What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Gene
- Pathology
His primary areas of investigation include Immunology, Pathology, Vasculitis, Internal medicine and Autoantibody.
J. Charles Jennette combines subjects such as Anti-neutrophil cytoplasmic antibody and Systemic vasculitis with his study of Immunology.
His research in Pathology intersects with topics in Focal segmental glomerulosclerosis, Glomerulonephritis and Nephropathy.
His study in the field of Granulomatosis with polyangiitis is also linked to topics like Medical terminology.
His Internal medicine course of study focuses on Endocrinology and Gene and Gene mutation.
The various areas that J. Charles Jennette examines in his Autoantibody study include Inflammation and Pathogenesis.
His most cited work include:
- The Classification of Glomerulonephritis in Systemic Lupus Erythematosus Revisited (1474 citations)
- International standardization of criteria for the histologic diagnosis of renal allograft rejection : the Banff working classification of kidney transplant pathology (1191 citations)
- Prostaglandin synthase 2 gene disruption causes severe renal pathology in the mouse (1014 citations)
What are the main themes of his work throughout his whole career to date?
J. Charles Jennette mostly deals with Pathology, Immunology, Internal medicine, Vasculitis and Autoantibody.
His studies deal with areas such as Nephropathy and Kidney as well as Pathology.
His Immunology study incorporates themes from Glomerulonephritis and Anti-neutrophil cytoplasmic antibody.
His Glomerulonephritis study combines topics in areas such as Lupus nephritis and Immunopathology.
His Internal medicine research includes themes of Gastroenterology and Endocrinology.
His research in Autoantibody focuses on subjects like Pathogenesis, which are connected to Alternative complement pathway.
He most often published in these fields:
- Pathology (39.16%)
- Immunology (37.26%)
- Internal medicine (30.04%)
What were the highlights of his more recent work (between 2013-2021)?
- Internal medicine (30.04%)
- Pathology (39.16%)
- Vasculitis (25.48%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Internal medicine, Pathology, Vasculitis, Immunology and Nephropathy.
His Internal medicine study integrates concerns from other disciplines, such as Gastroenterology and Endocrinology.
His biological study spans a wide range of topics, including Immunoglobulin light chain, Membranoproliferative glomerulonephritis, Glomerular basement membrane and Fibrillary Glomerulonephritis.
J. Charles Jennette has researched Vasculitis in several fields, including Inflammation and Pathogenesis.
In his work, Autoimmunity is strongly intertwined with Glomerulonephritis, which is a subfield of Immunology.
As a part of the same scientific study, J. Charles Jennette usually deals with the Nephropathy, concentrating on IgA vasculitis and frequently concerns with Cohort.
Between 2013 and 2021, his most popular works were:
- Heptinstall's Pathology of the Kidney (493 citations)
- Pathogenesis of antineutrophil cytoplasmic autoantibody-mediated disease (235 citations)
- Eosinophilic granulomatosis with polyangiitis (Churg-Strauss) (EGPA) Consensus Task Force recommendations for evaluation and management. (207 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Gene
- Disease
J. Charles Jennette mainly focuses on Internal medicine, Pathology, Nephropathy, Focal segmental glomerulosclerosis and Vasculitis.
His study in Surgery extends to Internal medicine with its themes.
J. Charles Jennette has included themes like Dermatology and Glomerulonephritis in his Pathology study.
His studies in Nephropathy integrate themes in fields like Glomerulosclerosis, Kidney disease and Renal function.
The concepts of his Vasculitis study are interwoven with issues in Myeloperoxidase and Consensus conference.
Proteinase 3 is a subfield of Immunology that J. Charles Jennette explores.
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.
