71 results about "Biomarker panel" patented technology

The present disclosure provides methods and compositions, e.g., kits, for diagnosing colorectal cancer or precancerous colorectal advanced adenomas based on a subject's biomarker panels in a biological sample (e.g., a feces sample or a blood sample).

Alzheimer's disease, the most common cause of dementia in older individuals, is a debilitating neurodegenerative disease for which there is currently no cure. In the past, AD could only be definitively diagnosed by brain biopsy or upon autopsy after a patient died. These methods, which demonstrate the presence of the characteristic plaque and tangle lesions in the brain, are still considered the gold standard for the pathological diagnoses of AD. However, in the clinical setting brain biopsy is rarely performed and diagnosis depends on a battery of neurological, psychometric and biochemical tests, including the measurement of biochemical markers such as the ApoE and tau proteins or the beta-amyloid peptide in cerebrospinal fluid and blood. The present invention discloses and describes panels of makers that are differentially expressed in the disease state relative to their expression in the normal state and, in particular, identifies and describes panels of makers associated with neurocognitive disorders. Such biomarker panel might have considerable value in triaging patients with early memory disorders to yet more specific but more invasive and costly approaches such as molecular markers in CSF and on PET imaging in clinical trials and possibly in clinical practice.

The present invention provides a method for detecting the presence of or risk of bladder cancer in a female patient, the method comprising the steps of: detecting the presence of a biomarker panel in a sample isolated from a female patient, the biomarker panel comprising IL-13 and IL-12p70 and one or more biomarkers selected from the group consisting of BTA, midkine, PAI-1 / tPA, 8OHdG, CEA, CK18, Fibrillin, Creatinine, CXCL16, Cystatin B, Cystatin C, d-dimer, EGF, FAS, HAD, IL-1a, IL-1b, IL-4, IL-6, IL-7, IL-8, MCP-1, Microalbumin, MMP9 NGAL, MMP9 TIMP1, NGAL, NSE, Progranulin, TUP, TGFB1, Thrombomodulin, sTNFR1, TPA, VEGF and Triglyceride, and / or the concentration of albumin / microalbumin / protein to creatinine in a sample isolated from a female patient, expressed as an albumin:creatinine ratio; and assessing the result and comparing it to a normal control, wherein an increase in the presence of the biomarkers compared to the normal control indicates the presence or risk of cancer in the patient from which the sample was isolated.

The invention discloses a diabetic retinopathy diagnosis and analysis platform based on double body fluids and a method for analyzing a biological fluid sample by using the diabetic retinopathy diagnosis and analysis platform, and relates to the field of biological diagnos.The diabetic retinopathy diagnosis and analysis platform comprises an acquisition and analysis module and a machine learning module, the acquisition and analysis module acquires mass spectrum data of a biological sample through a mass spectrum system for analysis, and the machine learning module analyzes and processes the acquired mass spectrum data of the biological sample, constructs a diabetic retinopathy biomarker panel, and analyzes the diabetic retinopathy biomarker panel to obtain the diabetic retinopathy biomarker. The invention also comprises a method for analyzing a biological fluid sample by using the analysis platform, the biological fluid sample comprises vitreous humor and plasma, and the nanoparticles are used as a matrix of the biological fluid sample for mass spectrum data acquisition. The invention provides a rapid, stable, reliable and high-sensitivity vitreous humor metabonomics detection method and system.

To provide a method for identifying a therapy recommendation for a subject exhibiting dysregulated host response.SOLUTION: A classification of the subject of subtype A, subtype B, or subtype C is obtained. The therapy recommendation for the subject is identified based at least in part on the classification. Responsive to the classification of the subject comprising the subtype A, the therapy recommendation can be no immunosuppressive therapy. Responsive to the classification of the subject comprising the subtype B, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, blocking of complement activity therapy, and / or anti-inflammatory therapy. Responsive to the classification of the subject comprising the subtype C, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, modulators of coagulation therapy, and / or modulators of vascular permeability therapy.SELECTED DRAWING: Figure 1A

The present invention discloses a biomarker, a kit and a system for predicting poor prognosis of ischemic stroke patients, which relate to the technical field of molecular diagnosis. The biomarker group disclosed by the present invention includes a protein biomarker group and a single nucleotide polymorphism (SNP) biomarker group. Using the biomarker group, kit and system provided by the present invention can better predict the risk of death or severe disability occurring 3 months after the onset of ischemic stroke patients, so as to early identify high-risk patients with poor prognosis of ischemic stroke, and guide doctors to take corresponding treatment and nursing measures to prevent the occurrence of death or severe disability after the onset of ischemic stroke patients, which has important clinical and public health significance.

