157 results about "Differential expression" patented technology

The invention discloses a rapid detection method for traumatic brain injury by fusing genetic engineering and quantum dots, and relates to the technical field of traumatic brain injury detection. The method comprises the following steps: acquiring a suspected patient biological sample, extracting biomolecule components, and comparing the biomolecule components with a healthy population biomolecule database to obtain related differential expression molecules. A specifically recognizable recombinant antibody is constructed by genetic engineering, and is connected with a quantum dot through a coupling reaction to prepare a detection probe. The probe and a biological sample are mixed and reacted, and the intensity of a fluorescence signal generated by combination is collected by a fluorescence detection device. A standardized signal value is obtained through signal processing algorithm noise reduction and feature extraction, and a preliminary detection result is determined by combining a bioinformatics database evaluation sample. And finally, according to the differential expression molecule, the standardized signal value and the preliminary result, analyzing a data change trend in a preset time window by using a multivariable statistical model, and generating a diagnosis report. According to the method, various technologies are fused, and a new path is provided for traumatic brain injury detection.

The invention discloses a screening method of a protein marker combination, which is used for risk prediction of abdominal aortic aneurysm or ruptured abdominal aortic aneurysm, and optimizes the stability of a protein expression profile by constructing an initial protein expression profile; screening differential expression proteins by adopting statistical analysis, and preliminarily locking candidate proteins remarkably associated with a disease endpoint based on survival analysis or a risk regression model; performing cross validation by combining a sparse constraint algorithm and a nonlinear feature selection algorithm, and extracting a robust core marker; the independent contribution degree of each marker is evaluated through the standardized weight, and finally the optimal protein combination for disease prediction or diagnosis is determined. According to the method, six core proteins obtained through multi-algorithm cross screening can be detected in serum, multivariable logistic regression and external verification set performance both show good robustness, and an aorta protein fingerprint feature set which can be popularized and explained is formed.

The invention relates to a deep feature fusion-based fabricated component surface defect multi-task detection method. The method comprises the following steps of: constructing a multi-view camera array and an illumination feedback regulation and control module at a data acquisition end; on the algorithm level, feature cross-layer propagation and information compensation are realized through an improved lightweight convolutional network and a multi-scale residual diffusion module; a dynamic feature fusion module is introduced, and a self-adaptive fusion weight is generated based on channel statistical features, so that feature sharing and differential expression are realized among different tasks; meanwhile, a defect perception attention mechanism and cross-task consistency constraint are adopted, and the problem that semantic space distribution is inconsistent in the multi-task detection process is solved; in the detection post-processing stage, a three-dimensional quantitative evaluation system based on the geometric dimension, the texture roughness and the depth volume is constructed, and unified grade evaluation of the surface defects of the component is achieved through the multi-feature fusion quality index. The problems of low detection efficiency, unstable precision, difficulty in collaborative recognition of multiple types of defects and the like in existing component delivery detection are solved.

The invention relates to the technical field of biology, and provides a genetic marker for children nephrotic syndrome genetic risk assessment. The invention relates to genetic detection and application of hormone sensitive nephrotic syndrome (pSSNS) of children. Nine risk sites, including new sites of 1q23.1, 1p36.13, 5p13.2, 10q21.3, 10q24.1 and the like, highly related to diseases are found by integrating whole genome association research (GWAS) data, combining Meta analysis and a conjugate false discovery rate (conjFDR) method and taking genetic information of IgA nephropathy as assistance. Research results show that genes near the loci have differential expression in pSSNS and IgAN patients, which prompts that the genes play an important role in the occurrence and development of diseases. The invention provides a molecular detection method based on the risk site, which can be used for risk assessment, auxiliary diagnosis and prognosis prediction of children's nephropathy. Meanwhile, the invention provides potential application values of the loci and related genes thereof in individualized medication and targeted therapy.

