What is he best known for?
The fields of study he is best known for:
- Gene
- Internal medicine
- Diabetes mellitus
His main research concerns Diabetes mellitus, Immunology, Type 1 diabetes, Internal medicine and Endocrinology.
He has researched Diabetes mellitus in several fields, including Autoantibody, Disease, Age of onset and Immunopathology.
His work carried out in the field of Immunology brings together such families of science as First-degree relatives and Prospective cohort study.
The concepts of his Type 1 diabetes study are interwoven with issues in Surgery, Epidemiology, Pancreas, Natural history and Pediatrics.
The various areas that George S. Eisenbarth examines in his Endocrinology study include Glutamate decarboxylase and Offspring, Pregnancy.
His research investigates the connection with Islet and areas like Antibody which intersect with concerns in Antigen and Cell.
His most cited work include:
- International Trial of the Edmonton Protocol for Islet Transplantation (1559 citations)
- Type I diabetes mellitus. A chronic autoimmune disease. (1344 citations)
- Type 1 Diabetes (1294 citations)
What are the main themes of his work throughout his whole career to date?
George S. Eisenbarth mainly focuses on Immunology, Diabetes mellitus, Internal medicine, Type 1 diabetes and Endocrinology.
His research on Immunology frequently links to adjacent areas such as Disease.
His studies in Diabetes mellitus integrate themes in fields like Immune system, Insulin and Genetic predisposition.
George S. Eisenbarth combines subjects such as Nod, Peptide and T-cell receptor with his study of Insulin.
His Internal medicine study combines topics from a wide range of disciplines, such as Gastroenterology and Offspring.
The Type 1 diabetes study which covers Human leukocyte antigen that intersects with Haplotype and Allele.
He most often published in these fields:
- Immunology (51.06%)
- Diabetes mellitus (47.60%)
- Internal medicine (44.15%)
What were the highlights of his more recent work (between 2008-2018)?
- Type 1 diabetes (38.96%)
- Immunology (51.06%)
- Diabetes mellitus (47.60%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Type 1 diabetes, Immunology, Diabetes mellitus, Internal medicine and Autoantibody.
His Type 1 diabetes research includes themes of Genetics, Antibody, Human leukocyte antigen, Prospective cohort study and Islet.
His work focuses on many connections between Immunology and other disciplines, such as Disease, that overlap with his field of interest in Intensive care medicine and Innate immune system.
His research in Diabetes mellitus focuses on subjects like Insulin, which are connected to Cohort and Age of onset.
His studies deal with areas such as Gastroenterology and Endocrinology as well as Internal medicine.
He focuses mostly in the field of Autoantibody, narrowing it down to matters related to Antigen and, in some cases, T cell and Monoclonal antibody.
Between 2008 and 2018, his most popular works were:
- Type 1 Diabetes (1294 citations)
- Genetics, pathogenesis and clinical interventions in type 1 diabetes (805 citations)
- Seroconversion to Multiple Islet Autoantibodies and Risk of Progression to Diabetes in Children (568 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Internal medicine
- Diabetes mellitus
His primary areas of investigation include Type 1 diabetes, Diabetes mellitus, Immunology, Internal medicine and Autoantibody.
His research integrates issues of PTPN22, Gene interaction, Allele and Glutamic acid in his study of Type 1 diabetes.
His Diabetes mellitus study combines topics in areas such as Prospective cohort study, Insulin, HLA-DR3 and Age of onset.
His study in Immunology is interdisciplinary in nature, drawing from both Disease, Oncology and Pancreas.
His research in Internal medicine intersects with topics in Gastroenterology, Endocrinology and Human leukocyte antigen.
His work deals with themes such as Secretion, Genome-wide association study and Islet, which intersect with Autoantibody.
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.
