706 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 VEGFR2-HDAC6 dual inhibitor containing chalcone fragments as well as a preparation method and application of the VEGFR2-HDAC6 dual inhibitor, and relates to the technical field of medicinal chemistry. The invention discloses a VEGFR2-HDAC6 dual inhibitor containing chalcone fragments. The structural formula of the VEGFR2-HDAC6 dual inhibitor is shown as a formula A, according to the invention, the VEGFR2-HDAC6 dual inhibitor containing the chalcone fragment and having a novel structure is designed and synthesized by utilizing a splicing principle, the anti-proliferative activity test is carried out on cervical cancer cells Siha and chronic granulocytic leukemia cells K562, and the result shows that the VEGFR2-HDAC6 dual inhibitor containing the chalcone fragment provided by the invention has relatively good inhibitory activity on the Siha cells and the K562 cells.

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 invention relates to application of recombinant III-type humanized collagen in treatment of cervical cancer, in particular to application of recombinant III-type humanized collagen in preparation of products for preventing and / or inhibiting and / or treating cervical cancer. The amino acid sequence of the recombinant III-type humanized collagen comprises n repetitions of the sequence as shown in SEQ ID NO.1, n is an integer greater than or equal to 1 and preferably is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 20, 24 or 32, and when n is an integer greater than or equal to 2, the repetitions are directly connected. Research data shows that the recombinant III-type humanized collagen can obviously inhibit the proliferation capacity and migration capacity of cervical cancer cells, and help is provided for preparation and wide application of products for preventing and treating cervical cancer and clinical treatment of cervical cancer.

The invention relates to a cervical TCT image cell segmentation method based on improved U-Net, and belongs to the field of image processing technology and medical technology. The problems that complex morphological characteristics of cells are not fully considered and fine cell segmentation is insufficient in cervical cancer intelligent screening research based on cervical TCT images are solved. Comprising the following steps: preprocessing a cervical cell TCT image; constructing a segmentation network based on the EGE-UNet; evaluating the cell image segmentation model based on qualitative and quantitative evaluation; and obtaining a cervical cell segmentation result based on the evaluated model. According to the method, high-quality cervical TCT cell image segmentation and fine segmentation of cervical cell nucleuses and cytoplasm can be realized by using the EGE-UNet model, and related research of subsequent cervical cell intelligent diagnosis is facilitated; based on an accurate cell segmentation result, the method can assist in extracting various cell morphological characteristics, has clinical guiding significance, and provides more interpretable quantitative data for subsequent cervical cell research.

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.

According to the cervical cancer white light and fluorescence picture fusion detection method based on the attention mechanism, a multi-source medical image fusion network is built, and the multi-source medical image fusion network can extract the characteristics of a white light picture and a fluorescence picture and is used for diagnosing and recognizing early-stage cervical cancer symptoms. The multi-source medical image fusion network comprises a feature extraction module, a multi-scale feature fusion module, a multi-modal feature fusion module and a multi-head attention mechanism module. Firstly, a feature extraction module is used for extracting multi-source depth features of a white light picture and a fluorescent picture, then a multi-modal feature fusion module is used for fusing the multi-source depth features, and a multi-head attention mechanism module is combined for target accurate positioning. Experimental results show that compared with a single-source image method, the accuracy of the method on an experimental data set is improved by 7%, the recall rate is improved by 6%, and experiments verify that the method improves the detection accuracy of the early-stage symptoms of the cervical cancer.

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.

The invention relates to application of plasma exosomes A1BG and TF in diagnosis of cervical cancer lymph node metastasis. Detection of lymph node metastasis is achieved through the plasma exosome A1BG and the plasma exosome TF, the ratio of the abundance of the plasma exosome A1BG to the mass of the total exosome is limited to be smaller than or equal to 14.2690 ng / mg, the ratio of the abundance of the exosome TF to the mass of the total exosome is limited to be smaller than or equal to 96.2011 ng / mg, and combined detection is conducted through the plasma exosome A1BG and the plasma exosome TF, so that the sensitivity is 44%, the specificity is 100%, and the detection time is shortened. The preoperative joint detection of the A1BG and the TF can be prompted to detect nearly half of the patient without lymph node metastasis, and unnecessary pelvic lymph node cleaning operation is avoided.

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 invention provides a cervical cancer clinical target segmentation method guided by anatomical prior position information, and the method comprises the steps: employing the CT image data of a cervical cancer patient and a reference CTV as basic data, and calculating a group of corresponding distance information image data Dimg according to the basic data; a multi-task double-branch feature fusion network MTF-Net based on a UNet network is constructed; the reference CTV and the Dimg are used as references for training the MTF-Net; in addition, CT image data is used as input information of MTF-Net intelligent segmentation; wherein the reference CTV is the DICOM format data of the cervical cancer CTV, which is drawn and determined manually according to a professional guide and a process; the distance information image data Dimg refers to a distance information graph between a surrounding adjacent organ and the cervical cancer CTV. The method has the advantages that the anatomical structure position information of the cervical cancer CTV and the surrounding adjacent organs is fully utilized, the anatomical prior position information is obtained, and the accuracy and efficiency of cervical cancer segmentation are improved.

The invention provides a drug combination composition for tumor chemotherapy. The drug combination composition comprises a safe and effective dose of a platinum chemotherapeutic drug and a safe and effective dose of an NAMPT inhibitor. Compared with a single platinum chemotherapeutic drug, the combined drug composition provided by the invention has higher sensitivity to cancers such as liver cancer, gastric cancer, colorectal cancer, lung cancer, cervical cancer, kidney cancer, pancreatic cancer, breast cancer and the like, the effective rate is remarkably improved, and the toxic and side effects are remarkably reduced, so that the combined drug composition has a wide application prospect.

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 belongs to the technical field of medicinal chemistry, and discloses a paeonol mannich base-rhein conjugate as well as a preparation method and application thereof. The conjugate is a compound as shown in a general formula (I) or (II) or a pharmaceutically acceptable salt thereof. Compared with the clinically common antitumor drugs fluorouracil and paeonol, the conjugate provided by the invention has obviously higher in-vitro anti-human hepatoma carcinoma cell (HepG2) and human lung cancer cell (A549) proliferative activity, and the in-vitro anti-human cervical carcinoma cell (Hela), human colon cancer cell (HT29) and human osteosarcoma cell (U2OS) proliferative activity of most compounds is obviously superior to that of fluorouracil and paeonol; and the conjugate has good water solubility, is expected to be applied to treatment of various cancers such as cervical cancer, colon cancer, osteosarcoma, lung cancer or liver cancer, and has a wide clinical application prospect. # imgabs0 #

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.

An oval body adjustable gynecological close-range after-loading source applicator solves the problem that cervical cancer patients who cannot adapt to various vaginal environments in the prior art are prone to errors in radiotherapy doses, leading to tumor control failure and aggravated side effects. It includes an intermediate connecting frame composed of a connecting frame body, characterized in that: the source applicator bend is inserted into the bend installation hole at the upper end of the intermediate connecting frame; the oval body left hemisphere and the oval body right hemisphere symmetrically arranged on both sides of the intermediate connecting frame are both composed of hemispherical shells, and the opposite sides of the two hemispherical shells are respectively provided with concave cavities; the oval body left hemisphere and the oval body right hemisphere are connected to the intermediate connecting frame through an oval body adjustment mechanism. It has a reasonable design and a compact structure, can meet the treatment needs of different types of patients with mid- and late-stage cervical cancer radiotherapy, is easy to operate, can effectively improve the quality of radiotherapy, is non-invasive, has small side effects after treatment, is economical and practical, and has good patient compliance.

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.