262 results about "Autofluorescence" patented technology

A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled / label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

Detection of microplastics is accomplished using a combination of techniques. A position-detection technique such as crossed-polarization detection, autofluorescence detection, or photothermal infrared imaging is used to determine the locations of microplastics in a sample. Infrared absorption can be detected at those locations to characterize the microplastics. In this way the microplastic content can be located and characterized more quickly and accurately than using conventional techniques.

The invention discloses a reversible background-free hydrogel nanosensor for acetone detection and an intelligent sensing platform, and relates to the technical field of biosensors. The hydrogel nanosensor consists of a hydrogel matrix and an upconversion nanoprobe, and the upconversion nanoprobe comprises upconversion nanoparticles (UCNPs), hydroxylamine sulfate and thymol blue (TB). The invention adopts hydroxylamine sulfate as a specific recognition unit of acetone to construct a reversible luminescent hydrogel probe for acetone detection, and the probe can be reused in multiple detections, thereby reducing the cost of a single detection. In addition, by adopting near-infrared light as an excitation light source, the autofluorescence interference of the hydrogel matrix is ​​effectively avoided, thereby improving the detection accuracy.

The invention discloses a reversible background-free hydrogel nanosensor for acetone detection and an intelligent sensing platform, and relates to the technical field of biosensing, the hydrogel nanosensor is composed of a hydrogel matrix and an up-conversion nanoprobe, the up-conversion nanoprobe comprises up-conversion nanoparticles (UCNPs), hydroxylamine sulfate and thymol blue (TB); according to the present invention, hydroxylamine sulfate is adopted as the specific recognition unit of acetone to construct the reversible luminescent hydrogel probe for acetone detection, and the probe can be repeatedly used in multiple detections so as to reduce the cost of the single detection; in addition, by adopting near-infrared light as an excitation light source, autofluorescence interference of the hydrogel matrix is effectively avoided, and the detection precision is improved.

A system for acquiring data regarding a wound in tissue comprises at least one excitation light source configured to directly illuminate a wound with excitation light; a spectral filtering mechanism configured to permit passage of optical signals responsive to illumination of the wound and having at least one wavelength corresponding to bacterial, fungal, and / or other microorganism autofluorescence and / or bacterial, fungal, viral, and / or microbial fluorescence, the spectral filtering mechanism including a plurality of selectable filters respectively corresponding to different discrete spectral bandwidths; an optical sensor configured to detect the spectrally filtered signals; and a processor configured to receive the detected, filtered signals and to identify a fluorescent signature of bacteria in the wound based at least in part on the detected, filtered signals and to output data regarding the bacterial, fungal, viral, and / or microbial fluorescent signature.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention relates to the technical field of crack image analysis, in particular to a building crack width measuring method based on image analysis, which comprises the following steps of: acquiring an autofluorescence attenuation image of a building surface under ultraviolet excitation, and generating a crack response coefficient matrix according to a material aging degree; driving micro-area pulse thermal imaging based on the crack response coefficient matrix, and extracting a heat conduction abnormal boundary as a crack physical edge; surface attachment interference is eliminated through an edge acoustic impedance matching model, and the real width value of the crack is calculated under a material intrinsic coordinate system. According to the method, the normal width of the crack is measured through uniform point selection in the main axis direction, the measured value is subjected to weighted fusion in combination with the local acoustic impedance matching weight, the obtained width result can better reflect the actual structural integrity and elastic continuity of the crack, geometric quantities are provided, and the local damage degree is reflected; the method is suitable for subsequent structure analysis and safety evaluation.

The invention relates to the technical field of tumor detection, and discloses a tumor detection method and system and computer equipment, and the method comprises the following steps: obtaining autofluorescence decay data of a target biological tissue, and extracting fluorescence intensity and fluorescence lifetime data from the autofluorescence decay data; performing data fusion on the fluorescence lifetime data and the fluorescence intensity data of the target biological tissue to obtain dual-channel fusion data; and inputting the dual-channel fusion data into a tumor detection model pre-trained based on a deep learning network model, outputting the prediction probability of each pixel corresponding to different categories, taking the category with the highest probability value as the tumor detection result of each pixel, and obtaining the distribution positions of tumors and blood vessels in the target biological tissue. According to the method, the fluorescence lifetime data and the fluorescence intensity data are fused and then input into the tumor detection model, so that the model can analyze tissue characteristics from richer and more comprehensive dimensions, the distribution positions of tumors and blood vessels in target biological tissues can be more accurately positioned, and the detection accuracy and the recognition speed are improved.

