14372 results about "Pathology" patented technology

A method of preparing sterile human placental allografts by providing a human placental tissue from a donor within 24 hours to 72 hours post-childbirth; removing any visible blood, blood clots, and / or blood components from the human placental tissue without scraping or scrubbing the human placental tissue to preserve structural integrity of the human placental tissue; washing the human placental tissue in an isotonic solution while maintaining the structural integrity of the human placental tissue; dehydrating the human placental tissue thereby forming the dehydrated human placental tissue; resizing the dehydrated human placental tissue into dehydrated human placental tissue portions having predetermined sizes; and sterilizing the dehydrated human placental tissue portions of step (e) thereby forming the sterile human placental allograft. Also disclosed is a sterile human placental allograft produced by the method.

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

This document generally describes methods and systems for generating digital reconstructions of tissue from humans or other species. The method can, for example, include receiving, at a computing system, image data of a tissue sample, where one or more sections of the tissue sample are stained with hematoxylin and eosin (H&E), registering the image data to generate registered image data, identifying tissue subtypes based on application of a machine learning model to the registered image data, annotating the identified tissue subtypes to generate annotated image data, and determining a digital volume of the tissue sample in three dimensional (3D) space based on the annotated image data. The disclosed technology can provide for single-cell analysis and other analysis of tissue samples, such as early detection of cancer in human tissue samples.

Disclosed is a system for detecting whether the breast cancer is benign or malignant based on multiple modalities, which comprises: an ultrasonomics model configured to extract breast tumor position information and breast tumor morphological features on the basis of an acquired grayscale breast ultrasound image, perform global interactive fusion on the breast tumor position information and breast tumor morphological feature information, and convert a Transformer encoder-based feature output into an output vector of the corresponding classification dimensions by means of a fully connected layer; a metabonomics model configured to obtain a metabolic fingerprint spectrogram and analyze the metabolic fingerprint spectrogram on the basis of a metabolic database and a predetermined significantly differential metabolite to obtain abnormal metabolic pathways, signal transduction pathways, and related biochemical reactions associated with breast cancer malignancy; and a detection module configured to perform joint modeling, based on the breast tumor position information, the breast tumor morphological features, and a peak value of the significantly differential metabolite of the breast cancer malignancy, to output a prediction result about whether the breast cancer is benign or malignant.

The invention discloses a bovine serum albumin combined nano antibody and a preparation method and application thereof, the amino acid sequence of the bovine serum albumin combined nano antibody Nb1 is shown as SEQ.ID No.1, the nano antibody Nb not only has high affinity to bovine serum albumin, but also has good binding activity to both human serum albumin and mouse serum albumin, and the nano antibody Nb has high affinity to human serum albumin and mouse serum albumin. And the antibody has a relatively good broad-spectrum application range, and can be widely applied to the aspects of drug half-life period enhancement, tumor targeted therapy and imaging, immunodetection or diagnosis, recombinant albumin purification or enrichment and the like.

The invention discloses an effect evaluation method for a non-dialysis diabetic kidney disease, which comprises the following steps: monitoring the change trend of data by collecting renal function related data of a patient, and generating a basic data set; a dynamic nonlinear weighted regression algorithm is adopted to analyze the multi-dimensional renal function data, data weights are dynamically adjusted, and influence weights of different time periods are distributed according to the change mode of the illness state; the data influence is adjusted in real time through a time correlation mechanism, and a disease assessment curve is generated; according to the method, historical data and real-time data are combined, a patient condition trend line and a prediction result are generated, personalized treatment feedback is output, and intervention suggestions are provided for clinicians based on long-term prognosis prediction. The method is suitable for disease monitoring and treatment feedback of patients with non-dialysis diabetic kidney diseases.

