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Interferons

Interferons are a group of cytokines with important roles in the immune system and cellular signaling.
They exhibit antiviral, antiproliferative, and immunomodulatory properties, making them a crucial factor in the body's defense against infections and diseases.
PubCompare.ai's AI-driven protocol comparisons can optimize Interferons research by easily locating and identifying the best protocols from literature, preprints, and patents with enhanced reproducibility and accuracy.
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Most cited protocols related to «Interferons»

1
Hepatitis C Antiviral Long-Term Trial against Cirrhosis
Cited 44 times
The design of the Hepatitis C Antiviral Long-Term Treatment against Cirrhosis (HALT-C) trial has been described previously.12 (link) Briefly, patients meeting the following criteria were entered into this study from 10 study centers in the United States between August 2000 and August 2004: lack of a sustained virologic response to previous therapy, advanced hepatic fibrosis according to liver biopsy (an Ishak fibrosis score13 (link) of 3 or more; scores range from 0 to 6, with higher scores indicating greater degrees of fibrosis and scores of 5 or 6 indicating cirrhosis), no history of hepatic decompensation or hepatocellular carcinoma, and absence of exclusion criteria (e.g., liver disease other than hepatitis C, uncontrolled medical or psychiatric conditions, or contraindications to interferon treatment). The patients were stratified according to their Ishak fibrosis score. The non-cirrhotic-fibrosis stratum consisted of 622 patients with a score of 3 or 4, and the cirrhosis stratum consisted of 428 patients with a score of 5 or 6. The patients provided written informed consent for participation in the trial.
During the lead-in phase of the trial, all patients underwent treatment with 180 μg of subcutaneous pegylated interferon alfa-2a weekly (Pegasys, Roche; the drug had not yet been approved by the Food and Drug Administration [FDA] when the trial began) and oral ribavirin (1000 to 1200 mg daily, according to body weight) for at least 24 weeks before undergoing randomization (Fig. 1). Randomization was stratified according to clinical center and the presence or absence of cirrhosis and was performed centrally by computer with the use of permuted blocks of random size. Patients with detectable serum HCV RNA levels at treatment week 20 were classified as having no response (<1 log10 IU per milliliter decrease in HCV RNA level from baseline) or a partial response (≥1 log10 IU per milliliter decrease in HCV RNA level from baseline) and were assigned for the next 3.5 years to either the maintenance-therapy group (90 μg of peginterferon alfa-2a weekly, without ribavirin) or the untreated control group. For treated patients who had unacceptable side effects, the weekly peginterferon dose was reduced to 45 μg or even lower, as needed.
Patients with undetectable serum HCV RNA at week 20 continued therapy for an additional 48 weeks, as reported previously.14 (link) If HCV RNA was detected in a patient again after week 20, either during treatment (breakthrough) or after cessation of treatment (relapse), the patient was offered the opportunity to undergo randomization in the controlled phase of the trial (the “breakthrough or relapse” cohort). During the trial, after pegylated interferons became available for treating hepatitis C, we amended the protocol to allow patients who had been treated with peginterferon plus ribavirin outside the study but had not had a sustained virologic response to treatment to undergo randomization to the treatment or control group (the “express” cohort).
Di Bisceglie A.M., Shiffman M.L., Everson G.T., Lindsay K.L., Everhart J.E., Wright E.C., Lee W.M., Lok A.S., Bonkovsky H.L., Morgan T.R., Ghany M.G., Morishima C., Snow K.K, & Dienstag J.L. (2008). Prolonged Therapy of Advanced Chronic Hepatitis C with Low-Dose Peginterferon. The New England journal of medicine, 359(23), 2429-2441.
Publication 2008
Antiviral Agents Biopsy Body Weight Fibrosis Fibrosis, Liver Hepatitis C Hepatocellular Carcinomas Interferons Liver Liver Cirrhosis Liver Diseases Long-Term Care Mental Disorders Patients Pegasys peginterferon alfa-2a Pharmaceutical Preparations Relapse Ribavirin Serum Withholding Treatment
2
Evaluating Breast Cancer Subtype Biomarkers
Cited 28 times
To determine whether any of the modules we identified were related to clinical breast cancer biomarkers, we calculated the overlap between module genes and the PAM50 intrinsic subtype gene set [1] (link), [32] (link), the NKI70 MammaPrint® gene set [33] (link), and the 21 genes used in OncotypeDX® [34] (link). Since different gene sets can be used to derive an identical classification schema, we also fit univariate logistic regression models relating intrinsic subtype assignments to module scores in GSE1456, GSE21653, and METABRIC, and then performed ROC analysis on these models to calculate an AUC estimate of how well each individual module is able to predict each subtype. For comparison of modules to other previously published signatures, pretreatment biopsies in GSE21653, GSE1456, and GSE2034 were scored for expression of the STAT1 immune cluster [19] (link), the IR-7 immune signature [20] (link), the IFN interferon cluster [21] , the proliferation signature MS-14 [37] , and for subsets of T cell and B cell surface markers [22] (link) by calculating the mean expression levels of signature genes weighted by +1 or −1 according to direction of association with RFS as previously described [31] (link); ECM1-4 cluster scores were calculated as the Pearson correlations between expression of the genes in the published ECM signature and the four ECM centroids, respectively [36] (link). Pearson correlation coefficients (r) between the module and signature scores were calculated to assess relatedness.
Wolf D.M., Lenburg M.E., Yau C., Boudreau A, & van ‘t Veer L.J. (2014). Gene Co-Expression Modules as Clinically Relevant Hallmarks of Breast Cancer Diversity. PLoS ONE, 9(2), e88309.
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Publication 2014
B-Lymphocytes Biopsy Breast Carcinoma Gene Expression Gene Modules Genes Interferons STAT1 protein, human T-Lymphocyte Subsets
3
Investigating OASL2 in Antiviral Immunity
Cited 20 times

