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Facts about Interferon lambda-1.
Acts as a ligand for the heterodimeric class II cytokine receptor composed of IL10RB and IFNLR1, and receptor participation contributes to the activation of this JAK/STAT signaling pathway resulting in the expression of IFN- stimulated genes (ISG), which mediate the antiviral state. Has a limited receptor distribution and therefore restricted targets: is primarily active in epithelial cells and this cell type- selective action is due to the epithelial cell-specific expression of its receptor IFNLR1.
Human | |
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Gene Name: | IFNL1 |
Uniprot: | Q8IU54 |
Entrez: | 282618 |
Belongs to: |
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lambda interferon family |
cytokine Zcyto21; IFNL1; IFN-lambda 1; IFN-lambda-1; IL29; IL-29; interferon lambda-1; interferon, lambda 1; interleukin 29 (interferon, lambda 1); interleukin-29; ZCYTO21
Mass (kDA):
21.898 kDA
Human | |
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Location: | 19q13.2 |
Sequence: | 19; NC_000019.10 (39296407..39298673) |
Secreted.
Sanbio, a provider custom services, can help you learn more about Boster Bio or the IFNL1 marker. They can assist you with BeNeLux deliveries and custom services. Learn more about the Boster Bio and IFNL1 markers and how they can benefit your research. Here's a quick overview.
Interferon lambda-1, also known as IFNL1, is used in many biological experiments, including those that test for antiviral host defence. Interferon lambda-1 (interferon-like) is a protein that plays an important role in epithelial cells and antiviral hosts defense. Boster Bio developed a polyclonal antibody that reacts with IFNL1 in many animal samples.
Boster ELISA kit are highly sensitive, and can detect IFNL1 up to picogram level. These antibodies are extremely sensitive and have been validated against 250 tissue samples. Boster Biologicals stocks a variety antibodies and ELISA tools that can be used to identify biomarkers. Their antibodies have a high affinity, and they are quantitatively validated against known quantities of untransfected and recombinant cells.
IHC/ICC experiments are made easier by polyclonal antibody targeting the IFNL1 mark. IHC/ICC experiments rely on the primary antibodies. For optimal visualization and minimizing background signals, it must be as specific as possible. The working dilutions for monoclonal and polyclonal antibodies are generally lower than for monoclonal. To minimize cross-reactivity and maximize immunostaining spots, working dilutions for polyclonal antibodies must be determined empirically.
The antigen was identified using a FFPE portion of a human colon carcinoma specimen. In the instance of a p21+ tumor, the antibody binds directly to an antigen found on the FFPE. The specificity of this method depends on the IFNL1 marker used for detection. The target antigen must be present for a successful detection.
The IFNL1 specific antibody recognizes the IFNL1 proteins in cells. It is also highly specific for IL29, a type cytokine expressed in human adrenals. Formalin-fixed paraffin embedding tissue is used for immunohistochemical analysis. The product in its unconjugated state is shown in the image.
IFNL1 -specific antibodies have improved antibody sensitivity by increasing the IgG molecules. Until recently, the majority of polyclonal antibodies were heterogeneous and lacked antigen specificity. This characteristic has been significantly improved by immunogen affinity purified polyclonal antibody using the IFNL1 marker. Antigen-specific antibodies are enhanced by passing polyclonal antibody through an affinity column.
IHC-optimized polyclonally based antibodies targeting IFNL1 can increase specificity while reducing background staining. However, polyclonal antibodies have more specificity and a wider range of conformation. The stability of IFNL1 is better over a pH and salt level.
Because antibodies can be bound to multiple epitopes of the antigen, the dilutions of these proteins may vary. Temperature and incubation time also influence binding. Longer incubations could result in higher non-specific signs. In order to be effective, polyclonal antibody should be diluted so that they can bind multiple epitopes of the antigen.
Human C-peptide ELISA uses low-picogram levels to determine the presence of human connecting peptides in plasma or serum. This marker is for research purposes only. This marker has a half-life that is similar to insulin. It can be used as an indicator of beta cell production. Researchers can also detect both endogenous C-peptide from humans and recombinant ones, which allows them the ability to determine their sample's level.
Collaboration and partnerships are key to cancer research. Advancements in this field are often cyclical, flowing from observations with medical relevance to the patient's bedside, and back again. Scientists from many disciplines should be involved, as they can influence the findings of another. Research in one area of cancer can provide new ideas for another. Therefore, a multi-OMICS approach is critical for cancer research.
Big data, or large datasets, in cancer research includes genome scale experimental studies, imaging and behavioral data, as well as longitudinal health records. This dataset is heterogeneous and may have different levels or granularities. These factors have an impact on the AI approach's capability to identify biomedically pertinent patterns. Researchers can use multiple data types to create AI systems because of the heterogeneous landscape.
Data containing multiple variables is becoming more common in cancer research. This makes it crucial to have high-quality labeled datasets. Additionally, data relating cancer are often of different sizes. This necessitates the development of both discriminative AND generative models. The interoperability and compatibility of AI approaches in cancer AI is crucial to meet these challenges. AI can be used to greatly improve precision and accuracy in cancer research, even though it has its limitations.
AFM is particularly useful for single cancer cells. Because cancer can be detected early, doctors can identify and treat it by examining the differences between individual cells. AFM allows researchers to examine the morphological differences in cancer cells. This aids in identifying disease-causing mutations earlier. Additionally, cancer cells' interactions and spreading mechanisms can be studied using AFM, and cancer drugs can be tested in this manner. The technology can be applied to clinical practice once more cancer research is completed.
The convergence of AI and big data has opened up new avenues for understanding the disease. Scientists can study the chemical and physical properties at atomic levels of cells using AI and data analysis. In this way, they can make decisions to improve the treatment of cancer patients. The AI-driven approach has the potential to revolutionize cancer research and improve the quality of life for cancer patients. The research benefits will continue growing exponentially.
PMID: 12469119 by Sheppard P., et al. IL-28, IL-29 and their class II cytokine receptor IL-28R.
PMID: 12483210 by Kotenko S.V., et al. IFN-lambdas mediate antiviral protection through a distinct class II cytokine receptor complex.
*More publications can be found for each product on its corresponding product page