The largest database of trusted experimental protocols
> Physiology > Clinical Attribute > Biological Markers

Biological Markers

Biological Markers are measurable indicators of some biological state or condition.
They are molecullar, biochemical, or genetic markers that can be used to assess risk, diagnose disease, or monitor response to treatment.
PubCompare.ai's AI-powered platform can help researchers locate the best biological marker protocols from literature, preprints, and patents, enabling optimized research and enhanced reproducibility.
With PubCompare.ai's cutting-edge tools, you can elevate your research and discover the most effective biological markers for your studies.

Most cited protocols related to «Biological Markers»

Except as stated otherwise, taxonomic abundances for 16S samples were generated from filtered sequence reads using the RDP classifier [101 (link)], with confidences below 80% rebinned to 'uncertain'. For all the datasets described below, the final input for LEfSe is a matrix of relative abundances obtained from the read counts with per-sample normalization to sum to one. Witten-Bell smoothing [102 ] was used to accommodate rare types, but due to LEfSe's non-parametric approach, this has minimal effect on the discovered biomarkers and on the LDA score. This also allows our biomarker discovery method to avoid most effects of sequence quality issues as long as any sequencing biases are homogeneous among different conditions, as no specific assumptions on the statistical distribution and noise model are made by the algorithm as is standard for non-parametric approaches.
Full text: Click here
Publication 2011
Biological Markers Self Confidence
eQTL p-values, effect sizes, and standard errors were obtained by fitting a linear trend test regression between the expression of each gene and all variants 200 kilobases upstream and downstream from each probe. After filtering out the variants with MAF <0.001, monomorphic SNPs, multi-allelic SNPs (as reported in 1000 Genomes or in the Ensembl database) and variants not sufficiently well imputed (Rsq <0.3, as defined by minimac http://genome.sph.umich.edu/wiki/minimac) between both datasets, we applied our colocalisation procedure. We conducted conditional analysis on SNPs with p-values for the expression associations, and repeated the colocalisation test using expression data conditioned on the most significant SNP. The aim of this analysis is to explore whether additional signals for expression other than the main one are shared with the biomarker signal.
Full text: Click here
Publication 2014
Alleles Biological Markers Genes, vif Genome
All IHC was performed in a hybrid laboratory (Northern Ireland Molecular Pathology Laboratory) that has UK Clinical Pathology Accreditation. Internally validated biomarker conditions, which followed UK-NEQAS guidelines (CD3 and CD8) or were based on expected performance from the literature (p53 and PD-L1), were as follows: CD3 (clone 2GV6 Ventana BenchMark; CC1 32 minutes, Optiview detection), CD8 (clone c8/144B, Dako: Leica Bond III, ER2 20 mins, 1/50, polymer detection), p53 (clone DO-7, Dako, ER2 30 mins, 1/100, polymer detection), PD-L1 (clone SP142, Ventana BenchMark, CC1 24 mins, optiview detection).
Full text: Click here
Publication 2017
Biological Markers CD274 protein, human Clone Cells Hybrids Polymers
Using the NHANES training data, we applied a Cox penalized regression model—where the hazard of aging-related mortality (mortality from diseases of the heart, malignant neoplasms, chronic lower respiratory disease, cerebrovascular disease, Alzheimer’s disease, Diabetes mellitus, nephritis, nephrotic syndrome, and nephrosis) was regressed on forty-two clinical markers and chronological age to select variables for inclusion in our phenotypic age score. Ten-fold cross-validation was employed to select the parameter value, lambda, for the penalized regression. In order to develop a sparse parsimonious age estimator (fewer biomarker variables preferred to produce robust results) we selected a lambda of 0.0192, which represented a one standard deviation increase over the lambda with minimum mean-squared error during cross-validation (Supplement 1: Fig. S13). Of the forty-two biomarkers included in the penalized Cox regression model, this resulted in ten variables (including chronological age) that were selected for the phenotypic age predictor.
These nine biomarkers and chronological age were then included in a parametric proportional hazards model based on the Gompertz distribution. Based on this model, we estimated the 10-year (120 months) mortality risk of the j-the individual. Next, the mortality score was converted into units of years (Supplement 1). The resulting phenotypic age estimate was regressed on DNA methylation data using an elastic net regression analysis. The penalization parameter was chosen to minimize the cross validated mean square error rate (Supplement 1: Fig. S14), which resulted in 513 CpGs.
Full text: Click here
Publication 2018
Biological Markers Cerebrovascular Disorders cytidylyl-3'-5'-guanosine Diabetes Mellitus Dietary Supplements DNA Methylation Heart Diseases Malignant Neoplasms Nephritis Nephrotic Syndrome Phenotype Respiration Disorders
A Pubmed search was performed to identify lung cancer survival associated biomarkers using all combinations of the keywords “lung cancer”, “NSCLC”, “adenocarcinoma”, “squamous cell carcinoma”, “survival”, “gene expression”, “signature” and “meta analysis”. Only studies published in English were included. Eligibility criteria also included the investigation of the biomarker in at least 50 patients - biomarkers described in experimental models only were omitted. For each gene/signature the exact conditions in which it was identified have been retrieved, and these have been used as filtering when selecting the patients for the survival analysis.
To visualize the performance of the various biomarkers in datasets including different number of patients, we have generated funnel plots depicting the hazard ratio (and confidence intervals) on the horizontal axis vs. the sample size on the vertical axis for each dataset. We also added an option to the online interface to simultaneously perform the analysis in each of the individual datasets. Finally, significance was set at p<0.01.
Full text: Click here
Publication 2013
Adenocarcinoma Biological Markers Eligibility Determination Epistropheus Gene Expression Genes Lung Lung Cancer Non-Small Cell Lung Carcinoma Patients Squamous Cell Carcinoma Tumor Markers