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2014
Actins Alexa594 Anti-Antibodies Antibiotics Antibodies Biopharmaceuticals Bone Marrow Cells C'-protein, Sendai virus DDX58 protein, human Embryo Encephalomyocarditis virus Enzyme-Linked Immunosorbent Assay Fluorescein-5-isothiocyanate FuGene HCT116 Cells HEK293 Cells Human Herpesvirus 1 Immunoglobulins Institutional Animal Care and Use Committees Interferons Macrophage Colony-Stimulating Factor McCoy's 5A medium Mice, Knockout Mus Penicillins prisma Rabbits Rivers Sendai virus Short Hairpin RNA Strains Streptomycin Student
4
Statistical Methods for Cytokine Data Analysis
Cited 18 times
We conducted a systematic literature search in the database MEDLINE (1980–2005) to review statistical methods that have been previously applied to cytokine data. Because the objective was to get a crude overview rather than to reveal the exact number of papers published in this area we defined quite sensitive search criteria using the following key words: "cytokine$" or terms to identify specific cytokines (e.g. among others "IL$," "interleukin$," IF$, interferon$, TNF$, etc.) and common univariate and multivariate statistical techniques (e.g. among others "linear regression,""analysis of variance,""cluster analysis,""factor analysis" etc.).
Table 1 shows the results of our search. The most widely used methods found were simple statistical approaches that investigate the relationship between two variables (so called bivariate methods – also called univariate methods when variables are classified as dependent and independent variables). We frequently found standard methods to compare means of immunological parameters between independent groups (e.g. t-test, analysis of variance or their non-parametric equivalents), bivariate correlation analysis (Pearson's or Spearman's correlation coefficients) and univariate linear regression. By contrast, multivariate techniques ( i.e. statistical approaches that consider three or more study variables simultaneously) were less frequently applied to cytokine data. Several studies used factor analysis (to identify groups of correlated immunological parameters) or cluster analysis (to identify groups of individuals with similar immunological profiles) or discrimination techniques such as logistic regression, discriminant analysis (to identify causes or consequences of immunological profiles). We also found a few examples of advanced modelling techniques (path analysis/structural equation modelling) that simultaneously model multiple relationships between the study variables.
In the following section we provide an overview of statistical methods that can be considered for analysing immunological data that should help the applied immunologists without a detailed knowledge of statistics to select the appropriate statistical technique for each particular research question. The definition of which method is the most appropriate is strongly dependent on the research objective, the type of data collected, whether data assumptions are fulfilled and whether the sample size is sufficient. We begin with a short introduction to these topics.
Genser B., Cooper P.J., Yazdanbakhsh M., Barreto M.L, & Rodrigues L.C. (2007). A guide to modern statistical analysis of immunological data. BMC Immunology, 8, 27.
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Publication 2007
Cytokine Discrimination, Psychology Interferons Interleukins
5
Immune Cell Infiltration Profiling in Cancer
Cited 17 times
In brief, the infiltration levels of immune cell types were quantified by ssGSEA in R package gsva. The ssGSEA applies gene signatures expressed by immune cell populations (20 (link)) to individual cancer samples (21 (link)). The deconvolution approach used in our study included 27 immune cells that are involved in innate immunity [natural killer (NK) cells, CD56dim NK cells, CD56bright NK cells, plasmacytoid dendritic cells (DCs), immature DCs, activated DCs, neutrophils, monocytes, mast cells, eosinophils, and macrophages] and in adaptive immunity (immature B cells, activated B cells, central memory CD4+ T, effector memory CD4+ T-, activated CD4+ T, central memory CD8+ T, effector memory CD8+ T-, activated CD8+ T-, NK T-, T follicular helper, Tγδ, Th1, Th2, Th17, and Treg). The observation of T-cell infiltration score (TIS) was defined as the average of the standardized values for CD8+ T, central memory CD4+ T, effector memory CD4+ T-, central memory CD8+ T, effector memory CD8+ T-, Th1, Th2, Th17, and Treg cells. The obtained CYT score rule from the data set of Rooney et al. (22 (link)) consisted of cytolytic genes (calculated as geometrical mean of PRF1 and GZMA). The CD8+ T/Treg ratio was the digital ratio of ssGSEA scores for these two cell types. Signaling pathway was evaluated based on ssGSEA (23 (link)) according to previously report (24 (link)). Gene set for “41BB signaling pathway” and “Interferon-a response” was retrieved from MSigDB (25 (link)).
Zhang L., Zhao Y., Dai Y., Cheng J.N., Gong Z., Feng Y., Sun C., Jia Q, & Zhu B. (2018). Immune Landscape of Colorectal Cancer Tumor Microenvironment from Different Primary Tumor Location. Frontiers in Immunology, 9, 1578.
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Publication 2018
Adaptive Immunity B-Lymphocytes Cells Dendritic Cells Digit Ratios Eosinophil Genes GZMA protein, human Immature B-Lymphocyte Immunity, Innate Interferons Macrophage Malignant Neoplasms Mast Cell Memory Monocytes Natural Killer Cells Neutrophil Plasmacytoid Dendritic Cells Population Group PRF1 protein, human Regulatory T-Lymphocytes Signal Transduction Pathways T-Lymphocyte Th17 Cells

