564 results about "Cervical ca" patented technology

The invention belongs to the technical field of biological detection, and particularly relates to a monoclonal antibody 4F6 for HPV16 type E7 protein detection and application. The heavy chain variable region CDR sequences and the light chain variable region CDR sequences of the antibody are SEQ ID NO.1-3 and SEQ ID NO.4-6 respectively, and the antibody can be specifically combined with HPV16 type E7 protein without cross reaction. A double-antibody sandwich ELISA detection system constructed based on the antibody is combined with a biotin-avidin amplification technology, the sensitivity reaches 100 pg / mL, and the antibody is suitable for rapid detection of the HPV16 type E7 protein in a cervical exfoliated cell sample. The invention further provides the HPV16 type E7 recombinant protein which is obtained through prokaryotic expression and has immunocompetence, and the HPV16 type E7 recombinant protein is applied to antibody screening and detection. Compared with nucleic acid detection, the method has a lower false positive rate, can be used for early screening of cervical cancer and precancerous lesions, and has a good application prospect.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention discloses a cervical cancer close-range radiotherapy high-risk target area sketching method fused with a multi-modal image. The method comprises the following steps of collecting an MRI image scanned before radiotherapy and a CT image during radiotherapy of a cervical cancer close-range radiotherapy patient and annotation data of the MRI image and the CT image; the method comprises the following steps: preprocessing an MRI image scanned before radiotherapy and a CT image during radiotherapy, and converting annotation data into a three-dimensional tag image; on the basis of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, registration of the MRI image and the CT image is conducted through a pre-constructed registration neural network model, feature extraction and fusion are conducted on the registered MRI image and the registered CT image through a pre-constructed high-risk target area automatic segmentation neural network model of a multi-scale cross-modal attention mechanism, and the high-risk target area automatic segmentation neural network model of the multi-scale cross-modal attention mechanism is obtained. Automatic delineation of a cervical cancer close-range radiotherapy high-risk target area is realized; according to the method, automatic segmentation of HR-CTV in close-range radiotherapy of cervical cancer is realized, high efficiency, accuracy and generalizability are realized, and intelligent support can be provided for clinical work.

The invention belongs to the field of biological detection, and discloses a kit for detecting cervical cancer and application of the kit. The kit comprises an elisa plate coated with a captured antibody, an HRP labeled antibody, an antigen standard substance, a washing solution, a developing solution and a stop solution, the heavy chain amino acid sequence of the monoclonal antibody CPT1A-1 of the anti-CPT1A protein in the capture antibody is as shown in SEQ ID NO. 1, and the light chain amino acid sequence is as shown in SEQ ID NO. 2; the heavy chain amino acid sequence of the monoclonal antibody CPT1A-2 of the anti-CPT1A protein in the HRP labeled antibody is as shown in SEQ ID NO.3, and the light chain amino acid sequence of the monoclonal antibody CPT1A-2 of the anti-CPT1A protein in the HRP labeled antibody is as shown in SEQ ID NO.4. The kit for diagnosis of cervical cancer provided by the invention has strong specificity, can specifically detect the content of CPT1A protein in serum, has good stability, and has good application prospects.

The invention relates to the technical field of biological detection, in particular to a monoclonal antibody combination for HPV18 type E6 protein detection and application. The provided combination is composed of 5G3 and 2C7, and the amino acid sequences of complementary determining regions of variable regions of a heavy chain and a light chain of the combination are clear and are respectively shown as SEQ ID NO.1-12. The antibody combination has high specificity and sensitivity, the lowest detection limit can reach 100 pg / ml, and cross reaction with other HPV subtypes is avoided. According to a double-antibody sandwich ELISA and biotin-avidin amplification detection system constructed based on the combination, the signal intensity and the detection accuracy are remarkably improved, and the combination is suitable for rapid detection of the HPV18 type E6 protein in a cervical exfoliated cell sample and has application value in early diagnosis of cervical cancer, risk stratification, vaccine research and development and curative effect evaluation.

The present disclosure relates to detecting one or more types of gynecological cancer in a biological sample from a subject. In particular, the present disclosure provides compositions and methods for detecting the presence or absence of one or more types of gynecological cancer (e.g., cervical cancer, ovarian cancer, endometrial cancer) in a biological sample of a subject suffering from or suspected of suffering from gynecological cancer.