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

The application discloses a marker for determining the severity of IgAN kidney tissue lesions and application thereof, and inventors find that ACTN4, ACADS and COL1A1 are significantly differentially expressed proteins in kidney tissues. The significant down-regulation of ACTN4 may cause the change of actin cytoskeleton of IgAN glomerular podocytes; the significant down-regulation of ACAD may indirectly participate in the occurrence process of abnormal structure and function of IgAN renal tubular epithelial cells by affecting fatty acid metabolism in the cells; and the significant up-regulation of COL1A1 may participate in the accumulation of extracellular matrix in the renal interstitium of IgAN, and play a certain role in promoting the renal interstitial fibrosis. The combination of ACTN4, ACADS and COL1A1 has good prediction efficiency for the diagnosis of the severity of IgAN kidney tissue lesions, and the area under the ROC curve is 0.815, P 0.043. In addition, the immunohistochemical results also confirm the expression trend of the three proteins ACTN4, ACADS and COL1A1 in the corresponding kidney tissue substructures in proteomics.

The invention discloses a method for researching influence on hemolytic activity of marine microalgae based on a transcriptome technology, and relates to the field of hemolytic activity analys.The method comprises the steps that pretreatment is conducted on the marine microalgae based on culture requirements, and cystic morphological cell density and hemolytic activity of the marine microalgae are measured according to experimental design rules after pretreatment is completed; carrying out transcriptome sample collection operation by utilizing the marine microalgae subjected to pretreatment, constructing a library according to a transcriptome sample collection result, and obtaining a gene function annotation and a differential expression gene result; the growth conditions of the marine microalgae under different temperature conditions are analyzed according to cystic morphological cell density and hemolytic activity, and the regulatory gene influencing the hemolytic activity of the marine microalgae is obtained by combining gene function annotation and differential expression gene results. According to the method, the hemolytic activity of the marine microalgae cultured under different temperature conditions is extracted, so that the aim of researching related metabolic pathways possibly participating in toxin synthesis and hemolytic activity regulation is fulfilled.

The invention provides a spatial transcriptome tissue specific expression gene identification method and system based on a multi-dimensional statistical index, and belongs to the technical field of gene identification, the method comprises the following steps: obtaining gene expression data corresponding to a plurality of genes in different cells, different cells belonging to different types of tissues; the gene expression data is data processed based on a space transcriptome; for each gene, determining a basic statistical feature and a differential expression feature corresponding to the gene based on the gene expression data corresponding to the gene in different cells; determining specific index characteristics of the gene based on the basic statistical characteristics of the gene; and determining a tissue specific expression gene from the plurality of genes based on the basic statistical characteristics, the differential expression characteristics and the specific index characteristics corresponding to each gene. According to the invention, the accuracy of tissue specific expression gene recognition can be improved.

The application discloses biomarkers for early diagnosis of lung adenocarcinoma and / or classification of unknown lung nodules and application thereof, and relates to the technical field of biological medicine.The biomarkers are piR-hsa-8429916 or piR-hsa-8393202.Two core biomarkers, piR-hsa-8393202 and piR-hsa-8429916, are successfully identified by screening and verifying piRNAs differentially expressed in lung adenocarcinoma tissues and serum, and the expression levels of the biomarkers are significantly positively correlated with tumor load.The diagnostic value of piR-hsa-8393202 and piR-hsa-8429916 in early diagnosis of lung adenocarcinoma and classification of lung nodules is significantly better than that of a traditional CEA marker.The application provides an innovative solution for early diagnosis of lung adenocarcinoma and risk stratification of lung nodules.

The application belongs to the field of plant biotechnology, and particularly relates to a cucumber CsODO1 gene and application thereof, and is a MYB transcription factor capable of being combined with a CsCSE1 promoter, which is screened through a yeast single hybridization. It is proved through EMSA and tobacco transient expression analysis that the CsODO1 gene directly and specifically combines with a "CAACCA" sequence in the CsCSE1 promoter, and inhibits the expression of the CsCSE1 gene. The CsODO1 gene can be induced to express by a powdery mildew fungus, and is differentially expressed in the cucumber infected by the powdery mildew fungus. It is found through a cucumber cotyledon transient transformation system mediated by an agrobacterium that the CsODO1 gene negatively regulates the biosynthesis of lignin and the resistance of the cucumber to the powdery mildew fungus. The application provides a new reference gene resource for the cultivation of a cucumber disease-resistant variety, and provides a new thought for the research on the molecular mechanism of the plant immunity mediated by the lignin pathway.