Most recents protocols related to «Interferons»

1
Interferon Signaling in ADAR1-Dependent Cancer Cells
Not available on PMC !

Example 9

An analysis of gene ontology (GO) categories associated with ADAR1 dependent cells revealed that NCI-H1650 and HCC366 (“HCC-366”), two ADAR1 dependent cell lines, both have elevated basal expression of interferon inducible genes (FIG. 35). The expression levels of interferon-inducible genes were also elevated in NCI-H196 cells (FIG. 36).

In light of the correlation between ADAR1 dependency and the expression of interferon-inducible genes, additional cancer cell lines from the Molecular Signatures Database (MSigDB) (Liberzon et al. (2015) Cell Systems 1:417-425) was examined. Cancer Cell Line Encyclopedia (CCLE) clustering was performed based on the Type I/Interferon-a gene set, which contained 97 genes including PKR. The resulting cluster included HCC366, NCI-H1650 and 9 additional lung cell lines. Among these cell lines, HCC1438 and NCI-H596 were sensitive to knockout of ADAR1 by lentiviral CRISPR-Cas9 (FIG. 37).

All the above-identified ADAR1 dependent cancer cell lines showed elevated interferon signaling markers, e.g., phosphorylation of STAT1 and expression of interferon-stimulated gene (ISGs) (FIG. 38). Elevated interferon signaling in the ADAR1 dependent cancer cell lines did not necessarily lead to PD-L1 overexpression (FIG. 38). Cell lines in the high interferon signaling cluster (LN215_CENTRAL_NERVOUS_SYSTEM, NCIH596_LUNG, HCC1438_LUNG, T3M10_LUNG, NCIH1869_LUNG, SW900_LUNG, HCC366_LUNG, SKLU1_LUNG, NCIH1650_LUNG, HCC4006_LUNG, and NCIH1648_LUNG) displayed high IFN-β, but not IFN-α (FIG. 39). As such, cancer cell lines sensitive to ADAR1 or ISG15 knockdown displayed elevated interferon secretion and downstream signaling. To further investigate the relationship between ADAR1 and IFN-β secretion, it was found that ADAR1 knockout led to amplified IFN-β secretion in cell lines primed with high basal interferon activation (FIG. 40). It was also found that ADAR1 dependent cell lines do not show enhanced sensitivity to IFN-α or IFN-β alone (FIG. 41).

