9 results about "Normal population" patented technology

An abnormality detection method for a hot plate for treating a substrate, the method comprising: a measurement step for measuring and storing changes in temperature in the hot plate during substrate treatment; a temperature integration step for integrating the changes in temperature during a plurality of substrate treatments stored in the measurement step; a normal population creation step for creating a normal population that is a normal integration result of a prescribed number of substrates integrated in the temperature integration step; and an abnormality detection step for creating an inspection target population that is an integration result of a predetermined number of inspection target substrates integrated in the temperature integration step, and detecting an abnormality related to the hot plate by comparing the inspection target population with the normal population.

The Endoscopic Adjustable Gastric Barrier represents a groundbreaking innovation in the management of obesity, offering a unique, minimal invasive approach without the complications and limitation of other currently available interventional managements., this revolutionary technique works by dividing the stomach into a smaller, tube-like space for food, without altering the stomach's natural anatomy or functions. This preserves the stomach's function to produce hormones and digestive juices, while effectively reducing food intake hence significant weight loss. What makes this innovation (EAGB) unique is its adjustability and reversibility, and when ideal weight is achieved it can be removed endoscopically. It is secured to the stomach by clips with minimal injury and without risk of necrosis and leak. Additionally, all endoscopic and laparoscopic interventions are possible as normal population.

The invention discloses an abdominal aortic aneurysm serum protein fingerprint detection and analysis method and system based on an LASSO regression algorithm. The method comprises the following steps: by optimizing a serum sample pretreatment process and combining an MALDI-TOFMS (Matrix-Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry) mass spectrometry technology and high-throughput proteomics data analysis, screening differentially expressed proteins related to occurrence, development and rupture of abdominal aortic aneurysm in serum; and further constructing a risk assessment model based on the key protein combination by using an LASSO machine learning algorithm. The method provided by the invention has the advantages of high sensitivity and good specificity, can effectively distinguish abdominal aortic aneurysm patients from normal people, can accurately predict the rupture risk of abdominal aortic aneurysm, especially early small aneurysm, and provides important tools and bases for clinical early diagnosis, risk stratification and personalized treatment decision.

The embodiment of the invention provides an autism auxiliary diagnosis method. The autism auxiliary diagnosis method comprises the following steps: constructing an individual brain function connection diagram; based on a community detection algorithm of a graph theory, performing functional region division on the individual brain function link graph to generate an individual brain map; taking the surface area percentage of the functional network of the healthy control group in the cerebral cortex as a response variable, taking the individual age as a smooth nonlinear independent variable, introducing an average head movement parameter as a linear covariable, and constructing a normal population development reference model; and comparing the surface area percentage of the functional network of the individual brain map with the normal population development reference model of the same age, and quantifying the deviation degree to realize auxiliary diagnosis of the autism. According to the autism auxiliary diagnosis method provided by the embodiment of the invention, individualized autism auxiliary diagnosis considering individual differences can be realized. The embodiment of the invention further provides an autism auxiliary diagnosis device and electronic equipment.

The application provides a method and device for generating a reference brain image, electronic equipment and a storage medium, wherein the method comprises: in response to a received evaluation instruction, obtaining an individual brain image to be evaluated indicated by the evaluation instruction; and inputting the individual brain image into a trained variational autoencoder model to obtain a reference brain image corresponding to the individual brain image output by the variational autoencoder model, the reference brain image being used to indicate brain features in a healthy state of the individual to be evaluated. The AI model is used to reconstruct the brain image of the individual to be evaluated based on brain features of a normal population, so that a more targeted reference brain image is obtained, a personalized normal reference brain image is formed, and the individual health evaluation is better assisted.

The invention discloses an intelligent abnormal psychological behavior screening method for college student groups, and relates to the technical field of psychological screening and artificial intelligence, and the method comprises the following specific steps: inducing a subject to respond through a standardized cognitive task; synchronously acquiring and processing eye movement and electroencephalogram signals; extracting multi-modal physiological features and carrying out fusion calculation to generate a time sequence cognitive coupling feature value; training a time sequence anomaly discrimination model based on the normal group data; and finally, the features of the person to be tested are input into the model, a discrimination coefficient is calculated, a grading screening result is output, and noninvasive, objective and efficient psychological state assessment and early warning are realized. According to the method, a non-invasive screening process suitable for a campus scene is constructed, multi-modal fusion and time sequence dynamic analysis are utilized, the accuracy and objectivity of college student psychological state assessment are improved, graded and personalized screening results and intervention suggestions can be output, and an efficient and quantifiable scientific tool is provided for campus psychological health management.

The present application relates to the technical field of nerve electrophysiological examination data processing, in particular to a method and system for nerve conduction examination data interpretation and diagnosis, first performing field extraction, term standardization and other preprocessing on the examination data to obtain a detection item set, performing pre-determination through a preset 15-class peripheral nerve injury disease diagnosis rule base, directly outputting results and determination basis if the diagnosis rule is met, and entering a model interpretation process if not; based on nerve conduction anatomy prior recognition, pairing relationship is identified and contrast features are generated, a direction consistent deviation matrix is constructed in combination with normal population data; multi-dimensional features are fused and a nerve conduction anatomy relationship graph is constructed, a relationship perception heterogeneous graph attention network coding is used by introducing edge type embedding, global features are extracted through contrast perception attention pooling enhanced by pairing difference to realize disease classification. The present application can improve interpretation efficiency and accuracy, reduce subjective errors, and provide objective auxiliary diagnosis support for neuromuscular diseases.

The invention discloses a divalent mercury hypersensitive whole-cell sensor based on a pigment signal and application of the divalent mercury hypersensitive whole-cell sensor in environmental sample detection. The invention provides a recombinant bacterium which is obtained by introducing a DNA fragment A and a DNA fragment B into a recipient bacterium, the DNA fragment A contains a Pmer gene, a merR gene and a vioABCE gene cluster; the Pmer is a bivalent mercury ion responsive bidirectional promoter, the merR gene expression is started in one direction, and the vioABCE gene cluster expression is started in the other direction; the DNA fragment B contains a constitutive promoter and a merC gene which is started and expressed by the constitutive promoter. The divalent mercury hypersensitive sensor constructed by the invention further reduces the sensing colorimetric detection limit and the naked eye direct reading judgment limit, and can be used for monitoring the mercury exposure of low-level normal people.

The application discloses a kind of abdominal aortic aneurysm serum protein fingerprint detection analysis method and system based on LASSO regression algorithm.The method is by optimizing serum sample pretreatment process, in combination with MALDI-TOFMS mass spectrometry technology and high-throughput proteomics data analysis, screening the differential expression protein in serum related to abdominal aortic aneurysm occurrence, development and rupture;Further utilize LASSO machine learning algorithm to construct risk assessment model based on key protein combination.The method of the application has the advantages of high sensitivity, good specificity, can effectively distinguish abdominal aortic aneurysm patients and normal population, and can accurately predict the rupture risk of abdominal aortic aneurysm, especially early small aneurysm, provide important tool and basis for clinical early diagnosis, risk stratification and personalized treatment decision.