The invention discloses aggregation-induced emission microspheres with colorimetric-fluorescence double signals as well as a preparation method and application of the aggregation-induced emission microspheres. The aggregation-induced emission microsphere comprises a carboxyl polystyrene microsphere and an aggregation-induced emission dye embedded in the carboxyl polystyrene microsphere, the aggregation-induced emission microsphere has colorimetric-fluorescence double signals, is red under natural light, has strong absorption at 600nm, shows high luminous efficiency at 725nm, not only effectively avoids the interference of biological autofluorescence and reduces background interference, but also has good light stability and photobleaching resistance, and can be used for preparing the aggregation-induced emission microsphere. And due to the high molar extinction coefficient, the colorimetric and fluorescent double-signal output capability is realized at the same time. In addition, the aggregation-induced emission microspheres are high in reproducibility in the preparation process, have the potential of large-scale synthesis and meet the requirements of industrial batch production.

A system and method for analyzing a tissue is provided. The system includes an excitation light unit, a probe, a photodetector, and a system controller. The excitation light unit produces excitation lights. The probe has a flexible cable and a probe head. The flexible cable includes a plurality of light source optical fibers and a light receiving conveyance structure. The probe head receives the excitation lights and produces a distribution of incident light. The photodetector detects the autofluorescence emission, or the diffuse reflectance signal, or both from the tissue and produces signals representative thereof. The photodetector is in communication with the light receiving conveyance structure. Instructions when executed cause the system controller to control the excitation light unit, receive and process the signals from the photodetector, and produce an image representative of the signals produced by the excitation light application.

The invention belongs to the technical field of microbiological detection, and discloses a composite probe for detecting salmonella enteritidis and a preparation method. The composite probe for detecting the salmonella enteritidis comprises magnetic nanoparticles FeO, wherein the surface of the magnetic nanoparticles FeO is modified with an aptamer apt which is specifically combined with the salmonella enteritidis; the surface of the long afterglow nano particle PLNPs is modified with a DNA (Deoxyribose Nucleic Acid) sequence cDNA (Complementary Deoxyribonucleic Acid) complementary with the aptamer; the aptamer apt and the complementary DNA sequence cDNA are subjected to hybridization to form a composite probe structure FeO-SEapt (at) PLNPs-cDNA (complementary deoxyribonucleic acid). The long afterglow luminescence characteristic of PLNPs (ZnGaO: Cr) is utilized, and an excitation light source is stopped before detection, so that signal acquisition completely avoids autofluorescence of a sample matrix, background fluorescence interference is thoroughly eliminated, and the signal-to-noise ratio is remarkably improved.

Provided herein are systems and methods for tissue characterization that may be utilized for intraoperative cancer margin assessment. A tissue sample is resected from a patient and then placed in a system where it is imaged for autofluorescence characteristics to help identify regions of interest such as cancerous tissue margins. Based on the imaging results and tissue characterization, the resection of tissue in the subject can be adjusted.

The invention discloses a double-gating optical detection method and system. The method comprises the following steps: generating an acquisition signal through a signal triggering module; controlling a camera module to acquire first fluorescence information in a first time gating and acquire second fluorescence information in a second time gating according to the acquisition signal; and the signal processing module is controlled to calculate and process the first fluorescence information and the second fluorescence information to obtain a ratio image. First fluorescence information is collected through first time gating, interference of autofluorescence of biological tissue on short-life fluorescence of probe molecules is avoided, then second fluorescence information is collected through second time gating, and then ratio calculation is performed according to the first fluorescence information and the second fluorescence information to obtain a ratio image. Therefore, fluorescence signals of the probe molecule and the reference molecule are distinguished, the accuracy of optical imaging in quantitative detection is improved, and the method can be widely applied to the technical field of detection.

A system for acquiring data regarding a wound in tissue comprises at least one excitation light source configured to directly illuminate a wound with excitation light; a spectral filtering mechanism configured to permit passage of optical signals responsive to illumination of the wound and having at least one wavelength corresponding to bacterial, fungal, and / or other microorganism autofluorescence and / or bacterial, fungal, viral, and / or microbial fluorescence, the spectral filtering mechanism including a plurality of selectable filters respectively corresponding to different discrete spectral bandwidths; an optical sensor configured to detect the spectrally filtered signals; and a processor configured to receive the detected, filtered signals and to identify a fluorescent signature of bacteria in the wound based at least in part on the detected, filtered signals and to output data regarding the bacterial, fungal, viral, and / or microbial fluorescent signature.

According to the OLED diagnosis and treatment integrated system and method for photodynamic precise treatment, treatment light field customization and regulation and control functions based on an OLED are achieved, light field distribution is regulated and controlled through fluorescence feedback, the optimal treatment effect is achieved, and preoperative detection and intraoperative monitoring of photosensitizer fluorescence and autofluorescence are supported. The phototherapy module uses an OLED flexible panel to carry out phototherapy, an OLED is divided into a plurality of independent small square blocks, each small square block is provided with a hole for embedding a fluorescent module, and other parts are used for phototherapy; the fluorescence module acquires a fluorescence signal at the focus; the user module communicates with the control module through the communication module; the control module acquires user module information and fluorescence signals, controls the driving module to realize light field customization and regulation, judges a focus range according to fluorescence distribution, adjusts illumination intensity according to fluorescence intensity, and realizes precise treatment of the focus; the power supply module supplies power to the diagnosis and treatment integrated system.