The invention provides a deep learning-based automatic sketching method and system for high-risk clinical target regions and endangered organs in cervical cancer brachytherapy, and is applied to the technical field of data processing. According to the application, CT image information and MR image information of a cervical cancer patient are processed based on a positioning network, and 3D position information and form information of a source applicator and image feature sequence information are generated; performing classification processing on the CT image information of the cervical cancer patient based on a positioning network, and generating a priori knowledge label; processing the priori knowledge label based on the segmentation network, and generating a source applicator positioning target factor; the source applicator positioning target factor, the 3D position information and form information of the source applicator and the image feature sequence information are processed based on the segmentation network, and high-risk clinical target region and organ segmentation parameter information is generated; and processing the high-risk clinical target region and organ segmentation parameter information to generate an automatic sketching result of the high-risk clinical target region and the organ at risk for cervical cancer close-range treatment.

The invention provides a cervical cancer MRI image automatic segmentation method based on multi-modal fusion, and the method comprises the following steps: S1, obtaining tumor segmentation mask images of three modals T1c, T2 and DWI of a cervical cancer MRI image through an automatic prompt segmentation method, and carrying out the cross-modal attention interaction of a task token and an output token, dynamic alignment of medical image features and segmentation tasks is achieved, and segmentation mask images of all modes are generated in combination with sparse and dense prompts; and S2, performing registration and feature extraction on the segmentation mask images of the three modes to obtain a structured medical description of the segmentation mask image of each mode, and performing fusion processing on the structured medical descriptions by using a large language model to obtain a tumor segmentation mask image after multi-mode fusion. According to the method, the segmentation mask image of the MRI multi-mode image is fused by introducing the large language model, and the fused tumor segmentation mask image is finally obtained by combining the segmentation characteristics of different modes, so that the segmentation precision of the MRI image is greatly improved.

The embodiment of the invention discloses a cervical cancer wettability risk assessment method and system based on three-dimensional ultrasound and tomographic ultrasound imaging. The method comprises the steps that three-dimensional ultrasound image data and tomographic ultrasound imaging data of a target cervical region are acquired; carrying out preprocessing and image registration on the three-dimensional ultrasonic image data and the tomography ultrasonic imaging data; fusing the spatial structure information and the chromatography tissue information by adopting a weighted fusion algorithm to generate fused image data containing multi-dimensional features; performing feature extraction on the fused image data by using a feature extraction model, and combining morphological features and histological features to generate a high-dimensional feature vector; extracting hemodynamic parameters based on the three-dimensional ultrasonic image data; inputting the high-dimensional feature vector and the hemodynamic parameters into a risk assessment model to generate a cervical cancer wettability risk score; according to the risk score, outputting a cervical cancer wettability risk assessment result; and high-precision cervical cancer wettability evaluation is realized.

The invention relates to the technical field of medical teaching molds, in particular to a cervical cancer staging model demonstration teaching aid, and aims to overcome the defects that in the prior art, the demonstration effect is single, the demonstration effect is poor, a model cannot be protected when the device is not used, the model is prone to being damaged, and follow-up use is affected. The device comprises a box body; the handle is fixedly connected to the top of the box body; the number of the upper fixing plates is two, and the two upper fixing plates are fixedly connected to the two sides of the outer portion of the box body correspondingly; the number of the lower fixing plates is two, and the two lower fixing plates are fixedly connected to the two sides of the outer portion of the box body correspondingly; the mounting plate is connected to the interior of the box body in a sliding manner; the second connecting column is fixedly connected to the top of the mounting plate; according to the application, through different model display, the demonstration effect is improved, and after use, the model can be protected to facilitate next use.

The invention relates to the technical field of medical detection, in particular to a cervical cancer screening system based on risk HPV (human papillomavirus) expansion typing, and a risk assessment module provides solid technical support for individualized CIN2 + / 3 + risk assessment by using a Bayesian network and a Cox proportional risk model. By dynamically acquiring key data such as HPV types, cytological results, patient ages and infection duration, the Bayesian algorithm is beneficial to constructing individual specific risk models, and real-time risk division is carried out. The Cox regression model predicts accumulated risks in three years, dynamically adjusts a risk assessment threshold value and refreshes the weight of an input variable in real time in combination with a machine learning mechanism, and the composite model structure enables the risk assessment to be more flexible and ensures the scientificity of a management decision.

