39 results about "Haematoxylin" patented technology

Techniques for using combination stain types for machine learning models for digital pathology are described herein. In an example, a system accesses a first image of a sample comprising an immunohistochemistry (IHC) stain for a biomarker. The system accesses a second image of the sample comprising a hematoxylin and eosin (H&E) stain for nuclei. The system can segment tissue regions in the one or more first images and the second image, partitions the tissue regions in the one or more first images and the second image, and extracts features from the set of tiles using a feature extractor. The system can generate, by a machine-learning model, an output classification indicating a first phenotype based on the features extracted from the set of tiles. The machine-learning model can include one or more classifiers and an aggregation model that provides an aggregated output for the set of tiles.

The present disclosure relates to a method for selection of ovarian cancer patients for treatment with PARP inhibitors This involves 1) preparing whole slide images of tissues of ovarian cancer patient to be treated 2) training and validating AI model built on Resnet-50 to recognize morphological features from tiles of hematoxylin and eosin stained whole slide images of tissues featuring the presence of homologous recombination deficiency as annotated by next generation sequencing 3) using the model to extract morphological features from the tiles of hematoxylin and eosin stained whole slide images of tissues of ovarian cancer patients, generating tile level predictions, aggregating tile level predictions to generate a probability score and selecting the patient for treatment with PARP inhibitors 4) administering PARP inhibitors treatment to the patient selected based on probability score reaching a predetermined threshold. Computer systems to implement the method are also disclosed.

Methods for dyeing suture material that include incubating the suture material in a high temperature staining bath comprising hematein are disclosed. The staining bath may include a nonionic surfactant. The method is carried out in the absence of chromium salts. Also disclosed are (i) a dyed suture material produced by the methods and (ii) a surgical suturing kit that includes the dyed suture material.

The invention also related to a device and a computer-implemented method for generating n virtual Immunohistochemical (IHC) stain images from one Hematoxylin and Eosin (H&E) stain image, using the IHC machine learning system obtained thanks to the device for training.

A method for automatic mitosis detection based on data and feature diversity is provided, in which a training-free hematoxylin-based detection approach is employed to obtain candidate samples; a balanced sampling strategy is applied to remove redundant information from the candidate samples, balance the data volume, and maintain sample diversity and eliminate easy samples, so as to obtain balanced and representative training dataset that facilitate a classifier in learning representative features; in view of the morphological complexity of mitotic cells, a jointly trained classifier is designed, in which subclass division is performed based on binary classification, the divided subclasses are used as pseudo-labels, and the classifier is trained with parent-class labels and subclass pseudo-labels to obtain a parent-subclass joint classifier. A system for implementing such method is also provided.

The application relates to the technical field of medical data processing, and discloses a training method of a pathological image recognition model, a pathological image recognition method and device, the training method of the pathological image recognition model comprising the following steps: obtaining a plurality of histopathological image samples; for part or all of the histopathological image samples, converting a color space into an H&E space to obtain an initial H&E space image, the H&E space comprising two color channels, namely a hematoxylin color channel and an eosin color channel; for the initial H&E space image, using a B-spline curve function to perform dyeing enhancement processing or dyeing weakening processing on at least one color channel to obtain a target H&E space image; and using the histopathological image samples and the target H&E space image to train a pre-constructed histopathological image recognition model. The application can improve the generalization ability of the histopathological image recognition model, and further improve the accuracy.

The application discloses a colorectal cancer HER2 state classification method based on a double-branch cooperative optimization network and belongs to the technical field of pathological image processing. The application adopts a double-path cooperative optimization framework, combines knowledge transfer and cross-modal feature re-projection, efficiently extracts features related to the HER2 state from a conventional hematoxylin-eosin (H&E) staining section, and can directly accurately judge the HER2 state. Through the prior knowledge transfer and cross-modal feature re-projection mechanism, the application breaks through the bottlenecks such as insufficient utilization of prior knowledge and modal heterogeneity interference in the traditional method, and significantly improves the accuracy and robustness of the colorectal cancer HER2 state classification.

To provide a hematoxylin formulation comprising racemic hematoxylin.SOLUTION: A first aspect of the present disclosure is a hematoxylin formulation including racemic hematoxylin and at least one additive (e.g., at least one additive selected from a solvent, a chemical oxidant, a mordant, a stabilizer, and an antioxidant). Another aspect is a method for staining a biological sample, comprising contacting the biological sample with a racemic hematoxylin formulation. In some embodiments, the racemic hematoxylin formulation includes a solvent, hematoxylin, an amount of a chemical oxidant sufficient to convert at least a portion of the hematoxylin into hematein, a mordant, an antioxidant, and a stabilizer. In some embodiments, the stabilizer is selected from the group consisting of a polysaccharide, a cryptand, a cryptophane, a cavitand, a crown ether, a dendrimer, a nanotube, a calixarene, a valinomycin, and a nigericin.SELECTED DRAWING: None

