Mycoplasma agalactiae P48 antibody and antigen (recombinant protein)

Diagnostic anti-Mycoplasma agalactiae P48 antibodies pairs and antigen for animal health (animal Bovines/Cattle, Ovines/Sheep, Caprine/Goat infectious disease contagious agalactia) testing in ELISA, colloidal gold-based Lateral flow immunoassay (LFIA), CLIA, TINIA and POCT

Target products collectionGo to Ruminants disease testing products collection >>


Product information

Catalog No. Description US $ Price (per mg)
GMP-VT-P081-Tg001-Ag01 Recombinant Mycoplasma agalactiae P48 protein $3090.00
GMP-VT-P081-Tg001-Ab01 Anti-Mycoplasma agalactiae P48 mouse monoclonal antibody (mAb) $3090.00
GMP-VT-P081-Tg001-Ab02 Anti-Mycoplasma agalactiae P48 mouse monoclonal antibody (mAb) $3090.00
GMP-VT-P081-Tg001-Ab03 Anti-Mycoplasma agalactiae P48 human monoclonal antibody (mAb) $3090.00
GMP-VT-P081-Tg001-Ab04 Anti-Mycoplasma agalactiae P48 human monoclonal antibody (mAb) $3090.00

Size: 1mg | 10mg | 100mg



Product Description

Cat No. GMP-VT-P081-Tg001-Ag01
Product Name Recombinant Mycoplasma agalactiae P48 protein
Pathogen Mycoplasma agalactiae
Expression platform E.coli
Isotypes Recombinant Antigen
Bioactivity validation Anti-Mycoplasma agalactiae P48 antibodies binding, Immunogen in Sandwich Elisa, lateral-flow tests, and other immunoassays as control material in Mycoplasma agalactiae level test of animal Bovines/Cattle, Ovines/Sheep, Caprine/Goat infectious disease with contagious agalactia.
Tag His
Product description Recombinant Mycoplasma agalactiae P48 proteinwas expressed in E.coli - based prokaryotic cell expression system and is expressed with 6 HIS tag at the C-terminus.
Purity Purity: ≥95% (SDS-PAGE)
Application Paired antibody immunoassay validation in sandwich Elisa, ELISA, colloidal gold-based Lateral flow immunoassay (LFIA), CLIA, TINIA, POCT and other immunoassays.
Formulation Lyophilized from sterile PBS, PH 7.4
Storage Store at -20℃ to -80℃ under sterile conditions. Avoid repeated freeze-thaw cycles.


Cat No. GMP-VT-P081-Tg001-Ab01,GMP-VT-P081-Tg001-Ab02
Pathogen Mycoplasma agalactiae
Product Name Anti-Mycoplasma agalactiae P48 mouse monoclonal antibody (mAb)
Expression platform CHO
Isotypes Mouse IgG
Bioactivity validation Recombinant Mycoplasma agalactiae P48 antigen binding, ELISA validated as capture antibody and detection antibody. Pair recommendation with other anti-Mycoplasma agalactiae antibodies in Mycoplasma agalactiae level test of animal Bovines/Cattle, Ovines/Sheep, Caprine/Goat infectious disease with contagious agalactia.
Product description Anti-Mycoplasma agalactiae P48 mouse monoclonal antibody (mAb) is a mouse monoclonal antibody produced by CHO technology. The antibody is ELISA validated as capture antibody and detection antibody. Pair recommendation with other anti-Mycoplasma agalactiae antibodies.
Purity Purity: ≥95% (SDS-PAGE)
Application Paired antibody immunoassay validation in sandwich Elisa, ELISA, colloidal gold-based Lateral flow immunoassay (LFIA), CLIA, TINIA, POCT and other immunoassays.
Formulation Lyophilized from sterile PBS, PH 7.4
Storage Store at -20℃ to -80℃ under sterile conditions. Avoid repeated freeze-thaw cycles.


Cat No. GMP-VT-P081-Tg001-Ab03,GMP-VT-P081-Tg001-Ab04
Pathogen Mycoplasma agalactiae
Product Name Anti-Mycoplasma agalactiae P48 human monoclonal antibody (mAb)
Expression platform CHO
Isotypes Human lgG1
Bioactivity validation Recombinant Mycoplasma agalactiae P48 antigen binding, ELISA validated as capture antibody and detection antibody. Pair recommendation with other anti-Mycoplasma agalactiae antibodies in Mycoplasma agalactiae level test of animal Bovines/Cattle, Ovines/Sheep, Caprine/Goat infectious disease with contagious agalactia.
Product description Anti-Mycoplasma agalactiae P48 mouse monoclonal antibody (mAb) is a human monoclonal antibody produced by CHO. The antibody is ELISA validated as capture antibody and detection antibody pair.
Purity Purity: ≥95% (SDS-PAGE)
Application Paired antibody immunoassay validation in sandwich Elisa, ELISA, colloidal gold-based Lateral flow immunoassay (LFIA), CLIA, TINIA, POCT and other immunoassays.
Formulation Lyophilized from sterile PBS, PH 7.4
Storage Store at -20℃ to -80℃ under sterile conditions. Avoid repeated freeze-thaw cycles.


