147 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.

Devices for improving adenoma detection during a colonoscopy and, more particularly, to devices positionable at a distal end of an endoscope and structured or configured to expand and retract upon longitudinal or rotational displacement of at least a portion of the device about the endoscope.

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.

The present disclosure provides methods and compositions, e.g., kits, for diagnosing colorectal cancer or precancerous colorectal advanced adenomas based on a subject's biomarker panels in a biological sample (e.g., a feces sample or a blood sample).

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 plasma proteome-based colorectal cancer precancerous lesion diagnosis model construction method, and aims to solve the problem that a current colorectal cancer diagnosis kit is insufficient in sensitivity and specificity when detecting an early stage (precancerous lesion / early cancer) through a proximity extension technology (PEA) and selecting a specific detection panel. Protein biomarkers in plasma samples of 80 patients with precancerous lesion (progression stage adenoma) and early cancer (Tis / T1 stage) in a discovery queue are identified. By adjusting potential clinical confounding factors, 15 key protein biomarkers are identified. In the discovery queue, four machine learning algorithms (Lasso regression, random forest, SVM and XGBoost) are adopted to diagnose early colorectal cancer lesions, and high diagnosis efficiency is realized through multiple machine learning algorithms. In a verification queue sample, the diagnosis model also shows an important diagnosis capability for patients with progression-stage adenoma or early-stage colorectal cancer, and has a potential clinical application 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 invention provides a colon adenoma detection method and system based on Mueller matrix analysis, and the method comprises the steps: collecting a fresh medical sample containing colon adenoma and normal tissues, and carrying out the calculation decomposition of the fresh medical sample, and obtaining a Mueller matrix and polarization decomposition parameters; according to the polarization information separation and combination characteristics of the Mueller matrix, extracting a preset polarized light characteristic component, and carrying out dimensionless processing and linear combination model solving on the preset polarized light characteristic component to obtain characteristic value distribution of each sample; and verifying the distinguishing capability of characteristic value distribution by using experimental data, optimizing weight parameters of the linear combination model, determining a final linear combination model, and outputting a colon adenoma detection result according to the final linear combination model. By utilizing the synergistic effect of M01, M11 and a depolarization factor, efficient differentiation of colon adenoma and normal tissues is realized after dimensionless treatment.

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 invention discloses a double-antibody sandwich ELISA kit for detecting ENTV and JSRV and application of the double-antibody sandwich ELISA kit, and belongs to the technical field of biology. The double-antibody sandwich ELISA kit contains an ELISA plate coated with a monoclonal antibody for resisting goat endemic intranasal tumor virus p27 protein, a polyclonal antibody for resisting goat endemic intranasal tumor virus p27 protein and HRP (horse radish peroxidase) labeled goat anti-rabbit IgG (immunoglobulin G). Wherein the monoclonal antibody for resisting the goat endemic intranasal tumor virus p27 protein is secreted by a hybridoma cell strain ENTV-2C3, the hybridoma cell strain ENTV-2C3 is preserved in the China Center for Type Culture Collection, and the preservation number is CCTCC NO: C 2024412. The double-antibody sandwich ELISA kit established by the invention has the characteristics of high detection sensitivity and good specificity, can be used for detecting goat endemic intranasal tumor virus and sheep lung adenoma retrovirus, and provides a technical means for prevention and control of goat endemic intranasal tumor and sheep lung adenoma.

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 relates to a thymic tumor microenvironment antigen detection kit and its application in myasthenia gravis evaluation. The thymic tumor microenvironment antigen detection kit includes quantum dot-coupled antibody AchR, quantum dot-coupled antibody Titin, quantum dot-coupled antibody MuSK, quantum dot-coupled antibody LRP4 and quantum dot-coupled antibody RyR; quantum dot staining is performed on thymic tumor tissue sections by the kit, and different antigen staining can be performed on the same tissue section at the same time. There are differences in the expression levels of AchR, Titin, MuSK, LRP4 and RyR antigens in patients with simple thymoma, simple myasthenia gravis and thymoma combined with myasthenia gravis, and this difference can be used to distinguish the cause, course and prognosis of myasthenia gravis. The method of using quantum dots to directly carry specific antibodies eliminates the complex staining link of secondary antibodies and improves work efficiency; and observes its expression level and spatial position.

Provided are methods for preventing or delaying the need for surgical intervention in a patient having familial adenomatous polyposis (FAP) and an at least partially intact lower gastrointestinal tract. Also provided are methods for preventing or delaying the formation of neoplasia and / or cancer in a patient having FAP. The methods comprise administering an effective amount of a pharmaceutical therapy that comprises eflornithine and sulindac to a patient having FAP and an intact lower gastrointestinal tract.

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.