What is he best known for?
The fields of study he is best known for:
- Immune system
- Antibody
- Gene
Srini V. Kaveri focuses on Immunology, Immune system, Antibody, IL-2 receptor and Innate immune system.
Srini V. Kaveri has included themes like Antigen and Cell biology in his Immune system study.
His study in Antibody is interdisciplinary in nature, drawing from both Receptor and Disease.
Srini V. Kaveri has researched IL-2 receptor in several fields, including Experimental autoimmune encephalomyelitis, Immune tolerance, FOXP3 and Antigen-presenting cell.
His studies deal with areas such as Proinflammatory cytokine, Microbiology and Mycobacterium tuberculosis as well as Innate immune system.
His study in the fields of Aspergillus fumigatus under the domain of Microbiology overlaps with other disciplines such as Cladosporium.
His most cited work include:
- Immunomodulation of autoimmune and inflammatory diseases with intravenous immune globulin. (1021 citations)
- Surface hydrophobin prevents immune recognition of airborne fungal spores (545 citations)
- Surface hydrophobin prevents immune recognition of airborne fungal spores (545 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Immunology, Antibody, Immune system, Autoantibody and Antigen.
His is involved in several facets of Immunology study, as is seen by his studies on Autoimmunity, Inflammation, Immunoglobulin G, Immunotherapy and Antigen-presenting cell.
His Antibody research is multidisciplinary, relying on both Molecular biology and Virology.
His work deals with themes such as Receptor and Cell biology, which intersect with Immune system.
His Antigen research incorporates elements of CD86, Cytotoxic T cell, IL-2 receptor, Proteomics and CD80.
His Innate immune system research focuses on Proinflammatory cytokine and how it connects with Mycobacterium tuberculosis.
He most often published in these fields:
- Immunology (123.02%)
- Antibody (87.21%)
- Immune system (67.26%)
What were the highlights of his more recent work (between 2013-2021)?
- Immunology (123.02%)
- Antibody (87.21%)
- Immune system (67.26%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Immunology, Antibody, Immune system, Immunotherapy and Autoantibody.
His work is connected to Immunoglobulin G, FOXP3, Innate immune system, Immunoglobulin E and Regulatory T cell, as a part of Immunology.
His research integrates issues of T cell, Molecular biology, Antigen and Virology in his study of Antibody.
Srini V. Kaveri does research in Immune system, focusing on Dendritic cell specifically.
His study looks at the relationship between Immunotherapy and topics such as Autoimmunity, which overlap with B cell.
Srini V. Kaveri studied Autoantibody and Internal medicine that intersect with Lipid peroxidation.
Between 2013 and 2021, his most popular works were:
- IVIG-mediated effector functions in autoimmune and inflammatory diseases (100 citations)
- Surface Structure Characterization of Aspergillus fumigatus Conidia Mutated in the Melanin Synthesis Pathway and Their Human Cellular Immune Response (74 citations)
- IVIG pluripotency and the concept of Fc-sialylation: challenges to the scientist. (60 citations)
In his most recent research, the most cited papers focused on:
- Immune system
- Gene
- Antibody
Srini V. Kaveri mostly deals with Immunology, Antibody, Immune system, Immunotherapy and FOXP3.
Srini V. Kaveri integrates many fields in his works, including Immunology and Immunologic Factors.
His Antibody research is multidisciplinary, incorporating perspectives in Prostaglandin E2, Interleukin 33 and Downregulation and upregulation.
His Immune system research incorporates themes from Autoantibody and Lymphocyte.
His Immunotherapy study combines topics from a wide range of disciplines, such as Autoimmunity and B cell.
His FOXP3 study incorporates themes from Regulatory T cell, IL-2 receptor, Immune tolerance, Concomitant and Flow cytometry.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
