14280 results about "Cell" patented technology

The invention discloses an abnormal monitoring method and system for immune cell culture. The method comprises the following steps: collecting cell physical state parameters, culture environment parameters and metabolic biochemical indexes in a culture system in real time; performing motility rate threshold judgment and morphological analysis based on the cell physical state parameters to generate a first abnormal signal; performing dynamic trend analysis on the metabolism biochemical indexes to generate a second abnormal signal; performing grade association on the culture environment parameters, the first abnormal signal and the second abnormal signal, and outputting a monitoring abnormal grade; and performing grading response construction according to the monitoring abnormity grade to obtain an abnormity monitoring report. According to the method, the pollution diffusion risk and the functional cell failure misjudgment rate can be reduced.

The invention discloses a biosynthesis system for high-yield 1, 6-hexamethylenediamine and application of the biosynthesis system, and belongs to the technical field of biology. According to the method, key genes which are excavated and optimized in a 1, 6-hexamethylenediamine biosynthetic pathway are co-expressed in a single cell module or a multi-cell module, and different enzyme elements expressed by the cell module are subjected to enzyme cascade reaction, so that 1, 6-hexamethylenediamine can be efficiently synthesized by taking caprolactam or 6-aminocaproic acid as a substrate; furthermore, after the carboxylic acid reductase MaCAR is mutated, the catalytic activity of the carboxylic acid reductase MaCAR can be remarkably improved, and the yield of the 1, 6-hexamethylenediamine is further improved; in addition, the biosynthesis system disclosed by the invention directly takes the cell module as a whole-cell catalyst, and an enzyme purification step is avoided, so that the biosynthesis system has the advantage of low cost.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention provides a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on an online Raman spectrum, belongs to the technical field of biological fermentation process control, and aims to solve the problems of unstable process and low efficiency caused by fermentation control lag and incapability of sensing the real metabolic state of cells in the prior art. The method comprises the following steps: acquiring the concentrations of a target product BDO and key byproducts such as acetic acid and ethanol in the fermentation liquor in real time through an online Raman spectrum; according to the method, a metabolic stress index is originally proposed and constructed, the index is obtained by performing weighted operation on the instantaneous generation rate of the by-product and the target product, and the index is used for quantitatively characterizing the intrinsic metabolic stress level of the cells in real time. The control strategy of maintaining the metabolic stress index in the preset optimal stable interval is taken as a core control strategy, the conversion from passive response to active prediction in the fermentation process is realized, and the yield, the stability and the batch repeatability of Bi-BDO production are remarkably improved.

The invention relates to the technical field of model analysis, in particular to an imaging flow cytometry cell detection method based on an improved model. The method comprises the following steps: introducing a cell sample to be detected into an imaging flow cytometry system integrated with a micro-fluidic chip for continuous image acquisition to generate an initial cell image sequence; an automatic digital focusing algorithm is applied to the initial cell image sequence, and a cell image frame set with the optimal focal plane is screened out; inputting the cell image frame set into a preset PA-YOLO improved model for multi-dimensional extraction and fusion, and generating a multi-scale cell characteristic spectrum; carrying out refined feature learning and cell target positioning and classification on the multi-scale cell feature spectrum, and outputting a cell detection result; and carrying out validity verification on the cell detection result, and carrying out comparative analysis in combination with an imaging flow cytometry system to generate a cell detection report. According to the method, the imaging quality and the detection accuracy of cell images with different depths can be remarkably improved.

In accordance with at least one aspect of this disclosure, there is provided an imaging agent, a including dye compound conjugated to an antigen specific targeting vector which can be particularly advantageous because their behavior in vivo can contribute to superior optical imaging properties, for example, by significantly increasing the target-to-background ratio of imaged tissues, leading to higher resolution imaging, and ultimately providing for better recognition of malignant tissue for resection and margin assessment and improved visualization during minimal invasive laparoscopic surgery, for example. A method of imaging tumor cells in a subject, can include, administering an imaging effective amount of an imaging agent according at least one embodiment of the invention; 2-4 hours after administration of the imaging agent, irradiating a region in the subject in which tumor cells are expected to be found with at a wavelength absorbed by the imaging agent; and detecting a signal from the imaging agent, thereby imaging the tumor cells, wherein the target-to-background ratio 2-4 hours after administration is from about 2.75 to about 15.