The disclosure provides biomarker panels, methods and kits for determining the probability for preterm birth in a pregnant female. The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing in the future or presently suffering from preterm birth relative to matched controls. The present disclosure is further based, in part, on the unexpected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preterm birth in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting a probability of preterm birth, monitoring of progress of preterm birth in a pregnant female, either individually or in a panel of biomarkers.

Disclosed is a machine learning method for curating biomarker panel, for screening of cancer(s) and system therefor, addressing the critical need for early detection through a combination of innovative biomarker research, advanced computational algorithms and integration with existing healthcare infrastructure. The biomarker panel is curated to facilitate pan-cancer screening of the patient with high sensitivity and specificity using the ML method deploying MoE (Mixture of Expert) models for accurate screening, thereby enhancing diagnostic accuracy and also empowering clinicians with actionable insights, thereby improving patient prognosis and facilitating personalized treatment strategies.

Panels of biomarkers, methods and systems are disclosed for determining gene expression, and diagnosing and treating melanoma.

The invention discloses a machine learning-based tumor early screening expiration VOCs marker identification and verification method. The method comprises the steps of obtaining and processing high-dimensional full-spectrum expiration VOCs data; a generalized linear model is adopted to evaluate the influence of the queue correlation difference on the expiratory VOCs data, and VOCs with significant interaction are removed; screening potential expired VOCs biomarkers with significant difference between an experimental group and a control group by adopting a difference volcano plot method in which a kernel weighting function is introduced; a Boruta feature screening algorithm based on a random forest is adopted, and an expiration VOCs biomarker panel most related to the tumor is constructed; and evaluating the tumor diagnosis and grading performance of the expired VOCs biomarker panel by adopting a machine learning algorithm based on multiple underlying logics. According to the method, a marker panel construction process based on a machine learning algorithm is created, and a biomarker panel suitable for various algorithms is accurately and efficiently captured from massive expired VOCs.

This invention provides methods of detecting biomarkers in the biofluid of sepsis-associated encephalopathy (SAE) patients, including but not limited to glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase LI, Tan protein, Neurofilament light chain (NF-L), myelin basic protein (MBP), secretogranin, Copeptin, total all-spectrin, all-spectrin breakdown products (SBDP, including SBDP145, SBDP150, SBDP120 all-spectrin N-terminal fragment or SBDP150N), neuron specific enolase (NSE), mature brain derived neurotrophic factor (BDNF), and full-length Pro-BDNF. These biomarker peptides are markers of axonal and blood brain barrier integrity which can be used to diagnose SAE and to assess and predict cognitive performance and outcomes in acute presentations of sepsis.

Provided herein are methods, systems, and compositions for detecting one or more HDL-associated proteins (e.g., ApoC3; ApoC3 and ApoA1; ApoC3 and SAA1 / 2; or proteins in Biomarker Panels 1-30) in a sample from a subject with, or suspected of having, cardiovascular disease (CVD) or other HDL related disease. In certain embodiments, such methods, systems, and compositions are used to determine the approximate risk of CVD (or other disease) for a subject, and / or the approximate cholesterol efflux capacity (CEC) of a sample.

Provided herein are methods, systems, and compositions for detecting one or more HDL-associated proteins (e.g., ApoC3; ApoC3 and ApoA1; ApoC3 and SAA1 / 2; or proteins in Biomarker Panels 1-30) in a sample from a subject with, or suspected of having, cardiovascular disease (CVD) or other HDL related disease. In certain embodiments, such methods, systems, and compositions are used to determine the approximate risk of CVD (or other disease) for a subject, and / or the approximate cholesterol efflux capacity (CEC) of a sample.

Disclosed herein are methods for analzying quantitative expression values of biomarkers of a biomarker panel for determining disease activity in a human subject. Further disclosed herein are kits for measuring quantitatative expression values of the markers as well as computer systems and software embodiments of predictive models for determining disease activity in human subjects based on the qualitative expression values of the markers.