Most recents protocols related to «Biological Markers»

Example 12

As a proof of concept, the patient population of this study is patients that (1) have moderate to severe ulcerative colitis, regardless of extent, and (2) have had an insufficient response to a previous treatment, e.g., a conventional therapy (e.g., 5-ASA, corticosteroid, and/or immunosuppressant) or a FDA-approved treatment. In this placebo-controlled eight-week study, patients are randomized. All patient undergo a colonoscopy at the start of the study (baseline) and at week 8. Patients enrolled in the study are assessed for clinical status of disease by stool frequency, rectal bleeding, abdominal pain, physician's global assessment, and biomarker levels such as fecal calprotectin and hsCRP. The primary endpoint is a shift in endoscopy scores from Baseline to Week 8. Secondary and exploratory endpoints include safety and tolerability, change in rectal bleeding score, change in abdominal pain score, change in stool frequency, change in partial Mayo score, change in Mayo score, proportion of subjects achieving endoscopy remission, proportion of subjects achieving clinical remission, change in histology score, change in biomarkers of disease such as fecal calprotectin and hsCRP, level of adalimumab in the blood/tissue/stool, change in cytokine levels (e.g., TNFα, IL-6) in the blood and tissue.

FIG. 72 describes an exemplary process of what would occur in clinical practice, and when, where, and how the ingestible device will be used. Briefly, a patient displays symptoms of ulcerative colitis, including but not limited to: diarrhea, bloody stool, abdominal pain, high c-reactive protein (CRP), and/or high fecal calprotectin. A patient may or may not have undergone a colonoscopy with diagnosis of ulcerative colitis at this time. The patient's primary care physician refers the patient. The patient undergoes a colonoscopy with a biopsy, CT scan, and/or MRI. Based on this testing, the patient is diagnosed with ulcerative colitis. Most patients are diagnosed with ulcerative colitis by colonoscopy with biopsy. The severity based on clinical symptoms and endoscopic appearance, and the extent, based on the area of involvement on colonoscopy with or without CT/MRI is documented. Treatment is determined based on diagnosis, severity and extent.

For example, treatment for a patient that is diagnosed with ulcerative colitis is an ingestible device programmed to release a single bolus of a therapeutic agent, e.g., 40 mg adalimumab, in the cecum or proximal to the cecum. Prior to administration of the treatment, the patient is fasted overnight and is allowed to drink clear fluids. Four hours after swallowing the ingestible device, the patient can resume a normal diet. An ingestible device is swallowed at the same time each day. The ingestible device is not recovered.

In some embodiments, there may be two different ingestible devices: one including an induction dose (first 8 to 12 weeks) and a different ingestible device including a different dose or a different dosing interval.

In some examples, the ingestible device can include a mapping tool, which can be used after 8 to 12 weeks of induction therapy, to assess the response status (e.g., based on one or more of the following: drug level, drug antibody level, biomarker level, and mucosal healing status). Depending on the response status determined by the mapping tool, a subject may continue to receive an induction regimen or maintenance regimen of adalimumab.

In different clinical studies, the patients may be diagnosed with Crohn's disease and the ingestible devices (including adalimumab) can be programmed to release adalimumab in the cecum, or in both the cecum and transverse colon.

