9 results about "Complex cell" patented technology

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses composite hydrogel bio-ink with a function of space-time delivery of double growth factors as well as a preparation method and application of the composite hydrogel bio-ink, and belongs to the technical field of biomedical materials and tissue engineering. According to the preparation method, imidazolyl-modified methacrylated gelatin and double-bonded PF127 are compounded, and meanwhile, free vascular endothelial growth factors and connective tissue growth factor-loaded PLGA microspheres are entrapped in a system, so that the bio-ink with excellent printability, high adhesion, swelling resistance and space-time delivery function is formed; the bio-ink physically has the advantages of high printing fidelity, low swelling property, high mechanical strength and strong tissue adhesion of a synthetic material; in biology, the system can be used as an intelligent carrier to realize programmed space-time delivery of various growth factors, so that complex cell biological behaviors are accurately arranged, and real regeneration of functional tissues is guided.

The invention discloses a lipid droplet automatic segmentation and quantitative analysis method based on deep learning, and relates to the technical field of cytobiology, and the method comprises the steps: carrying out the preprocessing of an input cell image, and carrying out the normalization of a pixel value to a preset value interval through the image standardization operation; inputting the preprocessed image into an improved UNet segmentation model, and outputting a pixel-level segmentation mask of a lipid droplet area through an encoder and decoder structure in the model in combination with a jump connection and attention mechanism; performing morphological post-processing on the lipid droplet segmentation mask to obtain an optimized lipid droplet segmentation result; extracting geometric features of each lipid droplet based on the optimized lipid droplet segmentation result, and calculating one or more lipid droplet quantitative indexes according to the geometric features; and generating and outputting data containing the lipid droplet segmentation result and the lipid droplet quantitative index. The method can adaptively focus on the lipid droplet area in the image, effectively inhibits the interference of a complex cell background, and guarantees the segmentation accuracy.

The invention discloses a micromolecular fluorescent probe for specific imaging autophagy and a preparation method and application thereof, the fluorescent probe comprises a fluorophore, a linking group and an autophagy targeting group, the fluorophore is an intelligent fluorescence reporter group, the linking group adopts cysteine as a linking unit, and the autophagy targeting group targets LC3 protein; the probe can be used for specifically imaging an autophagy process of a biological sample and screening an autophagy regulator. The micromolecular fluorescent probe has the advantages of being good in cell membrane permeability, simple and convenient in dyeing process and easy to be compatible with other fluorescence labeling technologies, can perform specific recognition and selective imaging on an autophagy related structure, improves the targeting property and imaging credibility of the probe in a complex cell environment, and can be used for detecting autophagy related structures. The method can be used for visual tracing and detection of autophagy flow in living cells under physiological and pathological conditions, and is beneficial for promoting a small-molecule fluorescent probe to analyze an autophagy regulation mechanism and biological functions in real time.

The application provides a method for synchronously detecting multiple cell death modes in a tissue slice based on multiplex immunofluorescence, and belongs to the technical field of biological detection. The application realizes the synchronous labeling and visualization of multiple cell death modes in a single tissue slice, significantly improves the detection efficiency and saves precious samples; by integrating key biomarkers covering main cell death modes in the same detection system, a self-defined interpretation scheme based on multi-marker expression combination is constructed, effectively reducing the error caused by non-specific expression of a single marker, improving the reliability and rationality of the death mode classification, and providing a general solution for rapid screening of complex cell death networks. The application is suitable for anticancer drug efficacy evaluation, combination drug regimen screening and disease mechanism research, and has important scientific research value and clinical conversion prospect.

A classifier engine provides cell morphology identification and cell classification in computer-automated systems, methods and diagnostic tools. The classifier engine performs multispectral segmentation of thousands of cellular images acquired by a multispectral imaging flow cytometer. As a function of imaging mode, different ones of the images provide different segmentation masks for cells and subcellular parts. Using the segmentation masks, the classifier engine iteratively optimizes model fitting of different cellular parts. The resulting improved image data has increased accuracy of location of cell parts in an image and enables detection of complex cell morphologies in the image. The classifier engine provides automated ranking and selection of most discriminative shape based features for classifying cell types.

The application provides a cerebellar Purkinje neuron rapid identification system and method based on multi-modal deep learning, relates to the cross technical field of biomedical engineering and computer vision, and fuses specific fluorescence features and multi-dimensional morphological features, constructs a special multi-modal deep learning model, solves the interference problem of other neurons in a mixed culture system, and greatly reduces the false positive rate; a small sample learning architecture solves the industry pain point of a small amount of biological sample labeled data, improves the model generalization capability, and adapts to multi-scene sample identification. The identification system fuses Purkinje neuron specific fluorescence labeling features and cell morphological features, constructs a multi-modal deep learning recognition model based on small sample learning, is matched with a full-automatic microscopic imaging and analysis system, realizes rapid and high-accuracy identification of Purkinje neurons under a mixed culture system, does not need a complex cell purification step, greatly shortens the experimental period, and reduces the technical threshold.

This invention discloses a method for generating high-resolution microscopic cell images based on an improved HAT model. The method includes: preprocessing cell slice images captured under a microscope to construct a single-cell microscopic image dataset for training; feeding the single-cell microscopic image dataset into the improved HAT model for training; iterating the improved HAT model multiple times until the classification-perception loss function is minimized, thus obtaining the optimal-performing model; and inputting the low-resolution cell image to be tested into the optimal-performing model to obtain a high-resolution cell image. This invention can effectively recover the complex cell texture information in microscopic cell images, generating high-quality, high-resolution microscopic cell images, thereby improving the efficiency and accuracy of subsequent medical diagnosis.

The application relates to the technical field of optical engineering design, in particular to a design method of a super-structured grating, the super-structured grating comprising a grating layer, the grating layer comprising a periodic cell, the design method comprising the following steps: constructing a neural network of deep reinforcement learning, taking the current state of the cell unit as the input of the input layer of the neural network, and taking the output of the output layer of the neural network as the action of filling the cell unit; obtaining a new state of the cell unit after the action is performed; calculating the diffraction efficiency of each diffraction order of the super-structured grating in the new state; obtaining the reward return of the action according to the diffraction efficiency; updating the input of the neural network based on the reward return and propagating reversely until the terminal state of the current deep reinforcement learning is reached; and repeatedly performing the above deep reinforcement learning steps until the requirement of the optimized design is met, so that the rapid optimized design of the super-structured grating comprising a complex cell structure can be realized.