The invention provides a feature gene selection method and system based on deep learning attribution analysis and beam combination optimization, and relates to the technical field of signal analysis, and the method comprises the following steps: obtaining single cell RNA sequencing data and batch RNA sequencing data; the method comprises the following steps: based on single-cell RNA sequencing data, obtaining an initial gene pool aiming at a plurality of disease subtypes through a consensus screening strategy fusing XGBoost multi-dimensional importance measurement and deep learning SHAP attribution analysis; performing first-stage optimization on the gene list of each subtype by taking the initial gene pool as a starting point and adopting beam combination search and based on a global discrimination objective function to obtain a first-stage optimized gene set; integrating the first-stage optimized gene set with the differential expression gene set of the batch RNA sequencing data, and carrying out second-stage optimization by adopting bundle combination search again to obtain a second-stage optimized gene set; and performing cross validation integration on the second-stage optimized gene set, and outputting a final feature gene set.

The application discloses a kind of based on multi-omics deep learning colorectal cancer liver metastasis detection method and system, comprising: extracting the feature gene group with CRC transfer-metabolic dual function, and construct CRC transfer risk prediction model foundation, based on analysis CRC transfer risk probability to screen the molecular marker with CRC transfer nature and CRLM specific characteristics as initial candidate biomarker;Meanwhile, from the metabolome to obtain the differential expression metabolite corresponding to CRLM patient sample and LCRC patient sample metabolome, and the enrichment pathway analysis of transcriptome and metabolome is combined to construct CRLM multi-layer core metabolic network, for the screening core biomarker of colorectal cancer liver metastasis accurate detection in accordance with, entire method can effectively screen the biomarker for detecting colorectal cancer liver metastasis.

The invention discloses a method for analyzing serum differential expression protein characteristics of an AQP4-IgG positive NMOSD patient, and relates to the technical field of differential expression protein characteristic analysis, and the method comprises the following steps: S1, collecting a serum sample of a to-be-detected subject; s2, carrying out protein detection on the serum sample to obtain expression level data of at least one or more of PRDX2, CLU, ECM1, CFD, GPI and S100A8, wherein the expression level data is one or more of PRDX2, CLU, ECM1, CFD, GPI and S100A8; s3, carrying out comparative analysis on the expression level of the serum protein and a pre-established AQP4-IgG positive NMOSD serum protein expression characteristic reference standard; according to the method for auxiliary diagnosis of the AQP4-IgG positive neuromyelitis optica pedigree disease provided by the invention, a judgment mode capable of reflecting molecular characteristics of the AQP4-IgG positive neuromyelitis optica pedigree disease is constructed by carrying out conjoint analysis on various serum protein expression characteristics related to immunoregulation, inflammatory response, complement activation and metabolism; therefore, the problem of insufficient detection stability of a single biomarker is avoided.

A single-cell level spatial omics multi-modal fusion method, comprising: extracting differential expression genes and nuclear spatial morphological features from spatial transcriptome data, single-cell sequencing data and histological images, and realizing field adaptation between different platforms by using conditional variational autoencoder. Based on a probability inference model, spatial transcriptome expression, single-cell omics and morphological features are fused to jointly infer the type and gene expression level of each cell. A spatial cell network is constructed by a graph attention mechanism to realize the spatial diffusion and recognition of cell types in the whole slice range. Combined with a multi-omics enhancement module, the unmeasured gene and protein expression are completed based on expression similarity, and the prediction consistency is improved through spatial correction. The method realizes high-resolution reconstruction of single-cell multi-omics information in three-dimensional space, improves the information coverage and spatial resolution of spatial omics data, and provides an efficient and low-cost solution for spatial biology and precision medicine research.

