23 results about "Cellular density" patented technology

A method includes introducing a suspension including cells suspended in a cell culture medium through a feed port or a drain port into a cavity of a cell culture vessel, the suspension being in an amount sufficient to cover a gas permeable, liquid impermeable membrane positioned at a bottom of the cell culture vessel, the feed port being disposed through a surface of the cell culture vessel and configured to permit additional cell culture medium into the cavity, and the drain port being disposed through the surface of the cell culture vessel and configured to permit removal of the cells, cell culture medium, and used cell culture medium from the cavity, allowing the cells to settle on the gas permeable, liquid impermeable membrane by gravity, removing the used cell culture medium through the drain port and introducing the additional cell culture medium through the feed port such that a constant volume is maintained in the cell culture vessel until the cells expand to a desired cell density, wherein the removing and introducing are performed subsequent to allowing the cells to settle on the gas permeable, liquid impermeable membrane, resuspending the cells in the cell culture medium in the cell culture vessel, wherein the resuspending is performed after the desired cell density is attained, and removing the resuspended cells and the cell culture medium through the drain port.

The invention relates to the technical field of medical image processing and artificial intelligence cross application, in particular to a medical image detection method based on a cross-channel and cell density adaptive mechanism. According to the method, on the basis of a YOLO backbone architecture, a cell density adaptive attention module, a cross-channel feature enhancement module and a multi-magnification detection head are introduced, and a multi-dimensional cooperative enhancement detection network model is constructed and formed; and accurate detection and semantic feature enhanced characterization of medical targets in scenes with different tissue cell densities, different target scales and different image magnification factors are realized. According to the technical scheme, through core mechanisms of cell density adaptive modeling, cross-channel feature interaction, hierarchical feature fusion and the like, the technical problems of insufficient detection stability, weak model generalization ability, insufficient target semantic feature expression and the like in the existing medical image detection technology are effectively solved; finally, high-precision, low-delay and high-expandability pathological image automatic detection is realized.

The invention relates to the technical field of medical data processing, and discloses a cancer immunotherapy prognosis prediction method, system and equipment based on CD8 + T cell density in a necrotic area and a medium. The method comprises the following steps: firstly, extracting pathological features on a CD8 immunohistochemical staining (IHC) slice image of tumor tissue of a to-be-detected person after neoadjuvant immunotherapy by using a multi-scale feature extraction model, and then carrying out regional division on the CD8 IHC slice image by using a regional classification model to obtain a necrotic region; then, the CD8 + T cells in the necrotic area are recognized and counted through the target recognition model, the cell density of the CD8 + T cells in the necrotic area is rapidly obtained, and finally, the EFS of the person to be detected is accurately predicted based on the cell density of the CD8 + T cells in the necrotic area. According to the method, efficient and high-precision EFS prediction can be carried out only through the CD8IHC slice image of the tumor tissue, and a doctor can make a proper diagnosis and treatment plan for a patient according to a prediction result.

The invention provides a high-density suspension transfection method for efficiently obtaining a lentiviral vector. The method comprises the following steps: accurately regulating and controlling the density of HEK293T cells to 5.5-6.5 * 10 < 6 > cells / ml, mixing GOI, pL-Gagpol, pL-Rev and pL-VSVG plasmids according to a molar ratio of 4: 2: 1: 0.6, and forming a transfection compound according to a ratio of PEI to total plasmids of 2.5: 1; and after transfecting for 24 hours, adding 5 mM of sodium butyrate, and continuously culturing for 72 hours to obtain the virus. According to the method, the traditional density limitation is broken through, the transduction titer is increased by 3 times when the density is 6 * 10 < 6 > cells / ml, special equipment is not needed, and the production cost is greatly reduced. The produced lentiviral vector is suitable for the field of gene therapy such as CAR-T cell therapy, the titer of the lentiviral vector is larger than or equal to 2.5 * 10 < 7 > TU / ml, and refractory target cells can be efficiently transduced.