The invention relates to the technical field of clinical diagnosis, in particular to a cervical cancer risk diagnosis system. The image processing module is used for extracting image features based on a preprocessed cervical cell pathological image; the text processing module is used for extracting text features based on the preprocessed medical record book data; the feature alignment module is used for aligning the image features and the text features to obtain the aligned image features and text features; the feature fusion module is used for fusing the aligned image features and text features by using a mutual attention mechanism to obtain fused features; and the prediction module is used for predicting the cervical cancer onset risk of the target patient by using a preset tumor risk prediction model based on the fusion features to obtain a dichotomy prediction result. Therefore, through the cervical cancer risk diagnosis system, the problem of low screening accuracy caused by manual limitation or difficulty in multi-modal data fusion in the existing diagnosis technology is solved, and the accuracy of early screening of cervical cancer is improved.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

The invention relates to the technical field of biological medicine, and discloses an aromatic polyketone compound as well as a preparation method and application thereof. According to the aromatic polyketone compound, a compound I and a compound II are separated from an ethyl acetate extract of rice fermentation liquor of Crystalomyces hyricus, and the compound I and the compound II are respectively named as Crystaloketide A and Crystalomycin D. In-vitro anti-cancer activity tests are carried out on the Crystaloketide A and the Crystalomycin D. According to the aromatic polyketone compound disclosed by the invention, the anti-cancer activity of the Crystaloketide A and the Crystalomycin D. Results show that the compound I and the compound II have the effects of inhibiting human cervical cancer cells, human liver cancer cells, human nerve cancer cells and human pancreatic cancer cells respectively, so that the compound I and the compound II can be further prompted to be used for preparing anti-cancer drugs.

The present invention relates to the field of biological detection technology, and in particular to a monoclonal antibody combination and application for the detection of HPV18 type E6 protein. The provided combination consists of 5G3 and 2C7, and the amino acid sequences of the complementary determining regions of the heavy chain and light chain variable regions are clear, as shown in SEQ ID NO.1-12, respectively. The antibody combination has high specificity and sensitivity, with a minimum detection limit of up to 100 pg / ml, and does not cross-react with other HPV subtypes. The double-antibody sandwich ELISA and biotin-avidin amplification detection system constructed based on the combination significantly improves the signal intensity and detection accuracy, is suitable for the rapid detection of HPV18 type E6 protein in cervical exfoliated cell samples, and has application value in the early diagnosis of cervical cancer, risk stratification, vaccine development and efficacy evaluation.

The invention provides equipment, a method and a system for evaluating prognosis of cervical cancer, squamous carcinoma and adenocarcinoma and application of the equipment, the method and the system, and belongs to the technical field of medical evaluation. Two biomarkers, namely adenylate kinase 4 and cavernous malformation 2 protein, for evaluating prognosis of cervical squamous cell carcinoma and adenocarcinoma are obtained through multivariate statistical analysis, difference screening, COX regression analysis and other biosignal analysis methods. Based on the biomarker, evaluation equipment, a computer execution method, a computer system and the like for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma are constructed, and the evaluation equipment, the computer execution method, the computer system and the like are used for accurately judging the prognosis condition of a cervical cancer patient.

The invention relates to the technical field of computer image processing and medical image analysis, in particular to a cervical cancer MRI (Magnetic Resonance Imaging) image segmentation method for improving a U-Net structure based on a feed-forward channel double-attention mechanism. In order to solve the problems that a traditional U-Net structure is weak in multi-scale information extraction capability, inaccurate in boundary fuzzy region recognition and the like in processing female abdominal cervical cancer MRI images, the method proposes that a feedforward connection mechanism and a double-attention mechanism are embedded into an encoder and a bottleneck module to form a novel U-Net segmentation model; the recognition and segmentation precision of the cervical cancer focus area is improved, and a high-quality image basis is provided for subsequent clinical diagnosis and treatment. A double-attention mechanism is integrated into a plurality of key nodes of the model, a joint channel-space attention module is added to the tail of each convolution module in an encoder, a cavity space pyramid pooling structure is introduced to a bottleneck position, a channel attention mechanism is embedded, and joint modeling of cross-scale, multi-channel and space context information is achieved.

The invention belongs to the field of plant endophytic fungus secondary metabolites, particularly relates to a secondary metabolite of fennel endophytic fungus fusarium oxysporum as well as a preparation method and application thereof, and provides a fennel endophytic fungus secondary metabolite with structures as shown in compounds 1-8. Wherein the compound 2 and the compound 7 have inhibitory activity on staphylococcus aureus, escherichia coli, bacillus cereus, bacillus subtilis, candida albicans and shigella, and have a certain inhibitory effect on cervical cancer Hela cell strains.

