7 results about "Asymptomatic" patented technology

An aortic aneurysm carries increasing risk of rupture with growing aneurysm diameter. This condition is typically asymptomatic, so screening and surveillance are essential. Ultrasound and other imaging methods are employed for such monitoring at high accuracy. However, these methods require an expert and are expensive. Aortic aneurysms are considerably under-detected at present and may become even more under-detected in the future as the disease prevalence increases with societal aging. This disclosure present devices that are convenient in use and cost for aortic aneurysm screening and surveillance.

A viral exposure signature (VES) that can identify early stage, pre-symptomatic hepatocellular carcinoma (HCC) among at-risk patients is described. The VES was developed using serological profiling and synthetic virome technology to identify unique viral peptide epitopes corresponding to 61 viral species. Methods of identifying a subject with early stage (pre-symptomatic) HCC using the VES are described.

The invention discloses a marker and a probe composition for early diagnosis of gastric cancer and application of the marker and the probe composition. The marker is an ST8SIA1 gene and / or an SPAG4 gene. According to the present invention, with the application of the marker, the methylation state of the gene can be sensitively and specifically detected so as to be used for the detection of the DNA of the plasma sample of the gastric cancer patient, and the composition is used for the screening of the asymptomatic population in the non-invasive manner so as to reduce the harm caused by the invasive detection, and further improve the detection efficiency. The composition has higher sensitivity and accuracy, and can realize real-time monitoring.

This invention discloses a biomarker for evaluating the immune status of COVID-19 vaccines and its application. The biomarker includes any one or a combination of at least two of the following SARS-CoV-2 viral peptides: M1, N16, N24, S15, S39, S44, S64, S82, S95, S104, or S115. This invention accurately identifies differences in peptide-specific antibody responses between asymptomatic and symptomatic patients. Through clustering heatmaps, it determines the differences in responses to peptides such as M1, N24, S15, S64, S82, S104, and S115 between vaccinated individuals and infected individuals. This effectively distinguishes between infected patients and vaccinated individuals, as well as between asymptomatic and symptomatic patients, providing a reference for COVID-19 treatment and peptide vaccine development.

The invention provides a method and a system for predicting the growth risk of accidental asymptomatic meningioma. The method comprises the following steps: acquiring baseline clinical data, iconomic semantic feature data and iconomic feature data of a patient suffering from accidental asymptomatic meningioma; dividing the patient queue into a training set, a verification set and a test set, screening independent prediction factors significantly related to tumor growth, training a random survival forest model based on the training set, and performing optimization and verification in the verification set and the test set to generate an optimal model for predicting the individualized time-dependent growth risk; and inputting related data of a patient to be predicted into the optimal model, and outputting meningioma growth risk assessment results at different time points in the future. According to the invention, the technical problem that a patient with primary asymptomatic meningioma lacks an individualized natural growth trend prediction tool is solved.

The present application relates to the technical field of infectious disease monitoring, and specifically relates to an infectious disease monitoring method for international ship crew, comprising: calibrating all crew members on the ship to be in a healthy state during a calibration period; collecting the body temperature and SDNN data of all crew members during the calibration period through a monitoring bracelet, and establishing a physiological baseline database of the ship; judging the physiological index abnormality degree of individual crew members; calculating the group abnormality degree according to the physiological index abnormality degree of individual crew members; calculating a risk index according to the group abnormality degree; setting an early warning value, and comparing the risk index with the early warning value; the present application takes the ship as a monitoring unit, adopts individual and group double-index joint determination, and reduces the false alarm rate; as long as the overall biological rhythm of the whole ship appears "resonance", the body temperature and / or SDNN of most people simultaneously rise or fall, even if the number of abnormal people is 0, the existence of the pathogenic risk can be determined, the weak group signal caused by asymptomatic infected persons can be captured, and the early warning effect is improved.

The application provides an early-stage dynamic reconstruction method of infectious diseases based on a population model, and belongs to the technical field of infectious disease modeling and risk assessment. Compared with the traditional population model which only focuses on a single dimension such as space or age, the application simultaneously considers age grouping, city migration and multiple disease states, significantly improves the model resolution and is more consistent with the actual transmission scenario, such as the cross-age and cross-city transmission characteristics of COVID-19. The application introduces asymptomatic infected persons and pre-symptom period states, quantifies their relative infectivity and more accurately depicts the early-stage transmission dynamics than the traditional model. The application uses a differential evolution adaptive metropolis algorithm (DREAM algorithm) to replace the traditional Markov chain Monte Carlo method (MCMC), improves the parameter convergence speed through multi-chain parallel search and adaptive parameter adjustment, and is particularly suitable for rapid fitting under the condition of sparse early-stage data.