20674 results about "Pathology" patented technology

Provided herein are methods, systems, and kits to interrogate a biological sample using templated ligation. The methods disclosed herein identify blood clots in biological samples by detecting mismatched templated ligation probe pairs in areas proximal to and / or within blood clots of biological samples compared to areas without blood clots. The methods disclosed herein also include diagnosing blood clots by detecting the mismatched ligation probe pairs in the biological samples.

Aspects of the disclosure relate to methods of promoting expression or activity of a dystrophin protein and / or methods of treating DMD in a subject. In some embodiments, the methods comprise administering to the subject a composition comprising complexes (e.g., muscle targeting complexes) comprising a phosphorodiamidate morpholino oligomer (e.g., useful for targeting DMD) covalently linked to an antibody (e.g., anti-TfR1 antibody).

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.

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.

Sonodynamic therapy systems and methods use short, high-pressure ultrasound pulses that produce small (and therefore clinically safe) increases (up to 1-2° C.) in tissue temperature but nonetheless activate sonodynamic agents, allowing isolated single-cav-itation events to produce therapeutically beneficial effects (e.g., cytotoxicity) on diseased tissue. An escalating sonication strategy may be employed whereby the focal pressure is gradually increased until maximum clinically tolerable parameters such as the degree of heating of intervening tissue (e.g., the skull when treating brain cancer) or undesired induced cavitation effects are reached.

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 symptom management and prognosis evaluation method and system for a tumor radiotherapy patient, and relates to the technical field of tumor radiotherapy patient management.The method comprises the steps that basic information of the tumor radiotherapy patient is obtained, prognosis evaluation is conducted according to the basic information, a first result is generated, and the risk category is judged according to the first result; obtaining symptom information of a tumor radiotherapy patient, evaluating the symptom severity according to the symptom information, and generating a second result; and performing symptom management according to the second result and the risk category, generating prognosis data, and updating the first result through the prognosis data. According to the invention, by fusing the dual contrast learning network and the dual-path decision tree integrated model, the dynamic association analysis and adaptive feature enhancement of the multi-dimensional medical data of the tumor radiotherapy patient are realized, so that a precise prognosis evaluation and a personalized symptom management scheme are provided for clinical practice.

The invention relates to the technical field of medical data analysis, in particular to a lung cancer dynamic evaluation method and system based on immune monitoring, and the method comprises the following steps: collecting immune index data obtained in the immune monitoring process of a patient, analyzing the mean value change between adjacent segments of an immune index data time sequence, marking an abnormal fluctuation region, and calculating the immune index data; and generating abnormal fluctuation section data. According to the method, the immune monitoring data of the patient are divided into the time sequences, the abnormal fluctuation sections and the abnormal change points are accurately extracted through the mean value change analysis and sliding window method, the remarkable fluctuation of the immune system can be captured more rapidly and accurately, and therefore the timeliness of dynamic monitoring is improved. And further in combination with statistical feature analysis and similarity matrix calculation, differentiated grouping of immune indexes among patients is refined, and personalized evaluation for patient groups with different immune states is facilitated.

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 present invention relates to methods and compositions for the treatment of BAF-related disorders such as cancer.

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 discloses a tea disease detection method and system, and belongs to the technical field of image processing, and the method comprises the steps: constructing a tea disease detection model, inputting an obtained to-be-detected tea disease image into the tea disease detection model, and carrying out the feature extraction of the tea disease image through introducing a residual block of ResNet as a backbone of a backbone network, obtaining a multi-scale feature map of the tea disease image; a self-attention mechanism is introduced into the extracted feature map, multi-scale feature maps of the tea disease image are input into the self-attention mechanism in parallel, and global and local feature maps of the tea disease image are acquired by capturing global and local information of the tea disease image; and fusing the multi-scale, global and local feature maps of the tea disease image to obtain a fused tea disease representation, detecting the tea disease, and outputting a tea disease detection result. The method improves the accuracy of tea disease detection.

The invention discloses an immune cell state analysis method and system, and relates to the technical field of image processing. The method comprises the following steps: acquiring a motion image sequence of immune cells by using microscopic imaging equipment; performing edge detection on the motion image sequence, and marking contour pixel data labels of immune cells; performing trajectory tracking on the immune cells under a time sequence, and outputting motion trajectory pixel data; inputting the motion trail pixel data into a dynamic change rate recognition model, obtaining a first-order change rate, a second-order change rate and a change rate mean value, and generating a dynamic change feature embedding vector; inputting into an immune cell state prediction large model for state prediction to obtain an active state prediction index, and performing active labeling on the immune cells according to the active state prediction index. The technical problems of inaccurate analysis and incomplete prediction of the immune cell activity state in the prior art are solved, and the technical effect of improving the accuracy and comprehensiveness of immune cell activity state prediction is achieved.

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 an intelligent cervical cancer screening method based on a multi-modal large language model, which comprises the following steps: data collection and processing: collecting and processing colposcope images and cervical cancer related medical reports, and pre-training a model for extracting key image and text features and providing input for the multi-modal model to enhance the accuracy and efficiency of cervical cancer screening; setting a multi-modal fusion model; fusing the image features and the text features; model training and tuning: taking the colposcope image and the corresponding text data as input, carrying out joint training on the multi-modal model, and finding an optimal configuration; and model evaluation and verification: carrying out performance evaluation on the model, analyzing the performance of the model on different categories and carrying out clinical verification. According to the method, colposcope images can be efficiently processed and analyzed, cervical cancer screening and diagnosis can be processed, a multi-modal large language model of a medical report can be generated, prognosis prediction, disease progress prediction and risk assessment can be carried out, and doctors can be helped to make more effective diagnosis and treatment plans.

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 of treating patients with chronic thyroid eye disease are provided for herein, such as by administering antibodies and compositions that bind to and / or antagonize IGF-1R.

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 present invention relates to methods of treatment of light chain amyloidosis and other CD38-positive hematological malignancies with anti-CD38 antibodies.