53 results about "Biomarker panel" patented technology

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.

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 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.

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.

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.

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.

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 pertains to a new method for the diagnosis, prognosis, stratification and / or monitoring of a therapy, of a pulmonary dysfunction in a subject. The method is based on the determination of the level of a panel of biomarkers selected from TM4SF18, TRGV9, ADORA2A, H963, IRF2, TNFSF14, TRIB1, SMAD3, TSLP, SLAMF8, THBS1, SOD1, HAS2, TLR2, THBD, TRA, TFEC, SPHK1, COL3A1, Elastin, IL6R, SN, TEK, SOX7, CXCL9, CXCL10, SLC38A6, SLC15A3, TGFA, SLPI, VWF, TLR8, TGFB1, CXCR6, CCL24, PD1, PLA1A, TRD, CTLA4, MMP9, CD301, PDL1, PDL2, TGFB1, CXCR6, CCL24, PD1, PLA1A, TRD, CTLA4, MMP9, and CD301. The new biomarker panels of this invention allow diagnosing and even stratifying various pulmonary dysfunctions in a subject, and even allow early detection of Chronic lung allograft dysfunction (CLAD). Furthermore provided are diagnostic kits for performing the methods of the invention.

The present invention generally relates to the field of cancer, in particular to cancers having microsatellite instability (MSI) and / or mismatch repair (MMR-) deficiency. Examples of such cancers include many colorectal, gastric, and endometrial tumors. Accordingly, the present invention provides a novel diagnostic marker panel for analyzing MSI loci, together with methods and kits of using said panel in the detection of cancers having microsatellite instability (MSI) and / or mismatch repair (MMR-) deficiency.

Provided herein are methods, devices, and kits for identifying glycosylated polypeptide biomarkers and signatures for progression of a disease or a condition, such as cancer or NASH, or and response of the disease or condition to a treatment. Also provided herein are: i) methods of generating and analyzing glycosylated polypeptide biomarkers, ii) methods of validating a model using glycosylated polypeptides for predicting the disease or condition or for making treatment recommendation, iii) systems and methods for implementing QC of a cohort of samples by analyzing peptide structure data for each sample using a machine learning model to generate a predicted age and / or sex associated for each sample. The quality control issue may include an error of mislabeled samples or an error from sample preparation, or a systemic measurement or an instrument error.

An assay system and method for generating quantitative data for a subject and a method for treating prostate cancer in a subject by determining an expression level of a biomarker panel in a sample obtained from the subject, the biomarker panel including the genes CDC45, CENPI, CLSPN, ERCC6L, EXO1, NCAPG, BUB1B, CDK1, NUSAP1, RAD51, and RRM2 and optionally E2F7 and / or GSG2, wherein the expression level is obtained by measuring expression of the biomarker panel in the sample, and wherein the subject has a cancer, or is suspected of having a cancer. The cancer may be, for example, prostate cancer, brain cancer, lung cancer, breast cancer, bladder cancer, or ovarian cancer.

Disclosed are methods of characterizing, diagnosing, monitoring, and / or treating an individual with Crohn's Disease (CD) comprising detecting, in a biological sample obtained from the individual, a plurality of biomarkers. The biomarkers may be used for one or more of predicting remission of CD in an individual, determining longitudinal assessment of response to a biologic therapy, predicting or determining response status of the individual to an anti-tumor necrosis factor (TNF) therapy. Further disclosed are systems and compositions for use with the disclosed methods.

A urine exosome marker panel for detecting bladder cancer and the use thereof. The provided biomarker panel comprising IGF2, KRT20, SPAG5, NRP1 and DHRS2 is used for detecting bladder cancer patients. The marker panel is used to construct a prediction model, and the model is trained by means of a training set and optimized by means of a test verification set, so as to finally obtain an early diagnosis prediction model for bladder cancer. The provided exosome marker panel and prediction model enable relatively accurate early screening of clinical samples from patients clinically suspected of having bladder cancer, presenting with symptoms such as hematuria, frequent urination, urgent urination, and dysuria, and for whom cystoscopy is recommended by clinical diagnosis, wherein the optimal marker panel is IGF2+KRT20+SPAG5+NRP1, with an AUC of 0.902, a sensitivity of 86.8%, a specificity of 83.0%, and an accuracy of 84.8%, demonstrating favorable clinical diagnostic value.

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 unexepected 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 dislosed 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.

A biomarker panel for a serum test for detecting lung cancer, wherein the biomarker is selected from the group consisting of arginine, C18.2, decadienoylcarnitine (C10:2), LYSOC18.2, methionine, ornithine, PC32:2AA, PC36.0AA, PC36.0AE, putrescine, spermidine, spermine, and valine. Serum testing for diagnosing lung cancer may take into account smoking history.

Disclosed are systems and methods for automated chromatographic peak detection and refinement in high-throughput LC-MS / MS datasets, applicable to both proteomics and metabolomics. The invention integrates signal smoothing, apex detection, boundary assignment, and machine learning-based quality scoring into a fully automated pipeline. The system supports multiple acquisition modes (e.g., DIA-PASEF, Orbitrap), chromatographic strategies (C18, C30, HILIC), and biological matrices. Detected peaks are refined using second derivative and percentile-based baseline logic and scored by an XGBoost classifier trained on curated datasets. Quantification-ready outputs are suitable for biomarker panel development, quality control, and diagnostic assay construction. The invention substantially reduces manual curation time while improving reproducibility across samples and platforms.

The application discloses a combined biomarker combination and evaluation model for evaluating chronic low-grade inflammation of polycystic ovary syndrome, and belongs to the technical field of biomarkers. The combined biomarker combination comprises inflammation-related proteins and metabolites; the inflammation-related proteins comprise FURIN, CCL3, CCL8, CD38, NOS2, NOS3, IL18R1 and HGF; and the metabolites comprise lithocholic acid-3-O-glucuronide and taurine. The application provides a combined biomarker combination for diagnosing and evaluating the chronic inflammation state of polycystic ovary syndrome and an application method thereof. By jointly detecting specific inflammatory proteins and metabolites, the systemic chronic inflammation load of PCOS patients is quantitatively evaluated, and the combined biomarker combination has the beneficial effects of high stability, accurate discrimination, wide applicability and easy clinical transformation, and solves the problem that there is no objective evaluation method for the chronic inflammation state of PCOS.

Biomarker panels for the prediction of patient response to immunotherapy, and methods of use thereof.