The present disclosure provides biomarker panels, methods and kits for determining the probability for preeclampsia in a pregnant female, including preterm preeclampsia or preeclampsia at any gestational age. The disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing, in the future, or presently suffering from, preeclampsia relative to matched controls. The disclosure is also partially based on the unexpected discovery that panels combining one or more of these proteins / peptides can be utilized in methods of determining the probability for preeclampsia in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting a probability of preeclampsia, monitoring of progress of preeclampsia, either individually or in a panel of biomarkers.

The present invention relates to the classification of cancers, in particular prostate cancers, using samples from patients. In particular, the invention provides methods for identifying potentially aggressive prostate cancers to determine which cancers are or will become aggressive (and hence require treatment) and which will remain indolent (and will therefore not require treatment). The present invention is therefore useful to identify patients with a poor prognosis. The specific population of cancer identified by the present invention is referred to herein as DESNT cancer. The invention also provides biomarker panels useful in the diagnosis and prognosis of cancer. 20 24 20 01 50 10 J an 2 02 4 1 0 0 5 0 7 3 3 7 8 J a n 2 0 2 4 A B S T R A C T 2 0 2 4 2 0 0 1 5 0 1 0

In recent years, in an attempt to identify new therapies and more accurate biomarkers for the early diagnosis and treatment of liver diseases, to date, no biomarkers exist that allow for a definitive determination of the prognosis of patients with hepatic fibrosis. The present invention relates to a panel of biomarkers for in vitro testing in the diagnosis of liver disease, particularly for clinical diagnosis, with an emphasis on in vitro tests for diagnosing diseases associated with fibrosis, such as the detection of hepatic fibrosis processes using non-invasive methods in patients, as well as in individuals without known liver disease.

This invention belongs to the field of multi-omics data integration technology. It discloses a system for screening multiple disease biomarkers from the gut microbiota metabolome. The system performs batch-to-batch drift correction based on the comparison values ​​of internal reference metabolites on metabolomic mass spectrometry data from multiple disease and healthy control groups. Then, it calculates the covariant stability index between metabolites and microbial species, and constructs a constrained metabolite association network by combining metabolic pathway topological distance. Based on this, the system integrates pathway constraint betweenness centrality and disease effect size to calculate network-enhanced discriminant scores, performs dimensionality reduction according to the principle of maximum coverage and minimum redundancy, and simultaneously evaluates the stability baseline values ​​of biomarker-related ecological functional modules. Finally, using ecological stability as a constraint, the system optimizes the discriminative power of multiple diseases through marginal contribution evaluation, outputting a balanced and efficient biomarker panel. This invention achieves systematic integration of microbiome and metabolome data, providing a reliable tool for the accurate diagnosis of digestive system diseases.

This invention discloses a gene biomarker panel for detecting PCNSL based on cerebrospinal fluid (cerebrospinal fluid) ctDNA, relating to the fields of gene detection and molecular diagnostics. The gene biomarker panel includes MYD88, PIM1, CD79B, GNA13, IRF4, DTX1, KMT2D, and B2M. By detecting the mutation status of these genes in a subject's cerebrospinal fluid sample, the mutation status is input into a trained random forest classification model to generate auxiliary judgment results, providing molecular evidence for diagnosing PCNSL in the subject. This eight-gene combination panel has shown high diagnostic specificity in multiple cerebrospinal fluid ctDNA-based validation cohorts and has the ability to identify MYD88 wild-type PCNSL.

Provided are methods of assessing future onset or progression of an ocular health condition in a subject, comprising obtaining a tear fluid sample from the subject; measuring, in the tear fluid sample, concentrations of a plurality of biomarkers of a biomarker panel, wherein the biomarker panel comprises at least two or more biomarkers from the group consisting of Nerve Growth Factor (NGF), Interleukin-8 (IL-8), Tumor Necrosis Factor-Alpha (TNF-α), and Interleukin- 13 (IL-13); comparing the measured biomarker concentrations to condition-specific signature criteria; and based on the comparison, identifying the subject as having proclivity to future onset or progression of: (i) Contact Lens Discomfort (CLD) when the measured NGF concentration and the measured IL-8 concentration meet a first condition-specific signature criterion, or (ii) Dry Eye Disease (DED) when the measured NGF concentration, the measured TNF-α concentration, and the measured IL-13 concentration meet a second condition-specific signature criterion.