In different clinical studies, the patients may be diagnosed with illeocolonic Crohn's disease and the ingestible devices (including adalimumab) can be programmed to release adalimumab in the late jejunum or in the jejunum and transverse colon.

Full text: Click here
Patent 2024
Abdominal Pain Adalimumab Adrenal Cortex Hormones Biological Markers Biopsy BLOOD Cecum Colonoscopy C Reactive Protein Crohn Disease Cytokine Diarrhea Diet Endoscopy Endoscopy, Gastrointestinal Feces Homo sapiens Immunoglobulins Immunosuppressive Agents Jejunum Leukocyte L1 Antigen Complex Medical Devices Mesalamine Mucous Membrane Neoadjuvant Therapy Patient Care Management Patients Pharmaceutical Preparations Placebos Primary Care Physicians Safety Therapeutics Tissues Transverse Colon Treatment Protocols Tumor Necrosis Factor-alpha Ulcerative Colitis X-Ray Computed Tomography
Not available on PMC !

Example 22

Clinicians can use several biochemical measurements to objectively assess patients' current or past alcohol use. Several more experimental markers hold promise for measuring acute alcohol consumption and relapse. These include certain alcohol byproducts, such as acetaldehyde, ethyl glucuronide (EtG), and fatty acid ethyl esters (FAEE), as well as two measures of sialic acid, a carbohydrate that appears to be altered in alcoholics (Peterson K, Alcohol Research and Health, 2005). Clinicians have had access to a group of biomarkers that indicate a person's alcohol intake. Several of these reflect the activity of certain liver enzymes: serum gamma-glutamyltransferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and carbohydrate-deficient transferrin (CDT), a protein that has received much attention in recent years. Another marker, N-acetyl-β-hexosaminidase (beta-Hex), indicates that liver cells, as well as other cells, have been breaking down carbohydrates, which are found in great numbers in alcohol (Javors and Johnson 2003).

In some embodiments the disclosed device focuses on detecting markers associated with alcohol abuse from menstrual blood or cervicovaginal fluid.

Full text: Click here
Patent 2024
Abuse, Alcohol Acetaldehyde Alcoholics Aspartate Transaminase Attention beta-N-Acetylhexosaminidase Biological Markers BLOOD carbohydrate-deficient transferrin Carbohydrates Cells D-Alanine Transaminase enzyme activity Esters Ethanol ethyl glucuronide Fatty Acids gamma-Glutamyl Transpeptidase Hepatocyte Liver Medical Devices Menstruation N-Acetylneuraminic Acid Patients Relapse Serum Staphylococcal Protein A
Not available on PMC !

Example 8

Serum samples from patients were tested with the FLNA IPMRM, as described above, using the anti-FLNA monoclonal antibodies of the invention. The results were combined with data on age, PSA, and Gleason score and subjected to regression modelling. As shown in FIG. 10, a Prostate Cancer Biomarker Panel consisting of biomarkers FLNA, FLNB, age and PSA improved the classification of prediction of prostate cancer over PSA alone (AUC=0.64, [0.59, 0.69], vs 0.58).

Samples of patient serum were also analyzed for the biomarkers FLNA, keratin 19 (KRT19) and age combined, versus PSA alone. FIG. 11 shows that the biomarkers FLNA, KRT19 and age have improved classification of prediction between patients with benign prostatic hyperplasia versus prostate cancer over PSA alone (AUC=0.70 [0.60, 0.80], vs 0.58).

Full text: Click here
Patent 2024
Anti-Antibodies Benign Prostatic Hyperplasia Biological Markers Keratin-19 Monoclonal Antibodies Patients Prostate Prostate Cancer Serum Tumor Markers

Example 17

Since interferon signaling is spontaneously activated in a subset of cancer cells and exposes potential therapeutic vulnerabilities, it was tested whether there is evidence for similar endogenous interferon activation in primary human tumors. An IFN-GES threshold was computed to predict ADAR dependency across the CCLE cell lines and was determined to be a z-score above 2.26 (FIG. 66, panel A). This threshold was applied to The Cancer Genome Atlas (TCGA) tumors, to identify primary cancers with similarly high interferon activation. Restricting the analysis to the 4,072 samples analyzed by TCGA with at least 70% tumor purity as estimated by the ABSOLUTE algorithm (Carter et al. (2012) Nat. Biotechnol. 30:413-421), 2.7% of TCGA tumors displayed IFN-GESs above this threshold (FIG. 66, panel B and. GSEA of amplified genes in these high purity, high interferon tumors revealed the top pathway as “Type I Interferon Receptor Binding”, comprising 17 genes that all encode type I interferons and are clustered on chromosome 9p21.3 (FIG. 67).