The invention discloses a blood marker for cervical cancer diagnosis and application thereof, and belongs to the field of biomarker detection. The blood marker is a serum exosome tRF-16, and the nucleotide sequence of the serum exosome tRF-16 is as shown in SEQ ID NO. 1. By separating serum exosome RNA of a cervical cancer patient and a healthy control group and combining data of a published database, high-specificity and high-sensitivity tsRNA related to the cervical cancer is found, and the diagnostic kit for clinical detection of the cervical cancer is developed. And simpler, more convenient and quicker help is provided for clinical early screening diagnosis and progress early warning of cervical cancer.

The utility model discloses a cervical cancer sampling device, and particularly relates to the technical field of sampling devices, which comprises a sampler shell, an inserting rod is vertically arranged at the lower end in the sampler shell, a plurality of sampling brush heads are annularly mounted on the side wall of the inserting rod, and a rotating mechanism for rotating the plurality of sampling brush heads is arranged in the sampler shell. The medical sampling device has the technical effects and advantages that the rotating mechanism is arranged, so that the sampling brush head can be conveniently driven to rotate and sample, and the situation that the comfort of a patient is influenced due to rotation of the sampler shell is avoided; and by arranging the limiting mechanism, the position of the pressing head can be conveniently limited, the operation convenience is improved, and the operation complexity is simplified.

The present application relates to the technical field of medical magnetic resonance imaging, and discloses an intelligent dynamic contrast-enhanced magnetic resonance parametric imaging method and apparatus. In the method, a pixel-level vision transformer is integrated at the connection position of an encoder and a decoder of a generator to enhance the learning capability for non-local patterns and time series information in medical images, thereby improving the parameter estimation precision in noisy and variable environments. Gradient penalty is introduced into a loss function to optimize the performance of a discriminator, so that the discriminator can more effectively distinguish between real and generated images. A CycleGAN-like model can fully extract and use spatial features while performing time series analysis, thereby providing a more comprehensive and detailed PK parameter map, and providing powerful technical support for improving the accuracy of diagnosis of diseases such as cervical cancer.

The invention discloses a preparation and purification method and application of a quinoline derivative with biological activity, and belongs to the technical field of medicine synthesis. According to the key points of the technical scheme, the quinoline derivative molecule has a structure # imgabs0 #, wherein X is NH or empty (benzene rings are directly connected); r is an aromatic ring derivative or alkyl or other groups. The invention designs and synthesizes a quinoline derivative with a novel structure, and the compound has a relieving effect on LPS-induced microglial cell inflammation and has a certain inhibition effect on cervical cancer cells.

The invention discloses a preparation method and application of an HPV16 / 18 type E6 / E7 protein multi-target detection test strip, and belongs to the technical field of biological immunodetection analysis. The detection test strip comprises a back plate, a sample pad, a combination pad, a nitrocellulose membrane and an absorption pad, AIE fluorescent microsphere labeled probes are distributed on the combination pad, and a capture antibody and an AIE solution are distributed on the nitrocellulose membrane. The preparation method comprises the following three steps: preparation of the nitrocellulose membrane, preparation of the combination pad and assembly of the test strip. The detection test strip can synchronously and quantitatively detect PV16 E6, HPV16 E7, HPV18 E6 and HPV18 E7, each target corresponds to four signal points, multi-target synchronous rapid detection is achieved, the linear range is wide, the detection limit is low, operation is easy and convenient, cost is low, and the detection test strip is expected to become a novel efficient and reliable on-site screening tool for cervical cancer and precancerous lesions.

The invention provides a monoclonal antibody for detecting E6 protein in cervical cancer cell nucleus and application of the monoclonal antibody. The invention provides the monoclonal antibody which is high in specificity, strong in affinity, stable in expression, good in repeatability and capable of specifically recognizing the HPV 16E6 protein in a cell nucleus, especially the HPV 16E6 protein, and has the potential of being used for preparing medicines for preventing and treating cervical cancer.

The invention provides a cervical cancer self-adaptive radiotherapy assessment decision method and device, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: obtaining a cone beam CT image and a plan CT image before radiotherapy of a patient to be subjected to radiotherapy on the day of radiotherapy, and extracting the structural quantitative features of the images; the method comprises the following steps: calculating a difference value of structured quantitative characteristics between a cone beam CT image on the day of radiotherapy and a plan CT image before radiotherapy to obtain multi-dimensional geometric change characteristics; and inputting the multi-dimensional geometric change features, the vectorized representation of the predetermined key region of the cone beam CT image and the clinical text information into a pre-trained clinical large language model, and outputting a self-adaptive radiotherapy demand tag. The problem that the prior art depends on manual analysis, and it is difficult to compare the change characteristics of the CT image between the radiotherapy day and the plan before radiotherapy to automatically analyze whether self-adaptive radiotherapy is executed on the radiotherapy day is solved.