The invention relates to an immunohistochemical detection reagent and kit for bladder cancer neuroendocrine subtype diagnosis and prognosis based on a PHF6 index. The reagent comprises a PHF6 primary antibody, a PHF6 secondary antibody, an antigen repair solution, blocking serum, a DAB developing solution, a hematoxylin staining solution, a hydrochloric acid-alcohol differentiation solution, an ammonia water blue-returning solution and a neutral gum mounting medium. The obvious correlation between the expression level of the PHF6 and the neuroendocrine subtype of the bladder cancer is determined by detecting the expression condition of the PHF6 in the bladder cancer tissue and combining a series of cell experiments and clinical pathological analysis. The invention provides a novel and effective diagnosis means which can help a clinician to accurately judge the neuroendocrine subtype of bladder cancer, so that a more accurate individualized treatment scheme is formulated for a patient, and more reliable prognosis evaluation is provided.

Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.

An apparatus and methods for determining features in an image of a Hematoxylin and Eosin (H&E) stained tissue sample. An apparatus can be configured to apply a machine learning model to the H&E stained tissue sample image to determine features in the image. Applying the machine learning model can include performing convolution operations on the H&E stained tissue sample image to generate a plurality of initial feature maps, applying octave-convolution-first-layer operations on the initial feature maps to generate initial high-frequency feature maps and low-frequency feature maps, applying octave-convolution operations on the high-frequency initial feature maps to generate refined high-frequency feature maps, applying octave-convolution operations on the low-frequency initial feature maps to generate refined low-frequency feature maps, and applying octave-convolution last layer operations on the refined high-frequency feature maps and the refined low-frequency feature maps to produce combined feature maps.

The invention belongs to the technical field of artificial intelligence and digital pathology crossing, and relates to a regulatory T cell infiltration prediction method based on a digital pathological section. According to the method, a conventional hematoxylin-eosin dyed full-section digital image is used as input, and an analysis range is limited through tissue region segmentation and tumor region positioning; extracting multi-scale morphological characteristics of the visual field units in the tumor area, and constructing a graph neural network coding local-global space context; on the premise of taking an adjacent section immunohistochemical quantification or transcriptome immune deconvolution result as a supervision signal, improving the robustness of a cross-staining batch and a cross-scanning condition by adopting regression learning in combination with adversarial regularization and transfer learning; and outputting regulatory T cell infiltration density or grade and thermodynamic diagram visualization, and further giving uncertainty and spatial heterogeneity indexes for review and decision support.

A machine learning predictor model is trained to generate a prediction of the appearance of a tissue sample stained with a special stain such as an IHC stain from an input image that is either unstained or stained with H&E. Training data takes the form of thousands of pairs of precisely aligned images, one of which is an image of a tissue specimen stained with H&E or unstained, and the other of which is an image of the tissue specimen stained with the special stain. The model can be trained to predict special stain images for a multitude of different tissue types and special stain types, in use, an input image, e.g., an H&E image of a given tissue specimen at a particular magnification level is provided to the model and the model generates a prediction of the appearance of the tissue specimen as if it were stained with the special stain. The predicted image is provided to a user and displayed, e.g., on a pathology workstation.

A method of displaying biological image data in an interface application on a digital pathology analysis system includes: receiving a first user input pertaining to a first patient case; contacting a remote server for one or more stored biological image tiles corresponding to the received first user input, wherein the stored biological image tiles are derived from scans of microscope slides having one or more biological samples disposed thereon, the biological samples stained with hematoxylin and eosin or stained to identify presence of one or more biomarkers; receiving location information for the one or more stored biological image tiles corresponding to the first user input; retrieving the one or more stored biological image tiles pertaining to the first user input based on the received location information; and visualizing the retrieved one or more biological image tiles pertaining to the first user input.

A method of displaying biological image data in an interface application on a digital pathology analysis system includes: receiving a first user input pertaining to a first patient case; contacting a remote server for one or more stored biological image tiles corresponding to the received first user input, wherein the stored biological image tiles are derived from scans of microscope slides having one or more biological samples disposed thereon, the biological samples stained with hematoxylin and eosin or stained to identify presence of one or more biomarkers; receiving location information for the one or more stored biological image tiles corresponding to the first user input; retrieving the one or more stored biological image tiles pertaining to the first user input based on the received location information; and visualizing the retrieved one or more biological image tiles pertaining to the first user input.

Methods for dyeing suture material that include incubating the suture material in a high temperature staining bath comprising hematein are disclosed. The staining bath may include a nonionic surfactant. The method is carried out in the absence of chromium salts. Also disclosed are (i) a dyed suture material produced by the methods and (ii) a surgical suturing kit that includes the dyed suture material.