This invention relates to the field of nucleic acid detection technology, specifically disclosing a liquid-phase nucleic acid chip for isothermal amplification of common algal blooms and its application. The method includes: collecting water samples and filtering to enrich algal cells; lysing the algal cells using cell lysis buffer to extract DNA; adding the extracted DNA to the liquid-phase nucleic acid chip for isothermal amplification; detecting the Ct value using a qPCR instrument; and determining the species and cell density of the algal bloom based on a preset standard curve. This invention directly quantifies algal cell density using algal gene copy number, with significantly higher accuracy than microscopic counting, and does not rely on the experience of professional personnel. It can be widely applied in frontline water resource management work.

The application belongs to the technical field of bioengineering, and specifically discloses an in-vitro expansion method and application of dog NK cells with high tumor cell killing efficiency, which comprises the following steps: recovering K562 engineering cells stored by freezing in sequence; taking 3 mL of dog peripheral blood to obtain dog peripheral blood mononuclear cells (PBMC); activating and expanding culture of dog NK cells with high tumor cell killing efficiency; collecting the expanded dog NK cells with high tumor cell killing efficiency, and storing them in a freezing medium at a cell density of 1×10 8 The application can obtain dog NK cells with high expansion multiple from only 3 mL of dog peripheral blood; the number of dog NK cells of a healthy volunteer dog sample is 3.02×10 8 after 11 days of continuous expansion culture, and the expansion multiple is 1008; the total number of dog NK cells obtained from a tumor volunteer dog sample reaches 6.84×10 7 , and the expansion multiple is 340; and the dog NK cells obtained by the application have high tumor cell killing efficiency.

A method includes introducing a suspension including cells suspended in a cell culture medium through a feed port or a drain port into a cavity of a cell culture vessel, the suspension being in an amount sufficient to cover a gas permeable, liquid impermeable membrane positioned at a bottom of the cell culture vessel, the feed port being disposed through a surface of the cell culture vessel and configured to permit additional cell culture medium into the cavity, and the drain port being disposed through the surface of the cell culture vessel and configured to permit removal of the cells, cell culture medium, and used cell culture medium from the cavity, allowing the cells to settle on the gas permeable, liquid impermeable membrane by gravity, removing the used cell culture medium through the drain port and introducing the additional cell culture medium through the feed port such that a constant volume is maintained in the cell culture vessel until the cells expand to a desired cell density, wherein the removing and introducing are performed subsequent to allowing the cells to settle on the gas permeable, liquid impermeable membrane, resuspending the cells in the cell culture medium in the cell culture vessel, wherein the resuspending is performed after the desired cell density is attained, and removing the resuspended cells and the cell culture medium through the drain port.

There is provided a system and method for generating a mapping of local brain cell activity and connectivity. The method including: receiving brain imaging data from image captures of a brain; generating a binary segmentation map using a trained deep learning model, the deep learning model taking the brain imaging data as input; performing registration of the brain imaging data to a brain template; voxelizing the segmentation map and warping the voxelized segmentation map using deformations obtained during registration; determining group-wise heatmaps of neuronal density or cellular density from the voxelized and warped segmentation map; generating a map of clusters of between-group differences in neuronal density or cellular density from the group- wise heatmaps of the voxelized and warped segmentation map; and outputting the map of clusters as a representation of local brain cell activity and connectivity.

The invention discloses a method for simultaneously extracting various cells of internal and external secretion parts of pancreas, and relates to the technical field of pancreas islet separation and purification. The invention discloses a method for simultaneously extracting various cells of internal and external secretion parts of pancreas. The method comprises the following steps: exposing a target visual field, performing pancreas perfusion, terminating digestion, sieving, performing gradient centrifugation, observing and separating layered cells, sorting pancreas islets, extracting acinus cells and identifying. According to the method, a gradient centrifugation method is adopted, a density gradient environment is constructed through a gradient centrifugation solution based on the difference of different cell densities, and various cells settle to corresponding positions according to self densities, so that efficient separation of islet cells, alveolar cells and impurities (such as adipocytes, digestive enzymes and tissue fragments) is realized, and the separation efficiency of the islet cells, the alveolar cells and the impurities is improved. Finally, different types of cells are obtained, and various cell populations obtained based on the method are high in purity, good in activity and high in yield.