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
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Patent 2024
CD274 protein, human Cell Lines Cells Central Nervous System Clustered Regularly Interspaced Short Palindromic Repeats Gene Expression Genes Hypersensitivity Interferon-alpha Interferons Interferon Type I Light Lung Malignant Neoplasms Phosphorylation secretion STAT1 protein, human
2
Xenograft Mouse Model for ADAR1 and ISG15
Not available on PMC !

Example 18

To study ADAR1 and ISG15 dependence in vivo xenograft mouse models are generated using the cell lines that have been studied in vitro. To ensure that tumors are established, inducible Cas9 and guide RNA expression vectors are used to infect the cell lines before implantation. Once the tumors for each cell line are formed, Cas9 expression is induced, which deletes each gene of interest. As ADAR1 knockout in these cell lines greatly reduces viability in vitro, similar effects to occur in the xenograft tumors are expected, with ADAR1 knockout reducing tumor progression in cell lines with interferon gene expression signatures after Cas9 induction. Alternatively, nanoparticles containing siRNAs targeting each gene can be used after these cell lines form tumors in the mice. These experiments further the understanding of the dependence on interferon-regulating genes in specific cell lines in vivo.

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
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Patent 2024
Cell Line, Tumor Cell Lines Cloning Vectors Disease Progression Genes Heterografts Interferons Mus Neoplasms Ovum Implantation RNA, Small Interfering Transcription, Genetic
3
Targeting ADAR1 in High Interferon Tumors
Not available on PMC !

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.

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
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
4
RIG-I Activation by Ligand Complexation
Not available on PMC !

Example 12

Materials and Methods

RIG-I reporter cells (HEK-Lucia RIG-I, Invivogen) were seeded at 50,000 cells per well and treated with RIG-I ligands (1 ug) or ligands complexed to 3E10-D31N (20 ug). This assay uses a cell line with a luciferase reporter that is activated when there is induction of interferons.

Results

In all cases, RIG-I ligands alone did not stimulate IFN-γ secretion. Delivery of RIG-ligands with 3E10-D31N, however, stimulated IFN-γ secretion above controls, with the highest secretion observed for poly (I:C), both low and high molecular weight (LMW and HMW).

US11872286B2. Compositions and methods for delivery of nucleic acids to cells (2024-01-16). Yale University [US]. Inventors: Elias Quijano [US], Peter Glazer [US].
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Patent 2024
Biological Assay Cell Lines Cells DDX58 protein, human Interferons Interferon Type II Ligands Luciferases Obstetric Delivery Poly I-C secretion
5
Tracking Cascade of HCV Care in Healthcare System
The number of patients in each step of the care cascade was determined: screening and confirmatory testing, linkage to care, initiation of DAA, SVR. Laboratory results for HCV antibody and HCV viral tests (i.e., ribonucleic acid [RNA] and genotype) were used to determine screening and confirmatory testing, and respectively. This healthcare system has employed reflex testing for HCV RNA since 2012. Although the clinical test for HCV screening is the HCV antibody, this study assumed that anyone with test results for HCV RNA or genotype was previously screened with an HCV antibody test even if 1 was not recorded, accounting for patients who may have had HCV antibody testing before 2014 and then had confirmatory testing during the study period. This criterion also accounted for patients who received HCV screening at outside facilities and entered the healthcare system for follow-up care.
It was determined which screened patients did not have chronic HCV, were lost to follow-up, or needed linkage to care according to results of HCV antibody and viral tests (Fig. 1). HCV RNA test results of < 15 International Units per liter were considered undetectable. Seronegative patients, those with nonreactive HCV antibody tests, did not have HCV and did not need confirmatory testing or HCV care. Seropositive patients had reactive HCV antibody tests. Seropositive patients with undetectable HCV RNA did not need further HCV care, and seropositive patients with missing HCV RNA testing were considered to have incomplete HCV testing. Seropositive patients with detectable HCV RNA had chronic HCV and needed HCV care. If a patient had a recorded HCV genotype but no recorded RNA, it was assumed that the patient had detectable RNA and thus needed HCV care. This study used the HCV genotype test as a proxy for determining linkage to care (i.e., meeting with a medical professional who provides HCV treatment and management). However, if a patient had a record of initiating antiviral treatment without a recorded genotype, it was assumed that the patient was linked to care.
Initiation of antiviral treatment was defined as the presence of any of the following in the patient’s medication history: boceprevir, daclatasvir, dasabuvir, elbasvir, glecaprevir, grazoprevir, interferon alfacon-1, interferon alpha-2a, interferon alpha-2b, ledipasvir, ombitasvir, paritaprevir, pegylated interferon, pegylated interferon alpha-2b, pibrentasvir, ribavirin, ritonavir, simeprevir, sofosbuvir, telaprevir, velpatasvir, and voxilaprevir. Date of initiation of antiviral treatment was considered the first date that any of the medications were recorded. While treatment durations can vary from 8 to 24 weeks, this study defined SVR as the presence of undetectable HCV RNA at least 20 weeks after initiation of antiviral treatment and on any subsequent test.
Differences in the number of patients between steps indicated drop-offs between steps. For instance, patients who had been linked to care but did not initiate antiviral treatment were considered to have dropped off in the care cascade after the linkage to care step. Additionally, recorded deaths in the EHR were considered in determining progress through the care cascade.
Nakayama J., Hertzberg V.S., Ho J.C., Simpson R.L, & Cartwright E.J. (2023). Hepatitis C care cascade in a large academic healthcare system, 2012 to 2018. Medicine, 102(10), e32859.
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Publication 2023
Aftercare Antiviral Agents boceprevir daclatasvir dasabuvir elbasvir Follow-Up Care Genotype glecaprevir grazoprevir Hepatitis C Antibodies IFNA2 protein, human Interferon alfa 2a interferon alfacon-1 Interferons ledipasvir ombitasvir paritaprevir Patients peginterferon alfa-2b Pharmaceutical Preparations pibrentasvir Reflex Ribavirin Ritonavir RNA Simeprevir Sofosbuvir telaprevir velpatasvir voxilaprevir