Aspects of the disclosure relate to methods of reducing fatigue in a subject having myotonic dystrophy type 1 (DM1). Aspects of the disclosure relate to methods of treating one or more symptoms assessable by the MDHI (e.g., a GI symptom, myotonia, upper extremity function impairment, fatigue, mobility impairment, impairment in the ability to perform activities, pain, vision impairment, communication impairment, sleep impairment, emotional issues, cognitive impairment, social satisfaction impairment, social performance impairment, breathing impairment, swallowing impairment, and / or hearing impairment) in a subject having myotonic dystrophy type 1 (DM1). In some embodiments, the methods comprise administering to the subject a composition comprising complexes (e.g., muscle targeting complexes) comprising an oligonucleotide (e.g., a DMPK- targeting oligonucleotide) covalently linked to an antibody (e.g., anti-TfRl antibody).

The invention discloses a liver disease image recognition processing method based on multi-modal fusion, a medium and equipment. The method comprises the following steps: acquiring multi-modal medical data in a liver disease diagnosis process; performing cross-modal feature extraction on the multi-modal medical data to obtain blood flow-image associated features; constructing a physiologically constrained space-time alignment model according to the blood flow-image correlation features, and outputting a synchronous mapping relationship between the blood vessel geometric topology network and the blood flow dynamic parameters; inputting the synchronous mapping relation into a liver disease grading classifier to generate a liver disease grading prediction result; and according to a prediction result, generating output content of multi-modal data visualization and displaying the output content. According to the method, effective fusion and collaborative analysis of multi-modal medical data are realized, and the accuracy and clinical practicability of liver disease grading are further improved.

The invention relates to the technical field of diagnosis, in particular to a diagnosis and treatment auxiliary system and method for the obstetrics and gynecology department, and the system comprises a parameter collection module, a feature offset recognition module, a feature reconstruction module, a collaborative trend recognition module and a diagnosis and treatment suggestion generation module. According to the system and the method, the body temperature, the heart rate, the pulse, the blood pressure and the image edge density are synchronously acquired and the timestamps are bound, so that a structured diagnosis and treatment data unit is formed, the time sequence comparison capability of multi-source information is effectively improved, and potential risk points can be positioned and feature tags can be constructed based on a numerical value offset identification and synchronous index combined judgment mechanism; according to the method, physical signs, images and laboratory data are normalized and fused into three-dimensional vectors, cross-dimensional feature linkage expression is achieved, the dynamic insight ability to pathological state changes is improved by screening features with large variation amplitude and long duration time and judging trend consistency, and the accurate response to the fetal risk state is enhanced by combining a specific trend combination matching rule.

Systems and methods are disclosed for diagnosis, risk assessment, and / or virtual treatment assessment of visceral ischemia and related disorders. One method includes receiving a patient-specific anatomic model of a patient's visceral vasculature, including visceral vasculature of the patient's visceral organs and bowel; determining a location in the patient-specific anatomic model of the patient's visceral vasculature; determining, for the location in the patient-specific anatomic model, a blood flow characteristic of blood flow through the location in the patient-specific anatomic model of the patient's visceral vasculature; determining a tissue region of the patient's bowel proximate the location in the patient-specific anatomic model of the patient's visceral vasculature; and generating an assessment of blood supply adequacy to the tissue region of the patient's bowel based on the determined blood flow characteristic and an expected blood flow characteristic associated with the tissue region of the patient's bowel.

Aspects of the disclosure relate to compositions comprising a plurality of complexes comprising an antibody (e.g., anti-TfRI antibody) covalently linked to one or more oligonucleotides (e.g. a DMPK targeting oligonucleotide), each oligonucleotide being covalently linked at a linkage site represented by a lysine (K) residue of the antibody. In some embodiments, the antibody comprises a heavy chain comprising a heavy chain variable region (VH) and a heavy chain constant region, and a light chain comprising a light chain variable region (VL) and a light chain constant region, wherein at least 80% (e.g., 80%-98%, 80%-95%, 80%-90%, 85%-98%, 85%-95%, 85%-90%, 90%-98%, 90%-95%, 95%-97%, or more) of the light chain constant regions of the antibodies of the complexes in the composition are independently covalently linked to an oligonucleotide at a linkage site represented by K188 (based on Kabat numbering) and / or a linkage site represented by K190 (based on Kabat numbering) of the light chain constant regions of the antibodies.