The invention discloses a plasma protein marker for identifying or assisting in identifying parathyroid cancer and parathyroid adenoma and application of the plasma protein marker. The method comprises the following steps: firstly, collecting plasma specimens of a parathyroid carcinoma patient and a parathyroid adenoma patient before an operation, and screening through an unmarked quantitative proteomics technology to obtain 24 differential expression proteins; on the basis of the other part of plasma specimens of the parathyroid carcinoma patient and the parathyroid adenoma patient collected before the operation, the 24 differential expression proteins are verified through a parallel reaction monitoring technology; finally, three plasma protein markers (myoglobin, coronin-1A and a polyimmunoglobulin receptor) which can be used for identifying the parathyroid carcinoma and the parathyroid adenoma are obtained. Experiments prove that the plasma protein marker or the combination thereof can be used for auxiliary differential diagnosis of parathyroid carcinoma and parathyroid adenoma, and has important significance for improving the accuracy of preoperative diagnosis of parathyroid carcinoma.

The invention belongs to the technical field of medical diagnosis, and provides a biomarker of complete grape tire. The biomarker comprises RASA1, and the biomarker shows obvious differential expression in normal and complete grape tire trophoblast stem cells and normal and complete grape tire tissues; the method can be used for distinguishing complete grape tire, extremely early complete grape tire, non-grape tire villus edema and normal placenta tissue, and shows good robustness. Compared with the serum beta-hCG level, the expression level of the RASA1 shows higher diagnostic efficacy on complete grape tire detection. The biomarker disclosed by the invention provides a basis for developing targeted diagnosis and treatment in the future.

The invention discloses a colorectal cancer molecular marker and application thereof, and belongs to the technical field of biological medicine. The invention finds that ATP5MK has significant differential expression in colorectal cancer patients for the first time, can be used as a biomarker for colorectal cancer diagnosis, has the advantages of high accuracy, good specificity, high sensitivity and the like, provides a brand new idea and strategy for early diagnosis of colorectal cancer, and has good clinical application value.

The invention relates to the technical field of lion-head goose breeding, in particular to a method for screening and identifying genes related to lion-head goose sperm motility regulation and control, which comprises the following steps: dividing healthy lion-head male geese in a breeding period into a high sperm motility group and a low sperm motility group according to sperm motility, collecting two groups of testicular tissues and sperm samples, and preserving the testicular tissues at low temperature for molecular detection; performing transcriptome sequencing on the testicular tissue to obtain mRNA and non-coding RNA expression data; screening two groups of nucleic acid molecules with significant differential expression, and determining candidate genes through functional enrichment analysis of reproductive development related biological pathways; and verifying the authenticity of the expression difference of the candidate gene through fluorescent quantitative PCR, and finally determining the target gene. According to the method, related data are obtained through sequencing, the candidate genes are determined by screening differential molecules through enrichment analysis and PCR verification, accurate identification of a related gene system is achieved, and the problems of missing detection and insufficient accuracy caused by lack of transcriptome sequencing in a traditional method are solved.

A method for detecting and treating Type II Diabetes includes the use of hsa_piR_020485, a PIWI-interacting RNA isolated from urinary extracellular vesicles (ECVs), as a biomarker for the diagnosis and treatment of Type 2 diabetes mellitus (T2DM). The method is non-invasive, utilizing the differential expression of hsa_piR_020485 in diabetic versus non-diabetic subjects to identify subjects in need of treatment for T2DM. The method includes obtaining a urine sample from a subject, isolating urinary ECVs from the urine sample, extracting total RNA from the isolated urinary ECVs, quantifying hsa_piR_020485 levels from the total RNA, determining if the hsa_piR_020485 expression level exceeds a threshold, and administering one or more T2DM treatments.

The invention discloses a biomarker for predicting or diagnosing early pregnancy of sows and application of the biomarker. The biomarker is selected from at least one of miRNA (micro Ribonucleic Acid) novel-235, miRNA novel-878 and miRNA novel-1215. The invention further discloses a preparation method of the biomarker. These biomarkers exhibit an expression difference in saliva extracellular vesicles of a sow population, and this expression difference is associated with sow pregnancy. The miRNAs are developed into molecular markers for early detection of sow pregnancy, and the pregnancy condition can be conveniently detected. The embodiment of the invention further provides a primer and a kit for rapid detection of early pregnancy of the sow, and a new way is provided for diagnosis of early pregnancy of the sow.

