7results about How to "High diagnostic sensitivity" patented technology

The invention provides an esophageal cancer marker screening method based on enzyme transfer method methylation sequencing, which comprises the following steps: S1, collecting esophageal early cancer and para-carcinoma tissue samples, and carrying out enzyme transfer method whole genome methylation sequencing; s2, integrating methylation data of esophageal cancer and para-carcinoma tissues in databases of TCGA, GEO, MethMarker DB and the like; s3, screening differential methylation sites, preferably sites which are hypermethylated in esophageal cancer but hypomethylated in samples such as leukocytes, so as to reduce plasma background interference; s4, obtaining a candidate marker combination based on an integrated methylation feature screening model of LASSO regression and a random forest algorithm; s5, constructing a classifier model based on the candidate marker combination, carrying out training and reasoning test on the model, and evaluating the prediction performance of the model; and S6, obtaining the esophageal cancer marker combination. According to the method, enzyme transfer sequencing and machine learning are combined, the marker prediction accuracy is remarkably improved, and a new reference is provided for clinical research of esophageal cancer.

Provided is a technique for detecting, with high sensitivity, a specific protein that can be used as a cancer marker. More specifically, the present invention provides a method and the like for detecting CEACAM1, the method comprising: (1) a step for modifying CEACAM1 in a sample containing CEACAM1 to produce modified CEACAM1; and (2) a step for measuring and detecting CEACAM1 by a sandwich method using both a capture antibody having the ability to recognize modified CEACAM1 and a labeled antibody.

The invention provides a flight control sensor electric signal abnormity detection method and system based on electric signal analysis, and the method comprises the steps: collecting the voltage and current signal data of an aircraft control sensor in real time, and carrying out the data preprocessing of the voltage and current signal data; inputting the preprocessed electric signal data into a pre-constructed nonlinear four-rotor kinetic model for analogue simulation, and outputting an electric signal data set containing fault information; based on the real-time feature vector in the electric signal data set and a pre-stored historical feature sample set, constructing a global adjacency graph containing real-time nodes and historical nodes; inputting the global adjacency graph into a pre-constructed graph convolutional network model, and extracting space-time fusion features of the real-time nodes; and carrying out classification processing on the space-time fusion features, and outputting an abnormal state category of a sensor electric signal, so as to achieve the purposes of online high-precision diagnosis and autonomous fault-tolerant control of aircraft sensor faults and guarantee safe and reliable operation of a complex equipment system.

The present invention belongs to the field of pharmaceutical technology, and particularly relates to a PET imaging agent precursor, a PET imaging agent, and a preparation method and application thereof. The structural formula of the PET imaging agent precursor of the present invention is: wherein M is a chelating group. The PET imaging agent precursor of the present invention has two cyclic peptide units, and the chelating group is linked by a glutamic acid. The distance between the two binding sequences in the dimer molecule is long enough so that it can simultaneously bind to the integrin receptor αvβ6 expressed on the cell surface, which can enhance the binding affinity and the tumor's uptake of the drug. The prepared PET imaging agent has a good ratio of target area to non-target area, lower normal tissue uptake and high tumor uptake, can be targeted and specifically taken up at the tumor site, and is more conducive to PET imaging of αvβ6-positive diseases.

The application discloses a shark-derived nanobody targeting CPV2c-VP2 protein and application thereof. The amino acid sequence of the complementarity determining region 3 of the shark-derived nanobody is selected from the sequence shown in any one of SEQ ID NO. 17 to SEQ ID NO. 32, or a sequence homologous to the sequence shown in any one of SEQ ID NO. 17 to SEQ ID NO. 32; or the amino acid sequence of the shark-derived nanobody is selected from the sequence shown in any one of SEQ ID NO. 1 to SEQ ID NO. 16, or a sequence homologous to the sequence shown in any one of SEQ ID NO. 1 to SEQ ID NO. 16. The shark-derived VNAR nanobody has the advantages of small molecular weight, high affinity, high stability, easy genetic engineering, low production cost and the like.

The application discloses a plunger pump wear fault detection method based on pressure-temperature layered triggering and physical prior mask self-attention, and belongs to the technical field of hydraulic system state monitoring and fault diagnosis. The method comprises the following steps: collecting vibration, pressure and temperature multi-source signals, and setting a default low power consumption of a main controller; realizing layered early warning and deep diagnosis awakening based on four dynamic characteristics of pressure and temperature and an abnormal proportion of a sliding window; performing synchronous compression wavelet transform on the vibration signal and performing temperature-pressure analytical linear real-time correction, block encoding and vibration Token sequence generation; generating an additive physical mask guided self-attention calculation based on a fault frequency band prior, and extracting wear sensitive features; comparing with a working condition grouping fault feature library to determine the fault type and grade. The application runs through the whole chain of signal correction, feature fusion and diagnosis decision with physical knowledge, and under the lightweight design of a model parameter quantity less than 500k, realizes a variable working condition diagnosis accuracy of 94.2% and an early wear false negative rate of 3.5%.

The invention relates to an exhaled air condensate metabolite combination, a kit and a method for predicting benign and malignant pulmonary nodules. The expired gas condensate metabolite composition for predicting benign and malignant pulmonary nodules comprises adenosine phosphate, adenine nucleotide, adenine, cytidine monophosphate, deoxyguanosine monophosphate, phosphatidylcholine 35: 2, cytidine ring monophosphate, hydroxyethyl piperazine ethanesulfonic acid, methylaminoisobutyric acid, fructose disodium phosphate, citrulline, ribitol and acetyl carnitine. The kit for predicting benign and malignant pulmonary nodules comprises an internal standard substance required for detecting the content of the metabolite combination. The combined marker provided by the invention has the characteristics of high sensitivity and high specificity on the diagnosis of tuberculous pleurisy. Meanwhile, the combined marker can be complementary with the traditional marker adenosine deaminase, and can be used for auxiliary diagnosis of tuberculous pleurisy by combined use.