Furthermore, analysis of TCGA copy number data showed that the interferon gene cluster including IFN-β (IFNβI), IFN-ε (IFNE), IFN-ω (IFNWI), and all 13 subtypes of IFN-α on chromosome 9p21.3, proximal to the CDKN2A/CDKN2B tumor suppressor locus, is one of the most frequently homozygously deleted regions in the cancer genome. The interferon genes comprise 16 of the 26 most frequently deleted coding genes across 9,853 TCGA cancer specimens for which ABSOLUTE copy number data are available (FIG. 66, panels C and D). Interferon signaling and activation, both in tumors with high IFN-GESs or deletions in chromosome 9p, therefore represent a biomarker to stratify patients who benefit from interferon modulating therapies.

In summary, specific cancer cell lines have been identified with elevated IFN-β signaling triggered by an activated cytosolic DNA sensing pathway, conferring dependence on the RNA editing enzyme, ADAR1. In cells with low, basal interferon signaling, the cGAS-STING pathway is inactive and PKR levels are reduced (FIG. 68, panel A). Upon cGAS-STING activation, interferon signaling and PKR protein levels are elevated but ADAR1 is still able to suppress PKR activation (FIG. 68, panel B). However, once ADAR1 is deleted, the abundant PKR becomes activated and leads to downstream signaling and cell death (FIG. 68, panel C). This is also shown in normal cells lines (e.g. A549 and NCI-H1437) once exogenous interferon is introduced (FIG. 68, panel D). ADAR1 deficiency in cell lines with high interferon levels, whether from endogenous or exogenous sources, led to phosphorylation and activation of PKR, ATF4-mediated gene expression, and apoptosis. Recent studies have shown that cGAS activation and innate interferon signaling, induced by cytosolic DNA released from the nucleus by DNA damage and genome instability (Mackenzie et al. (2017) Nature 548:461-465; Harding et al. (2017) Nature 548:466-470), led to elevated interferon-related gene expression signatures, which have been linked to resistance to DNA damage, chemotherapy, and radiation in cancer cells (Weichselbaum et al. (2008) Proc. Natl. Acad. Sci. USA 105:18490-18495). In high-interferon tumors, blocking ADAR1 might be effective to induce PKR-mediated apoptotic pathways while upregulating type I interferon signaling, which could contribute to anti-tumor immune responses (Parker et al. (2016) Nature 16:131-144). Alternatively, in tumors without activated interferon signaling, ADAR1 inhibition can be combined with localized interferon inducers, such as STING agonists, chemotherapy, or radiation. Generation of specific small molecule inhibitors targeting ADAR1 exploits this novel vulnerability in lung and other cancers and serves to enhance innate immunity in combination with immune checkpoint inhibitors.

Full text: Click here
Patent 2024
agonists Apoptosis ATF4 protein, human Biological Markers CDKN2A Gene Cell Death Cell Lines Cell Nucleus Cells Chromogranin A Chromosome Deletion Chromosomes, Human, Pair 3 Cytosol DNA Damage Electromagnetic Radiation Enzymes Gene, Cancer Gene Clusters Gene Expression Genes Genome Genomic Instability Homo sapiens IFNAR2 protein, human Immune Checkpoint Inhibitors Immunity, Innate inhibitors Interferon-alpha Interferon Inducers interferon omega 1 Interferons Interferon Type I Lung Malignant Neoplasms Neoplasms Oncogenes Patients Pharmacotherapy Phosphorylation Proteins Psychological Inhibition Response, Immune Tumor Suppressor Genes

Example 16

Blood samples were taken from mice with CAKI-1 RCC tumors, 44 days after CAR T administration. Briefly, 100 ul of mouse whole blood was collected via submandibular vein. Red blood cell lysis buffer was used to achieve optimal lysis of erythrocytes with minimal effect on lymphocytes. Human CD45 and mouse CD45 were used as a biomarker to separate human and mouse cells by FACS. The blood samples were evaluated by flow cytometry looking for absolute CAR T counts as well as memory T cell subsets. An anti-CD70 CAR anti-idiotype antibody was used to detect CAR T cells and CD45RO+CD27+ to define central memory T cells. See U.S. Patent Application No. 63/069,889, the relevant disclosures of which are incorporated by reference for the subject matter and purpose referenced herein.

