9 results about "Cellular level" patented technology

The invention provides a basic model construction method for pathological image high-resolution generation based on cell topology guidance, which comprises the following steps of: prompting a large language model based on an interactive learning scheme, and generating synthetic cell arrangement according to real cell arrangement by utilizing a cell arrangement generator; integrating the synthetic cell arrangement as a condition into a potential diffusion model; a high resolution pathology image containing cellular level control is generated through a potential diffusion model. The method has excellent performance in the aspects of authenticity of the synthetic image and retention of diagnostic information, and can significantly improve the performance of lesion subtype classification and segmentation tasks. The method not only enhances the authenticity of medical image synthesis, but also provides a high-quality data enhancement tool for downstream tasks, and promotes the development of artificial intelligence in the field of medical care.

This application provides a method, apparatus, device, and medium for detecting physiological state. The method includes: first, acquiring the potential signal and environmental parameters from the skin surface of the worker; then, processing the potential signal using a signal inversion algorithm to determine the cell ion concentration; next, extracting the characteristic frequencies of the environmental parameters if the cell ion concentration is within a preset threshold; further, calculating a dynamic equilibrium coefficient based on the cell ion concentration, potential signal, and characteristic frequencies; and finally, determining the worker's physiological state based on the dynamic equilibrium coefficient. This method enables early identification of latent fatigue at the cellular level, improving workplace safety.

The invention discloses an inhibitor-assisted cellular level sweetness quantitative determination method, and belongs to the technical field of molecular biology. A large number of experiments find that in the process of detecting the sweetness at the cellular level, cell stress caused by operation steps of exogenously adding a probe and the like can interfere with the sweetness detection result and influence the accurate quantification of the sweetness detection result. According to the invention, an inhibitor group containing a sweetness receptor inhibitor is arranged in the detection process so as to eliminate the change of the concentration of calcium ions in cells caused by cell stress and further cause a false positive result in the detection process, so that the aim of more accurately and quantitatively detecting the sweetness of the to-be-detected compound is fulfilled. Furthermore, the detection method disclosed by the invention can be used for various sweet feeling cells, is simple and convenient to operate, and can effectively reduce the false positive rate of a final detection result.

Provided are a method for estimating tissue-level information from cell-level information, and a device therefor. An estimation method according to several embodiments of the present disclosure may comprise the steps of: calculating the similarity between target tissue and a plurality of cells on the basis of first omics data on the target tissue and the second omics data on the plurality of cells associated with the target tissue, and estimating information about the target tissue by synthesizing the information about the plurality of cells on the basis of the calculated similarity. Here, the information about the plurality of cells is differentially synthesized on the basis of the tissue-cell similarity so that the information about the target tissue can be accurately estimated.

This utility model relates to the field of confocal systems and electromagnetic equipment technology, and discloses a coil structure for use in a confocal system, placed on a confocal lens. The structure includes: a ring-shaped magnetic core with a placement notch; the placement notch is used to place a cell container, with the confocal lens located below the cell container; a hollow coil wound around the magnetic core; and a coolant circulation mechanism connected to the coil, which is used to introduce a cooling medium into the coil to cool the coil structure. This coil structure can be placed in the lens area to operate, avoiding excessive attenuation of the magnetic field strength. The hollow coil allows the coolant to circulate within it and carry away the heat generated by the coil, avoiding the risk of heating the lens when using the coil directly. It solves the compatibility problem between alternating magnetic fields and confocal microscopes, and provides a reliable solution for studying the effects and mechanisms of alternating magnetic fields at the cellular level.

The invention provides an in-vivo confocal cell quantitative analysis system for lung transplantation rejection diagnosis, and the system comprises a database module which is used for obtaining a multi-class image library and an original pCLE video; the video processing module is used for processing an original pCLE video by using an Image J-AI analysis center; the cell segmentation module is used for obtaining label masks with the same size and the position corresponding to the size and the position of the original image; the feature extraction module is used for identifying single cells and carrying out cell morphology and space analysis; the rejection risk assessment module is used for constructing a score prediction model and calculating a risk score and an overall risk level of a patient; and the interface feedback module is used for visually displaying results and feeding back abnormity. According to the method, through pCLE + ImageJ + AI, cell-level cell segmentation, feature quantification and rejection scoring are achieved, and early precise diagnosis and treatment of lung transplantation rejection are powerfully assisted.

The universal biomarker array enables diagnosis and localization of disease states earlier than now recognizable, and overall increases the accuracy of diagnosing disease states. A universal biomarker array is formed from a series of mathematical parameters calculated from digital tissue images showing tissue details at cellular levels. The universal biomarker array includes one or more novel arrays of parameters (i.e., a spatial entropy array, a bin array, and / or a quartile array) in combination with one or more arrays of prior art parameters (i.e., a pattern array, a distance array, and / or a morphological array) and / or in combination with one or more other parameters. The AI / ML system can be used to analyze universal biomarker arrays relative to database-based reference data for early diagnosis and localization of disease processes.

The invention belongs to the technical field of single cell data processing, and particularly relates to a single cell time sequence data simulation and completion method and system. The method comprises the following steps that interpolation is carried out on hidden space vector representation in a time starting point and end point single cell matrix variational auto-encoder (VAE) model, then interpolation is carried out on the two vectors in a hidden space, and a middle time point hidden space vector is generated; finally, the vectors are reduced into a gene expression matrix through a decoder, so that missing or unobserved single cell data at the middle moment in the time sequence are simulated, the single cell data in the time sequence can be simulated through the method, and therefore the continuous change of the cell state can be deduced and complemented; the method is suitable for single cell level pedigree reconstruction and trajectory analysis in dynamic biological processes such as development, aging and disease progression, and has high biological interpretation value and practical application significance.

The invention discloses a pathological image proteomics analysis method and system based on deep learning, and relates to bioinformatics, and the method comprises the steps: constructing a paired image-protein data set through laser microdissection and mass spectrometry; a pre-trained VGG16 convolutional neural network is adopted to extract image deep features, and cell morphological features are fused to construct a whole proteome regression mapping model; outputting a cellular level protein expression matrix through a U-Net single cell recognition and iterative deconvolution algorithm; processing a label scarcity problem in combination with a hybrid semi-supervised learning strategy; and carrying out dimensionality reduction clustering, spatial heterogeneity quantification (CV + SHI) and visual analysis based on the protein expression matrix. According to the method, the regression mapping from the pathological image to the whole proteome (gt; 2800) is realized for the first time, the flux bottleneck of traditional virtual staining is broken through, real single cell resolution proteomics is realized, and a new means is provided for precise medical treatment.