The application belongs to the technical field of artificial intelligence and digital pathology, and relates to a regulatory T cell infiltration prediction method based on digital pathological sections. The method takes a conventional hematoxylin-eosin staining whole section digital image as input, limits the analysis range through tissue region segmentation and tumor region positioning, extracts multi-scale morphological features on a field unit in the tumor region, and constructs a graph neural network to encode local-global spatial context. Under the premise of taking adjacent section immunohistochemical quantification or transcriptome immunodeconvolution results as a supervision signal, regression learning is adopted, combined with adversarial regularization and transfer learning to improve the robustness of cross-staining batches and cross-scanning conditions. The output is the regulatory T cell infiltration density or grade and a heat map visualization, and further uncertainty and spatial heterogeneity indicators can be given for review and decision support.

A computer-implemented method (530), system (100, 201), and computer-readable medium for generating a slide-free histology image in the form of a reconstructed ultraviolet-based autofluorescence microscopy (UV-AtuoM) image from a plurality of speckle-illuminated low-resolution images. A computer-implemented method (1150), system (100, 201), and computer-readable medium for generating a slide-free histology image in the form of a pseudo-hematoxylin and eosin (H&E)-stained image (1120) from a grayscale input image, such as a UV-AutoM image or an ultraviolet-based photoacoustic microscopy (UV-PAM) image (1110).

The present invention discloses the use of a photocurable adhesive for inducing long-lasting emission from organic dyes. The photocurable adhesive is a mixture of one or more of UV-curable adhesive 3491, UV-curable adhesive 3492, UV glue D-606, and UV shadowless adhesive V-3218. The organic dye is a mixture of one or more of safranin, basic fuchsin, Nile blue, hematoxylin, acid fuchsin, fluorescein, and solvent green 7. By mixing the photocurable adhesive with a conventional organic dye, the present invention can induce long-lasting emission from the conventional organic dye.

Methods for dyeing suture material that include incubating the suture material in a high temperature staining bath comprising hematein are disclosed. The staining bath may include a nonionic surfactant. The method is carried out in the absence of chromium salts. Also disclosed are (i) a dyed suture material produced by the methods and (ii) a surgical suturing kit that includes the dyed suture material.

The invention discloses application of cryptotanshinone in preparation of a medicine for treating vascular endothelial cell injury, and belongs to the technical field of medicines. The application of the cryptotanshinone in preparing the medicine for treating the vascular endothelial cell injury is provided for the first time, a new treatment scheme is provided for the vascular endothelial cell injury, and the pharmaceutical application of the cryptotanshinone is expanded. According to the application disclosed by the invention, by culturing human umbilical vein endothelial cells, the influence of the P. gingivalis-sourced lipopolysaccharide on an HUVEC transcriptome is evaluated; constructing an in-vivo lung injury rat model, and utilizing hematoxylin amp; the protection effect of cryptotanshinone on pulmonary edema and pathological injury of rats is evaluated through methods of porphyromonas gingivalis infection, eosin staining, blood gas analysis and the like, and an experimental basis is provided for vascular endothelial cell injury and dysfunction caused by porphyromonas gingivalis infection.

Histologic classification of pathology specimens through machine learning is a nascent field which offers tremendous potential to improve cancer medicine. Its utility has been limited, in part because of differences in tissue preparation and the relative paucity of well-annotated images. We introduce tissue recognition, an unsupervised learning problem analogous to human face recognition, in which the goal is to identify individual tumors using a learned set of histologic features. This feature set is the “tissue fingerprint.” Because only specimen identities are matched to fingerprints, constructing an algorithm for producing them is a self-learning task that does not need image metadata annotations. Here, we provide an algorithm for self-learning tissue fingerprints, that, in conjunction with color normalization, can match hematoxylin and eosin stained tissues to one of 104 patients with 93% accuracy. We applied this identification network's internal representation as a tissue fingerprint for use in predicting the molecular status of an individual tumor (breast cancer clinical estrogen receptor (ER) status). We describe a fingerprint-based classifier that predicts ER status from whole-slides with high accuracy (AUROC=0.90), and is an improvement over traditional transfer learning approaches. The use of tissue fingerprinting for digital pathology as a concise but meaningful histopathologic image representation enables a new range of machine learning algorithms leading to increased information for clinical decision making in patient management.

The present disclosure provides stabilized hematoxylin formulations having a pH of less than 2.4. The present disclosure also provides methods of using such stabilized hematoxylin formulations to stain biological samples.