Top products related to «Interferons»

1
Interferon ifn γ by Thermo Fisher Scientific
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Interferon (IFN)-γ is a cytokine produced by various immune cells, including T cells and natural killer cells. It plays a crucial role in the body's defense against viral infections and in the regulation of the immune system.
2
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
3
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
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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.
5
Ifn γ by Thermo Fisher Scientific
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IFN-γ is a laboratory reagent used to detect and measure interferon-gamma, a cytokine protein that plays a key role in the immune response. It is often used in cell-based assays and immunoassays to quantify IFN-γ levels in biological samples.
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TNF-α is a lab equipment product produced by Thermo Fisher Scientific. It is a protein involved in the regulation of immune cells and inflammation processes. The core function of TNF-α is to serve as a tool for researchers to study immune system dynamics and inflammatory responses in experimental settings.
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Ionomycin is a laboratory reagent used in cell biology research. It functions as a calcium ionophore, facilitating the transport of calcium ions across cell membranes. Ionomycin is commonly used to study calcium-dependent signaling pathways and cellular processes.

More about "Interferons"

Interferons (IFNs) are a diverse group of cytokines that play a crucial role in the immune system and cellular signaling.
These signaling proteins exhibit antiviral, antiproliferative, and immunomodulatory properties, making them a vital component in the body's defense against infections and diseases.
Interferons can be classified into different types, including IFN-α, IFN-β, and IFN-γ, each with unique functions and mechanisms of action.
Interferons regulate various cellular processes, such as cell growth, differentiation, and apoptosis.
They also enhance the expression of Major Histocompatibility Complex (MHC) molecules, which are essential for antigen presentation and the activation of the adaptive immune response.
Additionally, interferons can modulate the production of other cytokines, such as Tumor Necrosis Factor-alpha (TNF-α), and can influence the activity of immune cells, including T cells, B cells, and natural killer cells.
Researchers studying interferons often utilize techniques like cell culture, gene expression analysis, and protein quantification.
Common experimental tools used in interferon research include Fetal Bovine Serum (FBS) for cell culture, the RNeasy Mini Kit for RNA extraction, and TRIzol reagent for RNA isolation.
Stimulating cells with IFN-γ or the calcium ionophore Ionomycin can also be used to study the effects of interferons on cellular processes.
By leveraging the power of machine learning and artificial intelligence, PubCompare.ai's protocol comparison platform can help researchers optimize their interferon studies.
This technology enables the identification and comparison of the best protocols from literature, preprints, and patents, enhancing the reproducibility and accuracy of interferon research.
Researchers can streamline their workflow and drive breakthroughs in the field of interferon biology and its applications in various areas of biomedicine.