The invention discloses a biopsy muscular fascia sample analysis method and system, and relates to the field of muscular fascia sample analysis. The biopsy myofascia sample analysis method comprises the following steps: acquiring pre-stored RNA data of a plurality of biopsy myofascia sample groups; carrying out RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascia sample group to obtain transcriptome data; performing inter-group difference analysis on the transcriptome data according to a preset comparison group, and extracting a differential expression gene data set; and carrying out BDNF layered enrichment analysis on the differential expression gene data set to obtain a BDNF enrichment significant set of each contrast biopsy myofascia sample group, and carrying out cross validation processing based on the BDNF enrichment significant set of each contrast biopsy myofascia sample group. Therefore, it is effectively ensured that the screened BDNF pathway target spot is highly matched with the pathological characteristics of myofascia lesion and is clearly associated with intervention response, and the potential curative effect of acupuncture intervention is accurately evaluated.

The invention belongs to the technical field of biological medicine, and particularly relates to a serum exosome circular RNA marker and application thereof. The invention relates to a serum exosome circular RNA (Ribonucleic Acid) marker for diagnosing or predicting bronchial lung dysplasia of a premature infant. The circular RNA marker is selected from at least one of the following components: hsacirc0001522; the molecular marker is Hsacirc0001359. By finding differentially expressed cyclic rna in serum exosomes of BPD infants and non-BPD infants on the 7th day, the 14th day and the 28th day, cyclic rna is differentially expressed in the serum exosomes of the BPD infants and the non-BPD infants. The invention relates to the field of biomarkers, in particular to hsacirc0001359, which can be used as a very promising and easily available biomarker and can be used for predicting BPD on the seventh day after birth.

The invention discloses a construction method and application of an animal model with delayed embryonic development, and relates to the technical field of animal models.The construction method of the animal model with delayed embryonic development comprises the following steps that S1, an early embryo is obtained; s2, performing contact culture on the early embryo and a development retardation inducer; s3, culturing to obtain a slowly developed embryo; the proteomics analysis method based on non-data-dependent acquisition is used for analyzing an animal model with slow embryonic development. The method comprises the following steps: providing an embryo sample; obtaining a protein expression profile by adopting a non-data-dependent acquisition proteomics method; according to a protein expression profile, identifying differential expression proteins related to the embryonic development retardation; the animal model with the delayed embryonic development is constructed, and an experimental model is provided for researching a molecular mechanism of the delayed embryonic development; a non-data-dependent collection proteomics analysis method is applied to the animal model, and compared with a traditional data-dependent collection method, the method has higher stability.

The application provides application of circ-0007527 as a target in screening of drugs for treating peritoneal dialysis related peritoneal fibrosis. The application also provides application of an inhibitor of circ-0007527 in preparation of a drug for treating peritoneal dialysis related peritoneal fibrosis. In a mouse peritoneal fibrosis model induced by high glucose peritoneal dialysis solution, circRNA expressed in peritoneal tissues is screened, and after bioinformatics analysis, it is found that the differential expression amount of circ-0007527 in the fibrotic peritoneal tissue is the highest, and the high expression of circ-0007527 in the fibrotic peritoneal tissue is verified by real-time fluorescent quantitative PCR. Further in-vitro cell experiments find that down-regulation of the expression of circ-0007527 can significantly inhibit peritoneal mesothelial cell phenotype transformation, proliferation and apoptosis, and thus reduce peritoneal injury and delay peritoneal fibrosis progression.

The invention discloses a colorectal cancer liver metastasis detection method and system based on multi-omics deep learning, and the method comprises the steps: extracting a characteristic gene group with CRC metastasis-metabolism dual functions, and on the basis of constructing a CRC metastasis risk prediction model, screening molecular markers with CRC metastasis general and CRLM specific characteristics based on the analysis of CRC metastasis risk probability, the biomarker is used as an initial candidate biomarker; meanwhile, a CRLM patient sample and differential expression metabolites corresponding to an LCRC patient sample metabolome are obtained from a metabolome, and a CRLM multilayer core metabolic network is constructed in combination with enrichment pathway analysis of a transcriptome and the metabolome, so that core biomarkers are screened for accurate detection of colorectal cancer liver metastasis; the whole method can effectively screen the biomarker beneficial to detection of colorectal cancer liver metastasis.