The invention discloses a PgEGY3 gene for improving cold resistance of pennisetum alopecuroides and increasing fresh weight of leaves and application of the PgEGY3 gene, and relates to the technical field of genetic engineering, and the nucleotide sequence of the PgEGY3 gene of the pennisetum alopecuroides is shown as SEQ ID NO.1. The invention also discloses a protein coded by the Pennisetum alopecuroides PgEGY3 gene. The invention also discloses a recombinant expression vector and a recombinant host cell comprising the Pennisetum alopecuroides PgEGY3 gene. The invention also discloses application of the PgEGY3 gene, a recombinant expression vector and a recombinant host cell in improving cold resistance and biomass of pennisetum alopecuroides. It is verified that the Pennisetum alopecuroides PgEGY3 gene can improve the cold resistance of plants, and the growth state of the plants under cold stress is remarkably improved through overexpression of the PgEGY3 gene.

This invention provides an in vitro cell-free protein synthesis system, its reagent kit, and its applications, belonging to the field of protein synthesis technology. This invention improves an in vitro cell-free protein synthesis system based on Kluyveromyces lactis by adding exogenous L-arabinose, significantly enhancing the system's protein synthesis capacity. Furthermore, it provides a more efficient and higher-throughput in vitro protein synthesis reagent kit and a method for synthesizing exogenous proteins. The improvements provided by this invention can be achieved without molecular modification, are simple and convenient to operate, and save costs.

The invention relates to the technical field of biology, and particularly discloses a recombinant collagen III and application thereof in preparation of gel. The recombinant collagen III is designed by optimizing functional area sequences of human I-type and III-type collagen, the amino acid sequence is shown as SEQ ID No.2, and the recombinant collagen III has the characteristics of high stability, good hydrophilicity and low immunogenicity. The preparation method comprises the steps of expression vector construction, escherichia coli induced expression, affinity chromatography purification and renaturation. The recombinant collagen III can be prepared into a gel dressing and comprises sodium alginate, methylparaben and other components. Experiments show that the gel can effectively promote cell proliferation and has no cytotoxicity; in a mouse skin injury model, the collagen can relieve inflammation, accelerate wound healing and inhibit scar formation, and the effect of the collagen is superior to that of natural human III-type collagen. The invention provides a safe and efficient novel material for wound repair, and is suitable for medical dressings and tissue engineering.

The invention relates to application of a targeted inhibition circPDK1 reagent in preparation of an anti-esophageal cancer drug, and belongs to the field of biological medicines. The reagent for targeted inhibition of circPDK1 expression provided by the invention is shRNA or siRNA, and in-vivo and in-vitro experiments prove that the reagent can significantly inhibit circPDK1 expression and inhibit growth and migration of esophageal cancer tumor cells; after siRNA of targeted annular circPDK1 is packaged into efficient and low-toxicity LNP-siRNA, circPDK1 expression is specifically silenced, proliferation and migration of esophageal cancer tumors can be remarkably inhibited, and a basis is provided for clinical treatment and scientific research of esophageal cancer related circRNA.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention provides a blood cell image recognition method based on a multi-scale Vision Transform model, and the method comprises the steps: constructing a multi-scale feature extraction architecture, combining the global modeling capability of a Transform network, carrying out the parallel processing of Patches of different sizes, and fusing the multi-scale information, thereby achieving the recognition of a blood cell image. The cells in the blood smear can be comprehensively perceived from microscopic details to macroscopic contexts, so that not only is the adaptability of the model to cell size difference and morphological diversity enhanced, but also the recognition stability under the conditions of cell overlapping and dyeing difference in a complex visual field is improved; the accuracy and calculation efficiency of cell classification and counting are effectively improved, and full-process automation from visual field quality evaluation to automatic cell recognition and counting is achieved.