An image depicting a tissue sample is received. One or more tissue compartments present in the image of the tissue sample are identified including by generating a compartment map identifying pixels in the image depicting the tissue compartments. One or more cells present in the image of the tissue sample are identified including by generating a cell map identifying pixels in the image depicting the cells. A density-based pathology signature for the image of the tissue sample is determined based on the compartment map and the cell map. The density-based pathology signature includes a density of each cell type present in the one or more tissue compartments. A response computation model is applied to determine, based on the density-based pathology signature, a predictive output indicative of a likelihood of a patient associated with the tissue sample responding to an anticancer therapy such as immunotherapy.

The invention provides a composite additive and application thereof in mammalian cell culture, and relates to the technical field of cell culture, the composite additive comprises a pyrimidine nucleoside precursor, vitamin, amino acid, a TCA circulation intermediate product and metal ions; wherein the deoxyuridine is used as a pyrimidine nucleoside precursor and can promote cell DNA synthesis so as to improve the peak living cell density; pyridoxine hydrochloride and choline chloride synergistically regulate amino acid metabolism and enhance the antibody synthesis efficiency; magnesium is an activating agent of mitochondrial key enzymes such as pyruvate dehydrogenase and citrate synthase and can improve the ATP generation efficiency and provide more energy for antibody synthesis, alpha-ketoglutaric acid optimizes tricarboxylic acid circulation to supply energy, the cell activity in a low-iron environment is guaranteed, finally, multi-channel synergistic interaction is achieved, the cell density and the antibody yield are improved, and the product quality is improved. And the key quality attribute of the antibody is not changed. The technical problem that a culture medium in the prior art cannot coordinate the balance of cell density, antibody yield and charge heterogeneity in a low-iron environment is solved.

The application provides a cervical cell nucleus multi-scale accurate segmentation method based on an HSV channel difference feature, the method first converts an image to an HSV space and extracts a saturation S and a brightness V channel; then, adaptive enhanced difference feature channels are constructed by fusing a weighted difference value and a ratio relationship, the discrimination of the cell nucleus and the background is effectively improved, and the method has strong robustness to staining and illumination changes; subsequently, cell density sensing adaptive CLAHE and gradient guided adaptive median filtering are used for multi-scale enhancement and denoising, interference is suppressed while details are retained; in the segmentation stage, a hierarchical strategy of a global threshold value, a local adaptive threshold value and a morphological gradient enhancement is adopted, and adaptive morphological post-processing and screening are combined, so that accurate and complete segmentation of the cell nucleus in a complex scene is finally realized; the whole process of the application is based on clear image processing principles, does not require large-scale training data, has high calculation efficiency and is easy to integrate into an existing pathological information system.

The invention relates to a ki-67 proliferation index counting method based on an improved UNet network. The method comprises the following steps: acquiring a pathological section image of ki-67 positive tumor cells; calibrating the pathological section image according to a preset standard optical parameter set to obtain a calibrated section image; extracting a region mean value and a region variance of the positive staining signal of the calibration slice image, and the cell density of the calibration slice image; identifying a hot spot region of the calibration slice image based on a region mean value, a region variance and a cell density; based on the hot spot region, a pre-trained improved UNet network is adopted to carry out ki-67 positive tumor cell recognition, and a ki-67 positive tumor cell recognition result is obtained; and calculating a Ki-67 proliferation index based on an identification result. By adopting the method, the accuracy and efficiency of ki-67 proliferation index counting can be improved, manual intervention is reduced through automatic image processing, and objective evaluation of tumor proliferation activity is facilitated.