The invention provides an integrated cervical HPV (human papillomavirus) and cervical cytology self-sampling device, and belongs to the field of medical instruments. The problems that most existing cervix uteri self-sampling devices are designed in a split mode, samplers and specimen protection liquid are packaged separately, the structure is complex, large-scale production is not facilitated, the sampling efficiency of sampling heads is low, and samples of the sampling heads are prone to being polluted are solved. The device comprises a sleeve, a central sampling tube and a balloon, the sleeve slidably sleeves the outer side of the central sampling tube, the central sampling tube is of a hollow structure, the front end of the central sampling tube is provided with a sampling head, the outer side of the central sampling tube is fixedly sleeved with a piston, the outer wall of the piston is in contact with the inner wall of the sleeve and is in sliding fit with the inner wall of the sleeve, and the outer wall of the piston is in sliding fit with the inner wall of the sleeve. The tail end of the central sampling tube is connected with a balloon, a plurality of through holes are formed in the side wall of the central sampling tube between the sampling head and the piston, and a sealing end cover is arranged at the front end of the sleeve. The self-sampling device is mainly used for self-sampling of cervical cancer screening.

The present invention discloses an in vitro method for obtaining DNA methylation biomarkers as exquisite DNA methylation positions in the human genome (i.e., CGIDs) that predict cervical cancer especially at as yet inaccessible early stages by examining progression of “categorical” DNA methylation alterations in three stages of premalignant lesions (cervical intraepithelial neoplasia (CIN)), progressing from CIN1 to CIN3. The present invention discloses combinations of CGIDs for detecting with high specificity and sensitivity cervical cancer by measuring their DNA methylation status and deriving a “methylation score”, which is useful as a biomarker for cervical cancer. Also disclosed are kits for predicting cervical cancer using such CGIDs using multiplexed next generation sequencing methylation assays, pyrosequencing assays and methylation specific PCR. The DNA methylation markers (CGIDs) described in the present invention are useful for cervical screening and early detection of cervical cancer by any person skilled in the art to detect cervical cancer.

The invention provides a method and a model based on transcriptome and intratumoral microorganisms, and particularly relates to a calculation model for evaluating cervical cancer lymph node metastasis risk and a related method, program, system and equipment. According to the model, abundance information of various gene markers (including CARD9, MNX1AS2, MRAS, OLFML2A and RPS28) and abundance information of multiple microorganisms (including Dialister, Catonella and Campylobacter) are fused as input characteristics, and a risk prediction model is constructed through a machine learning algorithm, so that quantitative evaluation on the lymph node metastasis risk of the cervical cancer patient is realized. The prediction precision of cervical cancer lymph node metastasis is improved, an auxiliary diagnosis decision basis is provided for clinic, and the method has important medical application value and industrial transformation potential.

The present invention provides a biomimetic MOF nanoplatform that can dual-target cervical cancer tumor cells and cancer-associated fibroblasts and co-deliver FAK inhibitors and bismuth, and its preparation method and application. The biomimetic MOF nanoplatform includes a MOF material loaded with FAK inhibitors (IN10018) and bismuth (Bi), and a hybrid membrane coated on the outside of the MOF material loaded with FAK inhibitors and bismuth. The hybrid membrane is obtained by physical extrusion after mixing cervical cancer tumor cell (HeLa and SiHa) membranes and cancer-associated fibroblast (CAFs) membranes. In an acidic environment, the biomimetic MOF nanoplatform of the present invention disintegrates ZIF-8 nanoparticles loaded with IN10018 and Bi, releases IN10018, reduces CAFs infiltration in the tumor, and at the same time, the addition of Bi improves radiation absorption efficiency, further enhancing the sensitivity of the tumor to radiotherapy through a dual-targeting mechanism.

The invention discloses a probe set, a kit and a nucleic acid signal amplification in-situ hybridization detection method, and relates to the technical field of molecular biology, the probe set comprises: a labeled extension probe, the sequence of which is as shown in SEQ ID No.9-24; and the sequence of the closed probe is as shown in SEQ ID No. 25-27. The kit comprises the probe set, a signal amplification precursor, a signal amplification body and an alkaline phosphatase labeled probe. According to the present invention, the partial probe sequence introduces the non-natural bases, and can be used for detecting the HPV18 infected cervical cancer Hela cells, the experiment results show that the introduction of the non-natural bases does not affect the normal positive value, the matching degree between the non-natural bases is more specific than the matching of the natural bases, and the detection sensitivity is improved.