The invention provides a sample pretreatment automation method for flow cytometry, and belongs to the technical field of sample pretreatment for flow cytometry. A biological sample to be detected is collected and placed in a collection tube containing an EDTA-K2 anticoagulant for pretreatment; an automatic pipetting system is used for carrying out incubation reaction on an anticoagulant whole blood sample and an intelligent matching fluorescence labeling antibody mixed solution, and a double-layer game optimization verification system is used for verifying the rationality of an antibody adding proportion and reaction condition parameters; carrying out fluorescence characteristic readability index prediction on the incubated cell suspension by adopting an intelligent dilution parameter regulator model, dynamically adjusting hemolysis parameters and suspension conditions, and adding 1 * hemolysin working solution by using an automatic liquid adding device to carry out hemolysis treatment so as to finish pretreatment; the technical problems that in the pretreatment process of flow cytometry detection samples, parameters are often based on artificial experience, and the reproducibility and standardization degree of detection results are insufficient are solved.

The invention provides a construction method of a predictive cell senescence model, a method for predicting the senescence state of a tissue sample based on the senescence model, a method for screening potential therapeutic drugs, equipment, a medium and a program product, and relates to the field of intelligent medical treatment. The model construction method comprises the following steps: acquiring a training set sample expression profile data set; identifying a key senescence gene set from the data set by using a feature selection algorithm; inputting the key senescence gene set into a machine learning model to fit a prediction model, and determining an optimal hyper-parameter to obtain a cell senescence model containing the weight of a single gene in the key senescence gene set; the cell senescence model is a senescence score obtained by calculating the sum of the product of the expression quantity of a single gene and the regression coefficient thereof. The cell senescence model, namely PreCSenM, is constructed by integrating a plurality of senescence characteristic gene sets and a gene scoring algorithm, the accuracy in CS evaluation is superior to that of 10 existing methods, and the application of CS from biological research to clinical scenes is also realized.

Apparatuses and methods for recovering solid tissue and dislodged cells (“D-cells”) from a biopsy are disclosed herein. In an embodiment, a biopsy container apparatus for recovering solid tissue and D-cells from a biopsy includes a receiving container and a sieve container. The receiving container has a receiving chamber with an upper opening and a watertight bottom surface. The sieve container is configured to be located at least partially within the receiving chamber and removed from the receiving chamber through the upper opening. The sieve container has a bottom sieve surface including a plurality of apertures configured to pass the D-cells from the biopsy but not the solid tissue from the biopsy into the receiving chamber after the solid tissue and the D-cells from the biopsy are placed into the sieve container.

The invention discloses a tumor evolution trajectory prediction method and system based on image feature learning, and the method comprises the steps: obtaining the whole-process pathological section image data of a target type tumor patient, carrying out the analysis and screening of the image quality, constructing a pathological section screening strategy, and extracting a standard image; extracting tumor cell characteristics based on the standard image, performing grouping analysis on the cell characteristics of different time periods through a clustering algorithm, and determining tumor cell development characteristics; further constructing the cell development characteristics of multiple patients into a heterogeneity propagation network, simulating the tumor evolution process by using a random walk algorithm, and identifying the multi-branch evolution trajectory of the tumor; and finally, establishing a tumor evolution trajectory prediction model to predict the tumor development trend of the current patient. According to the method, the accuracy and interpretability of tumor evolution trajectory modeling can be improved, and reliable support is provided for clinical individualized diagnosis and treatment.