The invention provides a biomarker screening method and system, electronic equipment and a medium, and the method comprises the following steps: carrying out preliminary screening on multi-modal biological sample data to obtain preliminary screening data, and preprocessing the preliminary screening data; training a plurality of machine learning models based on the pre-processed preliminary screening data, and selecting an optimal model from the plurality of machine learning models as a biomarker screening model; performing feature screening on the basis of features of substances in primary screening data output by the biomarker screening model and corresponding feature weight values, and performing secondary screening on the features subjected to feature screening by adopting a preset screening strategy to obtain a target biomarker panel; wherein the preset screening strategy comprises one or more of an accumulated weighted value screening method, a cross recursive feature elimination method, a correlation coefficient screening method and an accumulated feature AUC screening method. The problems existing in the existing biomarker screening process are solved.

A method for determining the degree of a central nervous system (CNS)-specific neurological condition in a subject includes collecting a biological sample of a biofluid from the subject and measuring the amount of a first biomarker, a metabolite of the first biomarker, or mRNA corresponding to the first biomarker from the sample via a dried spot or a microfluidic device. The biofluid is capillary blood or saliva, which offers the advantage of easy collection, making it attractive for field, hospital, and home environments. The method is useful in the diagnosis, care, and management of brain-specific abnormal neurological conditions in general, and specifically for traumatic brain injury (TBI), and (TBI-induced) Alzheimer's disease (AD) and Alexander disease (in which GFAP mutations are involved in white matter deterioration).

Disclosed herein are methods for analzying quantitative expression values of biomarkers of a biomarker panel for determining disease activity in a human subject. Further disclosed herein are kits for measuring quantitative expression values of the markers as well as computer systems and software embodiments of predictive models for determining disease activity in human subjects based on the quantitative expression values of the markers.

The invention provides biomarkers, methods involving their use, and a diagnostic kit that enables the distinction between muscle-invasive bladder cancer (MIBC) and non-muscle- invasive bladder cancer (NMIBC) which represents a critical step in the diagnosis and treatment of bladder cancer. This kit, based on widely used and cost-effective immunohistochemistry (IHC) techniques in pathology practice, comprises an effective biomarker panel for distinguishing between MIBC and NMIBC. This invention is suitable for routine use in clinical pathology practice, enabling better guidance of patient treatment processes and more accurate determination of disease progression.

The present invention provides a fecal biomarker combination for the early diagnosis of Sjogren's syndrome. The method is as follows: collect fecal specimens of the research subjects; collect fecal metabolites; use UPLC / Q-TOFMS technology to collect non-targeted metabolomics data of fecal metabolites to obtain a metabolic map; use XCMS to preprocess the metabolic map data; perform multivariate analysis to screen for differential metabolites and identify them in combination with relevant databases, retain endogenous differential metabolites, and further screen the endogenous differential metabolites using R language and establish a prediction model for verification to determine the biomarker combination for differentiating SS patients from healthy controls. Based on the metabolomics analysis of the feces of SS patients and healthy controls by UPLC / Q-TOFMS, a fecal biomarker combination for the early diagnosis or prevention of Sjogren's syndrome is obtained, which has high sensitivity and specificity, good predictability, and can be used for the early diagnosis of SS.

The present disclosure describes a miniarray that includes a substrate having on one surface thereof an immobilized capture antibody that specifically binds VSIG4 antigen and an assay area of immobilized human double-stranded DNA (to capture anti-dsDNA antibody). Assay areas of the miniarray can further include immobilized capture antibodies that specifically bind ALCAM antigen, TNFRSF1b antigen, OPN antigen, IGFBP2 antigen and VCAM1 antigen. Also disclosed are a cassette for conveniently handling the miniarray and kits and methods for a point of care immunoassay that can distinguish among healthy patients, patients having flaring Lupus Nephritis and patients having inactive Lupus Nephritis.

A diagnostic platform that enables multi-disease diagnostic panels which will help primary care physicians track the health status of patients as well as recognize disease early. The diagnostic platform implements a method of biomarker selection and tiered Artificial Intelligence (A.I.) approach comprising a multi-level machine / deep learning (ML / DL) system which is using multi-panels of biomarkers.

The present invention belongs to the field of biomedicine, and particularly relates to a methylation biomarker combination for diagnosing pancreatobiliary cancer. Specifically, the methylation biomarker includes one or more of SOX17, 3-OST-2, NXPH1, SEPT9, and TERT.