The results demonstrate that the addition of the TGFBRII and Regnase-1 gene edit significantly enhanced the population of central memory T cells compared to the edit of either TFGBRII or Regnase-1 alone, which correlates with massive expansion of CAR T cells (FIG. 19A) seen in these animals. And the TGFBRII edit further promoted the potential of CAR T cell proliferation in vivo, suggesting a robust synergistic effect along with the Regnase edit (FIG. 19B).

Full text: Click here
Patent 2024
Animals Antibodies, Anti-Idiotypic Biological Markers BLOOD Blood Vessel Tumors Buffers CD45RO Antigens Cells Central Memory T Cells Erythrocytes Flow Cytometry Genes Homo sapiens Lymphocyte Memory Mus Neoplasms Renal Cell Carcinoma T-Lymphocyte T-Lymphocyte Subsets Veins Vision Xenografting

Top products related to «Biological Markers»

Sourced in United States, Austria, Canada, Belgium, United Kingdom, Germany, China, Japan, Poland, Israel, Switzerland, New Zealand, Australia, Spain, Sweden
Prism 8 is a data analysis and graphing software developed by GraphPad. It is designed for researchers to visualize, analyze, and present scientific data.
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
Sourced in United States, Japan, United Kingdom, Austria, Canada, Germany, Poland, Belgium, Lao People's Democratic Republic, China, Switzerland, Sweden, Finland, Spain, France
GraphPad Prism 7 is a data analysis and graphing software. It provides tools for data organization, curve fitting, statistical analysis, and visualization. Prism 7 supports a variety of data types and file formats, enabling users to create high-quality scientific graphs and publications.
Sourced in United States, Germany, United Kingdom, Israel, Canada, Austria, Belgium, Poland, Lao People's Democratic Republic, Japan, China, France, Brazil, New Zealand, Switzerland, Sweden, Australia
GraphPad Prism 5 is a data analysis and graphing software. It provides tools for data organization, statistical analysis, and visual representation of results.
Sourced in United States, United Kingdom, Canada, China, Germany, Japan, Belgium, Israel, Lao People's Democratic Republic, Italy, France, Austria, Sweden, Switzerland, Ireland, Finland
Prism 6 is a data analysis and graphing software developed by GraphPad. It provides tools for curve fitting, statistical analysis, and data visualization.
Sourced in United States, China, Germany, United Kingdom, Canada, Switzerland, Sweden, Japan, Australia, France, India, Hong Kong, Spain, Cameroon, Austria, Denmark, Italy, Singapore, Brazil, Finland, Norway, Netherlands, Belgium, Israel
The HiSeq 2500 is a high-throughput DNA sequencing system designed for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data with speed and accuracy.
Sourced in United States, Germany, Canada, China, France, United Kingdom, Japan, Netherlands, Italy, Spain, Australia, Belgium, Denmark, Switzerland, Singapore, Sweden, Ireland, Lithuania, Austria, Poland, Morocco, Hong Kong, India
The Agilent 2100 Bioanalyzer is a lab instrument that provides automated analysis of DNA, RNA, and protein samples. It uses microfluidic technology to separate and detect these biomolecules with high sensitivity and resolution.

More about "Biological Markers"

Biological markers, also known as biomarkers, are measurable indicators of some biological state or condition.
These markers can be molecular, biochemical, or genetic in nature and are widely used in research, diagnostics, and clinical practice.
Biomarkers play a crucial role in assessing risk, diagnosing diseases, and monitoring response to treatment.
They provide valuable insights into the underlying biological processes and can help researchers and clinicians make more informed decisions.
The analysis of biomarkers often involves the use of advanced analytical techniques and software tools.
For example, researchers may utilize platforms like Prism 8, SAS version 9.4, or GraphPad Prism (versions 5, 6, and 7) to visualize and analyze biomarker data.
Additionally, technologies such as the Agilent 2100 Bioanalyzer and the HiSeq 2500 sequencing platform can be employed to measure and quantify specific biomarkers.
One important class of biomarkers is nucleic acid-based markers, which can be detected and quantified using techniques like RT-PCR and RNA sequencing.
The TRIzol reagent is a commonly used tool for extracting and purifying RNA from biological samples, enabling the measurement of these genetic biomarkers.
PubCompare.ai's AI-powered platform can help researchers and clinicians navigate the vast landscape of biomarker literature, preprints, and patents.
The platform's cutting-edge tools allow users to locate the most effective and optimized protocols for measuring and analyzing biological markers, enhancing research reproducibility and accelerating scientific discoveries.