The present invention relates to the field of tissue-derived cell identification, in particular to a method for identifying tissue-derived cells in the body fluid based on single-cell sequencing technology. In the present invention, on the basis of high-throughput single-cell RNA sequencing data of cell samples obtained from body fluids, by means of reference component analysis (RCA), expression of known tissue-related marker genes, and a tissue-derived cell prediction model constructed based on logistic regression, the specific identification of tissue-derived cells in body fluids is achieved.

The invention provides a pourable tumor organoid vascularization model and a construction method thereof, and relates to the technical field of organoid culture. A temperature-sensitive degradable material and a photo-crosslinking modified temperature-sensitive material are used, the composite stent is printed through two channels, a multi-component gel mask partition curing process is combined, the stability of a perfusion pipeline is improved, and a partitioned highly-bionic complex microenvironment is accurately constructed in a macroscopic three-dimensional space; and constructing to obtain the perfusion tumor organ vascularization model. The model is high in bionic degree and stable in structure, can be applied to deepening cell and microenvironment interaction research and tumor-related drug screening, and has a good application prospect.

The current invention provides a biocompatible energy recharge system to normalize body functions for a mammal. Both stationary and circulatory scalar wave energy was produced and utilized to enable the entire body of a mammal to increase the energy potential of any cell in the body, to promote cell growth, to facilitate cell repair, and to reduce cell destruction, hence the cells, organs, systems and the entire body of a mammal become healthy, and to treat a disease or disorder. The invention is a combination of the generators of the three-dimensional bio-waves in the stationary and in the circulationary formats for empowering the cells in all parts of a mammal, and a method of using the 3D bio-waves uniformly to empower each organ of a mammal to restore and maintain the normal body health and function.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

The invention discloses protease for improving the resistance of potatoes to late blight as well as a coding gene and application thereof, and belongs to the technical field of genetic engineering and plant disease resistance breeding. The amino acid sequence of the protease StRD21 is as shown in SEQ ID NO.2, or the protease StRD21 has a derivative sequence with the same disease-resistant function. The nucleotide sequence of the coding gene is as shown in SEQ ID NO. 1. The invention also provides a specific primer pair for cloning the gene, a potato recombinant overexpression vector containing the gene and a transgenic host cell. The StRD21 gene is introduced into potatoes and overexpressed, so that the resistance of the potatoes to late blight can be remarkably enhanced. The invention provides an effective gene resource and a biotechnological means for cultivating a new variety of disease-resistant potatoes.

The invention discloses a scorpion anti-inflammatory polypeptide as well as a screening method and application thereof, and belongs to the technical field of traditional Chinese medicinal material polypeptides. The scorpion anti-inflammatory polypeptide is one of G4, G5 and G17. The method comprises the following steps: detecting a scorpion buthus martensii polypeptide extract through nanoElute 2 nanoliter liquid phase separation, timsTOFPro2 mass spectrometry and BPS Novor search analysis, screening a scorpion buthus martensii polypeptide, and further synthesizing the screened candidate polypeptide by adopting a solid-phase synthesis method; the effect of the synthesized candidate polypeptide on inhibiting microglial cell inflammation is evaluated through a qPCR method, and then the scorpion buthus martensii anti-inflammatory polypeptide is screened out. The Buthus martensii Karsch anti-inflammatory polypeptides G4, G5 and G17 screened by the method, especially G5, can significantly inhibit microglial cell inflammation and present concentration dependence, a theoretical basis is provided for medicinal development of Buthus martensii Karsch polypeptides, and a potential anti-neuroinflammation mechanism of the Buthus martensii Karsch polypeptides is disclosed.

The invention discloses an indole-benzothiadiazole derivative as well as a preparation method and application thereof. The general structural formula of the indole-benzothiadiazole derivative provided by the invention is shown in the specification, wherein R is selected from one of methyl piperazinyl, diethylamino, azetidinyl and pyrrolidinyl. The derivative can be specifically combined and fluorescently labeled with mitochondrial RNA in cells, and has good selectivity, high fluorescence signal-to-noise ratio and excellent anti-interference capability in a complex biological system. Meanwhile, the indole-benzothiadiazole derivative provided by the invention is simple in synthetic route, easily available in raw materials, good in stability and convenient to store and use, can be specifically combined with RNA and generate strong fluorescence response, is suitable for real-time detection and imaging of in-vitro and in-vivo mitochondrial RNA, and is an efficient mitochondrial RNA micromolecular fluorescent probe.