Aspects of the disclosure relate to methods of reducing expression or activity of DMPK (e.g., reducing the level of a mutant or wild-type DMPK RNA, or the activity of a DMPK gene product) and / or methods of treating myotonic dystrophy (e.g., DM1) in a subject. In some embodiments, the methods comprise administering to the subject a composition comprising complexes (e.g., muscle targeting complexes) comprising an oligonucleotide (e.g., a DMPK—targeting oligonucleotide) covalently linked to an antibody (e.g., anti-TfR1 antibody).

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

Provided are ex vivo systems and methods of predicting the response of a drug or other agent on a tissue. In some embodiments, the systems and methods comprise cutting a tissue into tissue fragments, adding a drug or other agent to the tissue fragments based on an estimated tumor content, and performing an ex vivo measurement on the tissue fragments.

Methods for treating tissue with irreversible electroporation and immunotherapy are described. The methods include placing a probe in tissue within a human body, wherein the probe has at least a first electrode, applying a plurality of electrical pulses through the first electrode and a second electrode, causing irreversible electroporation (IRE) of the tissue within a target ablation zone, and administering one or more exogenous agents into the tissue within the target ablation zone or to the human, thereby stimulating or otherwise modulating an immune system response within the body.

This specification describes systems and methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients. The methods and systems are configured for using machine learning to determine measurements of various characteristics related to IBD. The methods and systems may also obtain and incorporate electronic health data of patients along with endoscopic data to use for scoring purposes.

Methods, apparatus, systems, and articles of manufacture are disclosed for generation and application of models for therapeutic prediction and processing. A balance of precision and recall can be applied to at least one of a toxicity-related model or an efficacy-related model to configure immunotherapy treatment of a patient and / or cohort of patients. Model output can be evaluated differently depending on a determine patient selection criterion to trigger different actions.

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.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The present invention relates to an immunomicrobubble complex and its uses. The immunomicrobubble complex (IMC) according to the present invention comprises microbubbles conjugated with an antibody, wherein the microbubbles have excellent stability and excellent antibody binding strength, and it has been confirmed that when the immunomicrobubble complex is treated with high-intensity focused ultrasound (HIFU), the anti-tumor effect is significantly enhanced and an immune-enhancing effect is exhibited. Therefore, the immunomicrobubble complex according to the present invention is expected to improve the delivery efficiency of the conjugated antibody and can be used for the diagnosis and treatment of cancer, and exhibits various functions in the field of immunotherapy, including a contrast effect, improvement of half-life, improvement of drug delivery, lymphocyte concentrating effect, cancer immunotherapy, and ultrasound-induced immunotherapy.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention discloses a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model, belongs to the field of cervical cell image recognition, and particularly relates to the cervical cell image intelligent diagnosis system based on the multi-modal visual language large model. In order to solve the problems of low diagnosis accuracy, strong subjectivity, insufficient efficiency and lack of interpretation in the prior art, the invention provides a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model. The system comprises an image block acquisition module, an image preprocessing and color normalization module, an effective tissue region screening module, a cell region extraction and post-processing module, an image end and text end processing module, a visual language large model acquisition and training module, a cell description text generation module in an inference stage, and a risk judgment and classification module. An interpretability verification and credibility evaluation module; and a structured diagnosis report output module.

Disclosed are systems and methods for identifying prostate cancer patients at high-risk of progression among clinically intermediate risk group. Images of patient cells are obtained and tiled into subsets of smaller images. Using a trained machine learning model, a morphology quantification process is performed on the subsets of smaller images. Portions of the images are input into the trained machine learning models. The trained machine learning model determines the occurrence of likely cancer cells and classifies the cancer cells with a grading. The system then uses this out of the machine learning model to identify whether the cells in the subsets of smaller images indicate whether a prostate cancer patient is at a risk of progression among a clinically intermediate risk group.

