16 results about "Asymptomatic" patented technology

The application relates to a tracing method for latent period asymptomatic transmission based on an SAIR model, which comprises the following steps: S1, determining a single initial transmission source node in an interpersonal relationship complex network, and transmitting according to the source node according to an SAIR model until a moment t0; S2, randomly selecting a certain number of nodes as observation nodes, and knowing the state of the observation nodes at the moment; S3, screening a candidate source node set in the complex network by using a reverse propagation algorithm according to the state of the partial observation nodes, and the hop distance and the effective distance, and the number of nodes is determined by a sampling method; S4, constructing a dynamic message transmission equation according to the SAIR model, and calculating the probability of the candidate source nodes being in different states at a moment Delta t by using the partial observation nodes; and S5, calculating the likelihood probability of the nodes in the candidate source node set becoming sources by using an average field theory, and the source is the node with the maximum probability. According to the application, the spread of infectious diseases of patients with latent period and asymptomatic can be accurately reflected, and the problem of tracing the source of infection can be solved.

In one aspect, the disclosure relates to compositions and methods for dispersing exiting Salmonella biofilms and inhibiting formation of Salmonella biofilms. In various aspects, the disclosed compositions can be used in methods of treating a persistent Salmonella infection, including an asymptomatic infection. Such infections can colonize a variety of tissues, including the gall-bladder. Also disclosed are methods of treating typhoid fever. Also disclosed are methods for mitigating or preventing secondary outbreaks of typhoid fever by treating asymptomatic subjects who had been symptomatic for typhoid fever at a previous time. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.

The invention belongs to the technical field of molecular biological detection, and relates to a polypeptide for detecting asymptomatic infection of plasmodium vivax malaria and application. The polypeptide for detecting the asymptomatic infection of the malaria of the plasmodium vivax is an amino acid sequence as shown in SEQ ID No.1, or a homologous sequence which is subjected to substitution, deletion or insertion of one or more amino acids or has the sequence similarity greater than 75%. The polypeptide has an amino acid sequence as shown in SEQ ID No. 1. The polypeptide SEQ ID No.1 in the invention specifically detects asymptomatic tertian malaria infection, and can effectively deal with tertian malaria input threats in the Burmese border area. Meanwhile, the polypeptide can be coated in a detection tool of a rapid detection kit, and the tool has the characteristics of portability, high efficiency, low cost and the like, and is suitable for field sample detection in a malaria prevalence area.

To provide molecular signatures of the asymptomatic phase of Alzheimer's disease, methods for diagnosing the asymptomatic phase of Alzheimer's disease in a subject, for predicting the progression of the asymptomatic phase of Alzheimer's disease and for determining a personalized course of treatment in a subject suffering from the asymptomatic phase of Alzheimer's disease, and a computer system comprising a machine learning algorithm trained to diagnose the asymptomatic phase.SOLUTION: The computer system comprises the steps of receiving inputs of the levels, amounts or concentrations of the at least five biomarkers as determined in a sample obtained from the subject, analyzing and transforming the inputs of the levels, amounts or concentrations of the at least five biomarkers by organizing and / or modifying each level, amount or concentration input via a machine learning algorithm to derive a probability score and / or a classification label, and generating an output which is a classification label or a probability score.SELECTED DRAWING: Figure 7

Disclosed herein are systems, methods, and devices of detecting illness in presymptomatic and asymptomatic infected persons using wearable sensor technology. In a first embodiment, a method is implemented on a computing device, the method includes receiving first sensor data associated with the person over a first period of time and applying the first sensor data to a multi-variate detection model. The method further includes receiving second sensor data associated with the person over a second period of time and applying the second sensor data to the multi-variate detection model. Further the method includes determining a probability value of the illness in the person using the multi-variate detection model and transmitting the probability value to a user interface (UI).

The present invention provides biomarkers allowing the diagnosis of symptomatic congenital cytomegalovirus (CMV) infection and in particular to differentiate between symptomatic and asymptomatic infected fetuses, methods of diagnosis of CMV using said biomarkers, diagnostic kits comprising thereof, and use of the kits and methods of diagnosing fetuses infected with a symptomatic congenital cytomegalovirus (CMV).

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.

This invention provides the application of human protocadherin 18 (PCDH18) as a biomarker in the preparation of products for the diagnosis, auxiliary diagnosis, or progression prediction of antiphospholipid syndrome (APS), belonging to the field of in vitro diagnostic technology. In this invention, detecting serum PCDH18 levels in patients is helpful in diagnosing APS and is specific to this disease, and can be used to differentiate APS from other connective tissue diseases. Furthermore, APS patients have higher serum PCDH18 levels compared to asymptomatic aPL-positive patients. Therefore, PCDH18 testing in aPL-positive individuals is helpful for the early diagnosis and intervention of APS. Additionally, serum PCDH18 levels in OAPS patients are significantly higher than in TAPS patients, suggesting that PCDH18 can serve as a biomarker for differentiating or assisting in the differentiation of TAPS and OAPS.

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 invention discloses a marker and a probe composition for ovarian cancer and application of the marker and the probe composition. The marker is LYPLAL1-DT. 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 peripheral blood free DNA, 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; the composition has higher sensitivity and accuracy, and can realize real-time monitoring.

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.

This invention discloses a metabolomics-gut microbiome biomarker combination for the diagnosis of HIV-related asymptomatic neurocognitive impairment (ANI), comprising metabolomics biomarkers and gut microbiome biomarkers. The metabolomics biomarkers include plasma and fecal biomarkers. Plasma biomarkers include Asp-Asn and arachidonic acid, while fecal biomarkers include histidine, lysine, ornithine, and indolelactone. The gut microbiome biomarkers include phylum-level and genus-level biomarkers. Phylum-level biomarkers include Firmicutes, Actinobacteria, and Proteobacteria, while genus-level biomarkers include *Clostridium*, *Microbacterium*, *Klebsiella*, and *Prevotella*. This biomarker combination integrates the core differential features of metabolomics and gut microbiota, exhibiting high diagnostic specificity and sensitivity. It can effectively distinguish ANI patients from CI patients and healthy individuals, providing a clear target for HAND targeted intervention and possessing significant clinical application value.