The invention relates to a development method of an insect culture medium, which comprises the following steps: S1, constructing a double-promoter tandem vector by taking pFastBac plasmid as a skeleton; s2, establishing a baculovirus expression system; S21, transforming the double-promoter tandem vector into DH10Bac escherichia coli, so as to obtain recombinant Bacmid; s22, transfecting an Sf9 cell by using the recombinant Bacmid, so as to obtain a P0-generation baculovirus; s23, transfecting an Sf9 cell by using the P0-generation baculovirus to obtain a P1-generation baculovirus; s3, screening insect culture media: S31, providing culture media to be detected, and respectively inoculating Sf9 cells into the culture media to be detected; s32, respectively inoculating the P0-generation baculovirus or the P1-generation baculovirus into each to-be-detected culture medium for culturing; and S33, after culture is finished, detecting virus antibodies in the culture media to be detected, and screening the culture media. A comprehensive and quantitative evaluation system is established for development of the insect culture medium.

The invention provides a cell growth dynamics prediction method and device based on a space-time image sequence, and belongs to the technical field of computer vision and deep learning. The method comprises the following steps: acquiring a multi-focal-plane space-time image sequence containing a cell complete growth process; preprocessing and normalizing images in the image sequence to obtain a final test image sequence; and inputting the test image sequence into a cell growth dynamics prediction model consisting of a multi-scale feature extraction module and a time sequence modeling module which are connected in sequence to obtain a cell growth stage identification result corresponding to each time point image in the sequence so as to realize cell growth dynamics prediction. The method can effectively solve the problems of poor adaptability, insufficient stability, high labeling cost and the like when a traditional model is used for processing the dynamic medical image sequence, and has remarkable advantages and application prospects in the aspect of dynamic prediction of the cell growth space-time image sequence.

The invention discloses a spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

Disclosed herein are methods and systems of determining a cancer status or an organ health status of a subject, the method comprising: obtaining a biological sample obtained or derived from the subject; enriching a population of cells in the biological sample, wherein the population of cells comprises stem cells and / or progenitor cells; extracting nucleic acids from the enriched population of cells; assaying the extracted nucleic acids to generate at least one of a transcriptomic profile of the subject, a genomic profile of the subject, and / or an exomic profile of the subject; computer processing the at least one of the transcriptomic profile of the subject, the genomic profile of the subject, and / or the exomic profile of the subject; and determining, based at least in part on the computer processing, the cancer status or the organ health status of the subject, wherein the cancer status or the organ health status comprises a presence or an absence of an organ and / or a tissue impacted by the cancer or impacted by a disease, disorder, or condition.

The embodiment of the invention provides an end-to-end B cell clone pedigree forest construction method and related equipment, and can be applied to the technical field of data processing. According to the method, a plurality of obtained receptor sequencing sequences are subjected to germline comparison identification to obtain a first test Fv sequence corresponding to each receptor sequencing sequence, and a germline Fv sequence corresponding to each receptor sequencing sequence is generated; performing integrity filtering on the first test Fv sequence, performing clone type division to obtain a plurality of first clone type sets, constructing corresponding first evolutionary trees to form a first pedigree forest on the basis of a second clone type set contract type conversion probability, and performing node optimization on all the first evolutionary trees to obtain a second pedigree forest; and after it is determined that the homotype category conversion probability after updating based on all the second evolutionary trees meets the preset requirement, visualization processing is performed on all the second evolutionary trees, so that the systematic cognition efficiency of related personnel on the adaptive immune response mechanism can be improved.