS FOR DETECTION OF DISEASE The present disclosure provides for, among other things methods for identifying methylation statuses of markers (e.g., biomarkers). In various embodiments, the present disclosure provides methods for methods of detection (e.g., screening) of a disease or 5 condition. A disease or condition discussed herein can be, e.g., advanced adenoma, colorectal cancer, other cancers, or other diseases or conditions associated with an aberrant methylation status. In various embodiments, the present disclosure provides methods for analysis of one or more methylation biomarkers in DNA (e.g., cell-free DNA, e.g., ctDNA) of a subject (e.g., a subject suspected of having colorectal cancer and / or 10 advanced adenoma).

The invention discloses an inflammatory bowel disease classification and interpretable diagnosis method and system based on deep learning, an image acquisition module acquires image frames from colonoscope videos, and an image preprocessing module rejects low-quality images with high black pixel proportion and blurred images and executes color space normalization. The qualified image is sent to a double-stage segmentation model to obtain a polyp area mask image, the mask image is used for positioning an ROI image of the polyp area, the ROI image is input into a polyp classification model constructed based on a visual Transform structure, and a hyperplasia type or adenoma type classification result is obtained. According to the method, on the premise that extra cost input is not needed, the efficiency and accuracy of polyp recognition and grading in the colonoscope image are greatly improved, manual subjective errors are reduced, and the intelligent level and clinical practicability of early intestinal cancer screening are improved.

The application relates to a thymoma epithelial cell subpopulation with neuromuscular characteristics and application, and a thymoma epithelial cell subpopulation with neuromuscular characteristics is obtained through single clone dilution culture screening from a thymoma cell line Thy0517 of a patient with myasthenia gravis (MG) in combination, and a thymoma epithelial cell with neuromuscular characteristics in the cell subpopulation is named as Thymus_NMi; the cell subpopulation has synapse-like structures and neuromuscular adhesion characteristics, and efficiently expresses genes participating in neural cell adhesion and synapse connection, highly integrates neuromuscular double characteristics, and simulates key pathological characteristics of abnormal thymus-induced immune tolerance of MG patients in a molecular phenotype and physiological function, so that a cell model closest to a real clinical state is provided for exploring a myasthenia gravis occurrence mechanism.