A device for measuring intrinsic autofluorescence of molecules present in a biological sample wherein the device includes: a) a light-sheet, virtual light-sheet or light line delivery pathway module including a laser generator capable of generating ultrashort pulses with a duration between 1 microsecond and 1 attosecond, and optical elements shaping the laser light into a light sheet, virtual light-sheet or light-line; b) an imaging chamber including an imaging dish inside of which the biological sample is placed, a translational stage, an illumination objective and a collection objective both immersed in an immersion liquid in a sealed immersion chamber, wherein both objectives are positioned below the imaging dish, wherein thes immersion chamber is placed inside an incubation chamber, allowing environmental control; and c) a fluorescence detection pathway including optical elements, light-splitting elements such as diffractive elements or dichroic mirrors, an array detector, and a device control and signal processing unit.

A machine learning (ML) workflow for virtual staining and computational pathology based on a label-free tissue sample is provided to avoid a laborious and time-consuming procedure of pathologist-based interpretation of formalin-fixed and paraffin-embedded histology. A virtual-staining ML model translates a label-free autofluorescence (AF) whole slide image (WSI) of the tissue sample into a virtual hematoxylin and eosin (H&E) -stained WSI, which is further transformed into a WSI virtually stained with a special stain by a stain-transformation ML model. Each of the ML models can be realized as a U-Frame model. A multiple instance learning (MIL) network with attention-based pooling can be applied to the virtual H&E-stained WSI or AF WSI to provide fast and accurate tumor diagnosis. Furthermore, the attention-based MIL network can be configured as a clustering-constrained-attention multiple instance learning (CLAM) model for providing multiclass tumor subtyping, or even multiclass disease typing, in diagnosis of the tissue sample.

A deep learning-based digital staining method and system are disclosed that enables the creation of digitally / virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally / virtually-stained whole slide images (WSIs) of unlabeled / unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample. This label-free digital staining method eliminates cumbersome and costly histochemical staining procedures and significantly simplifies tissue preparation in pathology and histology fields.

A fluorescence lifetime detection system and a detection method. The fluorescence lifetime detection system includes: a labeling unit for providing quantum dots and for labeling a detection object with the quantum dots; a light emitting unit for providing excitation light, the excitation light being projected onto the detection object labeled with the quantum dots, and the quantum dots being capable of generating fluorescence; a photodetector for detecting the fluorescence generated by the quantum dots and collecting fluorescence signals; and a time-correlated single photon counting unit for obtaining time-based fluorescence lifetime data based on the fluorescence signals. The quantum dots can exhibit different fluorescence lifetimes according to the different microenvironments of the detection object itself. Thus, by detecting the fluorescence generated by the quantum dots, the microenvironment of the detection object can be detected according to the fluorescence lifetime of the quantum dots. Moreover, compared with the autofluorescence of the detection object, the fluorescence lifetime of the fluorescence generated by the quantum dots is longer, and the fluorescence signal obtained by detecting the fluorescence generated by the quantum dots is stronger, which can reduce the detection difficulty and improve the detection accuracy.

Methods of obtaining a stained image of a biological sample using a single channel label -free fluorescence lifetime image of the sample comprising a fluorescence intensity image and associated fluorescence lifetime image as described. The methods comprise obtaining a single channel composite autofluorescence intensity and lifetime image from the single channel label -free fluorescence lifetime image, and providing the single channel composite autofluorescence intensity and lifetime image as input to a deep learning model that has been trained to take as input a single channel composite autofluorescence intensity and lifetime image and provide as output a corresponding stained image. Related methods, systems and products are also described.

Techniques for implementing image normalization for multispectral fluorescence microscopy and virtual staining are disclosed. In an example method, a computing device receives, from an imaging device of a first imaging device type, a first autofluorescence image of a first tissue sample, the first autofluorescence image including one or more imaging channels. The computing device determines one or more normalization parameters for a first channel of the one or more imaging channels, the one or more normalization parameters for the first channel based on a first relationship between the first imaging device type and a second imaging device type, the second imaging device type being different from the first imaging device type. The computing device applies the one or more normalization parameters for the first channel to the first channel of the first autofluorescence image. The computing device outputs the normalized first channel of the first autofluorescence image.