The application discloses a kind of metrology method and system for visual cell density, it is related to visual cell metrology technical field, the method comprises: obtaining target area video based on first preset angle threshold;Frame by frame extraction obtains target area image set;Registration processing obtains target area registration image;Intelligent processing model is constructed and pretreated, and pretreated target area registration image is obtained;Generate pixel abstract graph;The segmentation processing result of the pixel abstract graph is obtained by using superpixel segmentation technology processing, which includes a plurality of segmentation regions;Extract segmentation region and collect to obtain segmentation region data set;The visual cell density of the target retina macular region is calculated.The technical problem that visual cell diameter and density cannot be accurately measured in real time when observing living non-invasive visual cells in the prior art is solved.The technical effect of improving the real-time and accuracy of visual cell diameter and density measurement is achieved.

The invention discloses a method for separating mononuclear cells from alpaca peripheral blood, a preparation and a preparation method, and relates to the technical field of biology, the preparation comprises a reagent A and a reagent B, the reagent A comprises 1.0 mg / mL to 4.0 mg / mL of hydroxypropyl methylcellulose, 2.0 mg / mL to 6.0 mg / mL of chitosan quaternary ammonium salt, 48.0 mg / mL to 54.0 mg / mL of polysucrose 400 and 80.0 mg / mL to 10.0 mg / mL of meglumine diatrizoate; the reagent B comprises a Percoll separating medium, a diluent of the Percoll separating medium is normal saline or PBS, and the density of the Percoll separating medium is 1.079 g / mL-1. 081 g / mL. The problems that in the prior art, the density of neutrophils and the density of mononuclear cells in alpaca peripheral blood are close, the mononuclear cells are separated and extracted by using a single or discontinuous gradient separating medium, and the separation efficiency of the neutrophils and the mononuclear cells is low can be solved. And the effect is poor.

The application belongs to the technical field of biological information data processing, and particularly relates to a spatial gene co-expression network construction system based on in-situ sequencing. First, in-situ sequencing raw data of a target tissue section is acquired, the raw pixel coordinates are linearly scaled and translationally corrected into objective and true raw spatial physical coordinates, and a gene expression matrix is statistically generated; second, the real spatial distance between sequencing sites is calculated based on the raw spatial physical coordinates, and a spatial adjacency graph is constructed in combination with a dynamic distance threshold and a local neighborhood constraint algorithm; and finally, the gene expression matrix is mapped into the spatial adjacency graph to extract local spatial co-expression features and construct a network. The application eliminates physical scale distortion caused by underlying hardware sampling, flexibly adapts to the cell density and heterogeneity characteristics of different tissues, effectively avoids loss of real biological communication links in sparse areas, and realizes high-fidelity reconstruction of gene synergistic regulation networks in microenvironments.

The invention belongs to the technical field of nucleic acid detection, and particularly relates to a sample lysate, application of the sample lysate in a human puncture sample nucleic acid extraction kit and a product. The sample lysis solution is prepared from the following components: 1 to 5 M of guanidine hydrochloride, 1 to 5 M of guanidine isothiocyanate, sodium citrate with the mass concentration of 2 to 10 percent, lauryl sodium sulfate with the mass concentration of 1 to 5 percent, 100 to 300 mM of ethylenediamine tetraacetic acid, Nonet P-40 with the mass concentration of 1 to 6 percent, polyvinylpyrrolidone with the mass concentration of 1 to 3 percent, 2 to 10 percent of Triton X-100 and 1 to 5 mM of tris (hydroxymethyl) aminomethane. The lysate provided by the invention has a good nucleic acid extraction effect, and can effectively adapt to thyroid puncture samples with different sources and different cell densities.

According to the method, gene expression specific to the cell tumor state in a predefined gene list is analyzed by using a trained binary classifier algorithm, so that the tumor of a biological sample can be estimated.