The present invention provides methods of screening for compounds that interfere with the interaction between ERG. ETV1, ETV4 or ETV5 and mSWI / SNF (BAF) chromatin remodeling complex proteins. Included are methods of screening for compounds that interfere with the interaction between ERG, ETV1, ETV4 or ETV5 and BAF155. Methods of treating prostate cancer with compounds that interfere with the interaction between ERG, ETV1, ETV4 or ETV5 and mSWI / SNF (BAF) chromatin remodeling complex proteins are also provided.

According to some embodiments, a method of promoting hair growth or hair stimulation in a subject comprises applying vacuum or suction using a handpiece assembly along a targeted portion of the subject's skin surface where hair growth or hair stimulation is desired and providing at least one treatment material to said targeted portion of the subject's skin surface, wherein the application of vacuum or suction helps promote hair growth or stimulate hair.

This disclosure provides a method for treating a subject afflicted with a lung cancer, which method comprises administering to the subject therapeutically effective amounts of: (a) an antibody or an antigen-binding portion thereof that specifically binds to a Programmed Death-1 (PD-1) receptor and inhibits PD-1 activity; and (b) an antibody or an antigen-binding portion thereof that specifically binds to a Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4) and inhibits CTLA-4 activity.

The invention relates to the technical field of prognosis analysis, in particular to a pancreatic cancer prognosis analysis system based on big data mining, which comprises a jump trend extraction module, an index grade mapping module, a sample stability screening module, a path node offset identification module and a prognosis stage classification module. According to the method, a change rate difference value and a third-order response sequence are constructed for continuous three-stage pancreatic cancer biological index sequences, jump trend feature points are extracted, and a risk grade transition sequence is formed in combination with grade span and direction judgment; multi-dimensional normalized parameters of a variable coefficient, a survival score difference value and an organ function score standard deviation are introduced to perform stable sample screening, interference of data disturbance on a path judgment result is reduced, and path mutation node positions are screened in combination with a path stability threshold value; precise classification and marking of high-risk variation stages in prognosis paths of pancreatic cancer patients are realized, stage reference is provided for individualized intervention strategies, and risk identification sensitivity and layered intervention scientificity are improved.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention provides a family blood pressure measurement and identity recognition system and method based on oscillatory wave characteristics, and relates to the technical field of household medical equipment. The method comprises the following steps: firstly, acquiring original oscillatory wave signals during blood pressure measurement, judging whether the signals are available or not, further fusing time domain and frequency domain features to perform quality evaluation on available oscillatory wave signals, and screening high-quality signals; filtering and noise reduction preprocessing is carried out on the oscillatory wave signals with good quality; then respectively extracting time domain, frequency domain and time-frequency joint features from the preprocessed oscillatory wave signal to obtain multi-index features of the oscillatory wave signal; carrying out standardization processing and unsupervised feature fusion on the multi-index feature data; finally, non-invasive blood pressure measurement and identity recognition are completed. According to the method, the oscillation wave signals are subjected to fusion analysis according to the multi-index characteristics, non-invasive blood pressure measurement is achieved, the accuracy and calculation efficiency of identity recognition are enhanced, historical blood pressure measurement results of fixed members are conveniently stored, and a family blood pressure monitoring system is constructed.

The invention discloses a group of gene markers for predicting or evaluating neoadjuvant chemotherapy response of breast cancer and application of the gene markers. The gene markers comprise CCNA2, CCND1, CD52, ESR1 and FAM234B. The system for predicting or evaluating the neoadjuvant chemotherapy reaction of the breast cancer is constructed on the basis of the gene marker, and can accurately predict or evaluate whether the breast cancer patient has the risk that complete remission cannot be achieved after neoadjuvant chemotherapy, and discriminate whether the breast cancer patient can benefit from the neoadjuvant chemotherapy reaction; therefore, whether the breast cancer patient can be subjected to neoadjuvant chemotherapy or not is accurately predicted, medical intervention is performed in time, and the method has important clinical reference value.