Reference




    Validation Data


    Click to get more Data / Case study about the product.



    Pathogen Information


    The knowledge cutoff date for this response is September 2021, and the current date is October 31, 2023. Please keep in mind that I won't have access to events, developments, or discoveries that occurred after September 2021.

    Artificial Intelligence (AI): A Comprehensive Overview

    Artificial Intelligence (AI) is a rapidly evolving field that focuses on creating machines and software capable of intelligent behavior. These intelligent systems are designed to perform tasks that would typically require human intelligence, such as learning, problem-solving, perception, language understanding, and decision-making. AI technologies have a broad range of applications, from virtual personal assistants like Siri and Alexa to autonomous vehicles and advanced medical diagnostic systems.

    History of AI:

    AI has a rich history that dates back to ancient times when philosophers like Aristotle contemplated the nature of thought and reasoning. However, the modern era of AI began in the mid-20th century. In 1956, the Dartmouth Workshop, led by John McCarthy, is often considered the birth of AI as a field. Early AI research focused on symbolic AI, which used formal logic and symbols to mimic human reasoning.

    During the 1970s and 1980s, AI experienced a period of "AI winter" when progress slowed due to over-optimistic expectations and challenges in AI development. However, in the 21st century, AI has experienced a remarkable resurgence, largely thanks to advancements in machine learning, increased computational power, and the availability of large datasets.

    Machine Learning:

    One of the key drivers of the recent AI renaissance is machine learning. Machine learning is a subset of AI that involves training algorithms to recognize patterns in data and make predictions or decisions based on that data. There are various machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training algorithms on labeled data, while unsupervised learning clusters data based on patterns, and reinforcement learning focuses on decision-making and optimization.

    Deep learning, a subfield of machine learning, has gained significant attention and success. Deep neural networks, inspired by the structure of the human brain, have shown exceptional capabilities in tasks like image recognition, natural language processing, and playing complex games. Convolutional neural networks (CNNs) are widely used for image-related tasks, while recurrent neural networks (RNNs) are employed for sequence data like language processing.

    Natural Language Processing (NLP):

    Natural Language Processing is an area of AI that focuses on enabling machines to understand, interpret, and generate human language. NLP has led to the development of chatbots, virtual assistants, and language translation tools. One prominent example is the GPT (Generative Pre-trained Transformer) series, which includes models like GPT-3 and GPT-4. These models can generate human-like text and have numerous applications in content generation, language translation, and chatbots.

    Computer Vision:

    Computer vision is another significant branch of AI that involves teaching machines to interpret and understand visual information from the world, such as images and videos. This technology is used in facial recognition, object detection, and self-driving cars. Advances in computer vision have made it possible for autonomous vehicles to navigate complex environments, and for systems like Amazon Go to create cashier-less stores that rely on computer vision for customer transactions.

    Ethical and Social Considerations:

    The rapid advancement of AI raises ethical and social concerns. Issues related to bias in AI systems, job displacement, privacy, and the potential misuse of AI for malicious purposes have come to the forefront. There's an ongoing conversation about how to regulate and govern AI to ensure its responsible development and deployment. Organizations and governments worldwide are working on AI ethics guidelines and regulations to address these concerns.

    Current and Future Applications:

    AI is finding its way into a wide range of applications, from healthcare and finance to entertainment and education. In healthcare, AI is being used for early disease detection, drug discovery, and personalized treatment plans. In finance, AI helps with fraud detection, risk assessment, and algorithmic trading. In education, AI can personalize learning experiences and provide students with customized content and feedback. As AI continues to evolve, its potential applications are only limited by our imagination and ethical considerations.

    Conclusion:

    Artificial Intelligence is a dynamic and transformative field that has come a long way from its inception in the 1950s. With advancements in machine learning, natural language processing, computer vision, and ethical considerations, AI has the potential to reshape numerous industries and improve various aspects of our lives. However, it also brings significant ethical and social challenges that need to be addressed as it becomes increasingly integrated into our daily existence. The ongoing development of AI continues to be a fascinating journey with endless possibilities, and we must ensure it is used for the betterment of humanity.



    About GDU


    GDU

    GDU helps global diagnostic partners in high quality of raw material discovery, development, and application. GDU believes in Protein&antibody Innovation for more reliable diagnostic solutions.