The invention provides a construction method of a plant tissue space transcriptome sequencing library, which comprises the following steps: fixing and embedding plant tissues to obtain embedded blocks; performing autofluorescence detection and tissue permeabilization treatment on part of the embedded blocks, and determining an autofluorescence threshold value and target tissue permeabilization time; slicing, pasting and fixing the remaining embedding blocks, and performing microscope fluorescence scanning detection and tissue permeabilization treatment on the tissue-containing chip according to an autofluorescence threshold value and target tissue permeabilization time to obtain a permeabilized tissue chip; and carrying out reverse transcription, tissue removal, cDNA release, recovery and amplification on the permeabilized tissue chip to obtain a cDNA amplification product. The plant tissue transcript constructed by the construction method is not easy to diffuse and high in capture rate, high-quality in-situ capture time-space group data can be obtained, the accuracy and the credibility are high, the sequencing accuracy is ensured, and the application value is high.

One example method includes receiving an image of a tissue sample stained with a stain; determining, by a first trained machine learning (“ML”) model using the image, a first set of abnormal cells in the tissue sample; receiving an autofluorescence image of the unstained tissue sample; determining, by a second trained ML model using the autofluorescence image and the first set of cells, a second set of abnormal cells, the second set of abnormal cells being a subset of the first set of abnormal cells; and identifying the abnormal cells of the second set of abnormal cells.

A system and method for performing virtual birefringence imaging and virtual staining (e.g., Congo red) of label-free human tissue is disclosed to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue into brightfield and polarized microscopy images, matching their histochemically-stained versions. Blind testing with quantitative metrics and pathologist evaluations on cardiac tissue showed that the virtually stained polarization and brightfield images highlight amyloid patterns in a consistent manner, mitigating challenges due to variations in chemical staining quality and manual imaging processes in the clinical workflow.

A device for determining the presence of a characteristic of a sample includes a light source for emitting pulsed excitation radiation, a detection device for detecting an autofluorescence radiation emitted by the sample, and a computer-based evaluation device. The detection device is configured to detect the autofluorescence radiation of the sample in a time-resolved manner at different wavelengths by means of time-correlated single photon counting and to provide the evaluation device with two-dimensional data with a wavelength dimension and a time dimenion. The evaluation device is configured to classify the provided data into classes by means of a classifier. The evaluation device is configured to identify specific wavelengths to be prioritized during the classification on the basis of features formed in the time dimension of the data.

Devices, systems, and methods are provided for imaging cholesteatoma, e.g., using an endoscope including a light source configured to deliver visible light at one or more narrowband wavelengths distally from the endoscope selected to cause autofluorescence in target tissue, a camera coupled to the imaging element to acquire image signals of locations beyond the distal end, and one or more filters configured to remove wavelengths of light in the image signals acquired by the camera including the one or more narrowband wavelengths of visible light.

A system for acquiring data regarding a wound in tissue comprises at least one excitation light source configured to directly illuminate a wound with excitation light; a spectral filtering mechanism configured to permit passage of optical signals responsive to illumination of the wound and having at least one wavelength corresponding to bacterial, fungal, and / or other microorganism autofluorescence and / or bacterial, fungal, viral, and / or microbial fluorescence, the spectral filtering mechanism including a plurality of selectable filters respectively corresponding to different discrete spectral bandwidths; an optical sensor configured to detect the spectrally filtered signals; and a processor configured to receive the detected, filtered signals and to identify a fluorescent signature of bacteria in the wound based at least in part on the detected, filtered signals and to output data regarding the bacterial, fungal, viral, and / or microbial fluorescent signature.

A computer-implemented method (530), system (100, 201) and computer readable medium for generating a slide-free histological image in the form of a reconstructed ultraviolet-based autofluorescence microscopy (UV-AutoM) 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 histological image in the form of a pseudo-hematoxylin and cosin (H&E) stained image (1120) from a grayscale input image such as a UV-AutoM image or a ultraviolet-based photoacoustic microscopy (UV-PAM) image (1110).

Examples of resin compositions include free radical curable resin matrices containing acrylates and siloxanes, and free radical photoinitiators. When cured, the resin composition has low or no autofluorescence when exposed to a blue excitation wavelength in the range of from about 380 nm to about 480 nm or a green excitation wavelength in the range of from about 510 nm to about 560 nm.

A computer-implemented method for processing an image, comprising: receiving at least one image of an unsliced sample; and processing at least the image by a neural network device for performing digital staining to obtain a digitally stained histological image of the unsliced sample. The image of the unsliced sample is obtained by an imaging system having an excitation light source that provides light to cause one or more substances, such as one or more photoluminescent substances, in the unsliced sample to emit light in order to image the sample. The digitally stained histological image generally corresponds to or substantially corresponds to an image of the unsliced sample that has been chemically stained with one or more chemical stains. The unsliced sample may be an unstained biological sample with an endogenous fluorophore, and the image may be an autofluorescence image of the unstained biological sample.