The present disclosure relates to a method for diagnosing and treating multiple-system atrophy (MSA) in a subject, the method comprising: determining in a subject-derived biological sample an expression level of a gene from the group consisting of: QKI, GGCX, MOCS1, NF1, LINC01572, PRRG3, HMBOX1, PLP1, PPP1CA, C8orf88, TGFB2, MASP1, TIAM1, SYNGAP1, ACTN1, EMP1, NFIL3, GPNMB, PGAM2, ST5, STON1, RFTN1, and MMP14; comparing the subject-derived expression level of the gene with a normal control expression level of the gene obtained from a non-neurodegenerative biological sample; diagnosing the subject as a having MSA by detecting a differential expression of the gene in the subject-derived biological sample as compared to the normal control expression level; and administering a peroxisome proliferator-activated receptor β (PPARβ) agonist or a retinoid X receptor (RXR) agonist to the subject diagnosed as having MSA.

The invention discloses a one-stop tumor RNA-seq analysis system which comprises an original data quality control module, a sequence comparison and quantification module, a fusion gene analysis module, a variable shear analysis module, a differential expression analysis module, a function enrichment analysis module, an immune pathway module, a data management and visualization module and a containerization safety deployment module. The system receives an FASTQ format of original RNA-seq data, carries out quality evaluation and filtering, generates high-quality clean reads, generates gene expression quantitative data through sequence alignment, carries out differential expression, function enrichment and other analysis on the gene expression quantitative data, and carries out data management, visualization and safe deployment on an analysis result. The analysis system is high in data analysis efficiency and credible in analysis result, so that scientific researchers and biomedical workers can perform tumor RNA-seq data analysis more conveniently.

An ornithine decarboxylase gene related to Auricularia auricula-judae degradation and application thereof belong to the field of bioengineering technology. In order to solve the technical problem that there is lack of key functional genes and molecular targets which can be used for early identification, anti-degradation molecular breeding and strain rejuvenation due to continuous subculture leading to degradation in Auricularia auricula-judae industry, based on the G1, G10 and G20 mycelium transcriptome difference analysis of Auricularia auricula-judae strain Heiwei S1908, the ornithine decarboxylase gene g4162 significantly differentially expressed between normal mycelium and G20 degraded mycelium is screened, the molecular function of the gene in the degradation of Auricularia auricula-judae subculture is determined, and the system biology information prediction of the gene coding protein is completed, which provides core gene resources and theoretical basis for the degradation mechanism research, molecular detection kit development, anti-degradation germplasm screening and gene breeding of Auricularia auricula-judae.

The application relates to a screening and determining method of an ischemic cerebral stroke animal model biomarker of qi deficiency and blood stasis syndrome, which integrates proteomics and transcriptomics technologies, systematically screens differentially expressed proteins and genes related to the syndrome, and on the basis, constructs various disease animal models, verifies candidate targets by adopting ELISA, simultaneously combines qPCR technology, multi-dimensionally confirms expression changes in the models, further draws ROC curves, calculates AUC values, sensitivity and specificity and other diagnostic performance indexes, evaluates diagnostic potential, and further knocks down and overexpresses differentially expressed genes, deeply excavates potential biomarkers with diagnostic values, and provides scientific bases for objective diagnosis of ischemic cerebral stroke qi deficiency and blood stasis syndrome and exploration of molecular mechanisms.

Provided herein are methods for diagnosing pre-eclampsia, methods for determining the risk of a subject developing pre-eclampsia, as well as methods for preventing and treating pre-eclampsia should the subject be found to have, or be at risk of developing, pre-eclampsia. The methods involve determining a level of at least three transposable element subfamilies in a sample, wherein differential expression of the transposable element subfamilies relative to reference values indicates that the subject is at risk of developing, or has, pre-eclampsia.