The invention discloses a preparation method of a hematoxylin imprinted nano material based on an ultralow-density magnetic carrier. The preparation method comprises the following steps: firstly, preparing a nano magnetic hollow carrier by utilizing a pore-foaming agent and a solvothermal method, and then carrying out boric acid functional modification on the surface of the carrier through chemical modification. And finally, adding the nano boric acid nano magnetic hollow carrier and hematoxylin into a solvent for prepolymerization, then adding a cross-linking agent and a catalyst, and preparing the hematoxylin imprinted material by using a surface molecular imprinting technology. The hematoxylin imprinted material prepared by the invention has an ultralow-density magnetic carrier, shows excellent magnetic response enrichment capacity, has excellent specific recognition capacity and efficient adsorption capacity on hematoxylin, and can be used for rapid, efficient and selective adsorption and detection of hematoxylin in a complex environment.

The invention also relates to a device and method for generating n virtual Immunohistochemical (IHC) stain images from one Hematoxylin and Eosin (H&E) stain image, using a IHC machine learning system trained using the training parameters obtained thanks to said device for training.

This invention provides a method for detecting spatial proteomics and spatial phosphorylation modification omics. The method includes the following steps: (1) obtaining two consecutive sections of the target tissue and staining the sections with hematoxylin-eosin to obtain stained sections; (2) using a 3D printing mold to cut the stained sections into continuous, gapless strip-shaped subunits; (3) extracting peptides from the strip-shaped subunits and enriching the peptides by phosphorylation modification; (4) performing chromatographic separation and mass spectrometry detection on the phosphorylation-enriched peptides to obtain one-dimensional omics information of each strip-shaped subunit; (5) using the image of the stained sections as a spatial reference, reconstructing the one-dimensional omics information into a two-dimensional spatial distribution to obtain the spatial distribution of the target omics biomolecules. The method of this invention only requires two tissue sections to simultaneously obtain two-dimensional spatial proteomics and spatial modification omics results, which is friendly to small clinical samples.

The invention relates to the technical field of medical research, in particular to a method for evaluating the invasive growth mode of hepatocellular carcinoma based on multiple pathological staining technologies, which comprises the following steps: taking liver tissue, pretreating a specimen, preparing a slice, and obtaining a specimen slice; dyeing the specimen sections by using hematoxylin-eosin dyeing, and analyzing the dyeing condition; the method comprises the following steps: respectively carrying out masson staining and specific immunohistochemical staining on a key junction region of a specimen section on the basis of a staining condition, and evaluating an invasive growth mode of hepatocellular carcinoma, so that the invasive growth mode of hepatocellular carcinoma is evaluated by carrying out multi-dimensional observation and analysis on the junction region of hepatocellular carcinoma tissue and multiple adjacent structures; three pathological technologies of basic morphology, fibrosis evaluation and specific cell localization immunohistochemical staining are combined, mutual verification is carried out, the limitation of a single technology is made up, and a new pathological basis is provided for accurately knowing the growth characteristics and invasion potential of hepatocellular carcinoma and formulating an individualized diagnosis and treatment strategy.

The present application discloses a method, apparatus, electronic device and storage medium for detecting angiopoietin, relating to the technical field of image processing. The method includes: obtaining hematoxylin-eosin staining pathological images of tissues of different hepatocellular carcinoma patients; segmenting the cancer tissue regions in each pathological image and randomly selecting them as sub-images; extracting multiple features of each sub-image and screening out the optimal feature subset from each feature; constructing a pathological omics model based on the optimal feature subset; using the pathological omics model to calculate the angiopoietin expression probability of the corresponding hepatocellular carcinoma patient tissue according to the features, and detecting the expression level of angiopoietin according to the angiopoietin expression probability. By obtaining rich features of pathological images and then constructing a pathological omics model to predict the corresponding angiopoietin expression probability for each feature, the expression level of angiopoietin can be objectively and truly detected based on the pathological images, improving the detection accuracy of angiopoietin.

The invention provides pogostemon cablin homogeneous polysaccharide PC-B-1 as well as a preparation method and application thereof, and belongs to the technical field of biological medicines. The invention provides pogostemon cablin homogeneous polysaccharide PC-B-1 and application thereof in preparation of a medicine for treating ulcerative colitis. A DSS-induced mouse ulcerative colitis model proves that PC-B-1 can significantly improve disease symptoms, including inhibition of weight loss, relieving of colon shortening and regulation of immune organ indexes. Histopathologic analysis (hematoxylin-eosin staining and alsine blue-periodate Schiff's staining) further shows that the PC-B-1 can effectively relieve inflammatory infiltration of the colonic mucosa and promote repair of goblet cells and mucus. The polysaccharide PC-B-1 with a novel structure is separated and identified from pogostemon cablin, and it is proved that the polysaccharide PC-B-1 has remarkable anti-inflammatory and intestinal mucosa protection activity and is expected to be developed into drugs for treating ulcerative colitis.