The present application relates to the technical field of medical image processing and artificial intelligence cross-application, in particular to a medical image detection method based on cross-channel and cell density adaptive mechanism. The method introduces a cell density adaptive attention module, a cross-channel feature enhancement module and a multi-magnification detection head on the basis of a YOLO main architecture, and constructs a multi-dimensional collaborative enhancement detection network model, so as to realize accurate detection and semantic feature strengthening representation of medical targets in different tissue cell density, different target scale and different image magnification scenarios. Through the core mechanisms of cell density adaptive modeling, cross-channel feature interaction and hierarchical feature fusion, the technical scheme effectively solves the technical problems of insufficient detection stability, weak model generalization ability and insufficient target semantic feature expression in the existing medical image detection technology, and finally realizes high-precision, low-delay and highly-scalable pathological image automatic detection.

A method for predicting the metabolic state of a cell culture of cells of a specific cell type includes providing a metabolic model of a cell of the specific cell type, and performing at each of a plurality of points in time during cultivation of the cell culture, receiving measured concentrations of a plurality of extracellular metabolites and a measured cell density in the culture medium; inputting the received measurements as input parameter values to a trained machine learning program logic-MLP; predicting extracellular fluxes of the extracellular metabolites at a future point in time by the MLP; performing metabolic flux analysis to calculate the intracellular fluxes at the future point in time based on the predicted extracellular fluxes and the stoichiometric equations of the metabolic model.

The invention discloses an algae density detection method, device and equipment and a medium, and relates to the technical field of water quality detection.The method comprises the steps that target algal bloom dominant algae in a to-be-detected water body are determined through a target image of the to-be-detected water body; performing fluorescence detection on the to-be-detected water body to obtain the target fluorescence intensity of the to-be-detected water body, and determining a target characteristic coefficient of the dominant algae genus of the target water bloom from the characteristic coefficients of the algae genus; wherein the characteristic coefficient of each algal genus is constructed based on a linear relation between the fluorescence intensity and the cell density of each algal genus; and evaluating the target cell density of the target water bloom dominant algae by using the target fluorescence intensity and the target characteristic coefficient. According to the scheme, the accuracy and efficiency of algae density detection can be improved.

The invention discloses a single cell density prediction method based on machine learning, and relates to the technical field of machine learning and cell quantitative analysis, the implementation of the method comprises the following steps: controlling cells to move in a microfluidic environment through dielectrophoretic force generated by a light-induced dielectric technology; collecting motion state change data of the to-be-detected cells in the sedimentation process; performing data cleaning and normalization on the data; time points in the cell sedimentation process and image frame numbers matched with the corresponding time points are extracted, and a feature vector is formed by the time points and the image frame numbers to serve as a feature data set; constructing a cell density prediction model by using a machine learning algorithm, and performing training and testing; and evaluating a prediction result by using indexes such as R2, RMSE, MSE, MAE and the like, and selecting an optimal model. According to the prediction method based on machine learning, the problems that a traditional method needs complex image processing to calculate the cell radius, calculation is complex and time cost is high are solved, and the effects of high efficiency and high accuracy are achieved; the method has the application prospects of improving the research efficiency of cell-related physical parameters and providing reference for prediction of other biological parameters.

The invention discloses a siniperca chuatsi brain tissue cell line culture medium which comprises an amino acid part, a carbon source part, a vitamin part, an inorganic salt part and water. The amino acid part is prepared from tyrosine, tryptophan, phenylalanine, methionine, threonine, cysteine, histidine, asparagine, lysine, valine, leucine, glutamine, serine, arginine and isoleucine; the carbon source part comprises D-glucose, D-galactose and sodium pyruvate; the vitamin part comprises choline chloride, D-calcium pantothenate, folic acid, niacinamide, pyridoxine hydrochloride, inositol and thiamine monophosphate; the inorganic salt part comprises calcium chloride, magnesium chloride, potassium chloride, monopotassium phosphate, sodium chloride, magnesium sulfate, sodium dihydrogen phosphate and sodium bicarbonate. According to the personalized culture medium, glucose and galactose serve as main carbon sources, the concentration of amino acid is greatly reduced, CPB cells are higher in growth speed and cell density and have stable cell activity, and efficient proliferation of the cells is promoted.