What is he best known for?
The fields of study he is best known for:
- Gene
- Internal medicine
- Enzyme
The scientist’s investigation covers issues in Cell biology, Receptor, Molecular biology, T cell and T-cell receptor.
His research in Cell biology is mostly concerned with Signal transduction.
His Receptor research includes elements of Immune system, Immunology, Proinflammatory cytokine and Thymocyte.
His study looks at the relationship between Molecular biology and fields such as Antigen, as well as how they intersect with chemical problems.
His study in T cell is interdisciplinary in nature, drawing from both Cell signaling, CD3 and Raft.
His T-cell receptor study incorporates themes from Cell and CTLA-4.
His most cited work include:
- Induction of colonic regulatory T cells by indigenous Clostridium species (2332 citations)
- Fcγ Receptor–mediated Induction of Dendritic Cell Maturation and Major Histocompatibility Complex Class I–restricted Antigen Presentation after Immune Complex Internalization (842 citations)
- Interaction of tyrosine-based sorting signals with clathrin-associated proteins (825 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Cell biology, Internal medicine, Molecular biology, Receptor and Immunology.
His Cell biology research includes themes of T cell, Immunological synapse and T-cell receptor.
The various areas that Takashi Saito examines in his T cell study include Cytotoxic T cell and Antigen.
His studies in T-cell receptor integrate themes in fields like CD28, CD3 and CD8.
His Internal medicine research is multidisciplinary, incorporating elements of Endocrinology and Cardiology.
Takashi Saito studies Immunology, focusing on Immune system in particular.
He most often published in these fields:
- Cell biology (25.91%)
- Internal medicine (13.77%)
- Molecular biology (13.50%)
What were the highlights of his more recent work (between 2017-2021)?
- Neuroscience (10.39%)
- Cell biology (25.91%)
- Internal medicine (13.77%)
In recent papers he was focusing on the following fields of study:
Takashi Saito mainly focuses on Neuroscience, Cell biology, Internal medicine, Disease and Amyloid precursor protein.
The study incorporates disciplines such as Proteome and Microglia in addition to Neuroscience.
His work carried out in the field of Cell biology brings together such families of science as Alzheimer's disease, Receptor, Amyloid and T-cell receptor.
His Internal medicine study integrates concerns from other disciplines, such as Gastroenterology, Endocrinology and Cardiology.
Takashi Saito works mostly in the field of Disease, limiting it down to topics relating to Cognition and, in certain cases, Epilepsy.
His work deals with themes such as Genetically modified mouse, Hippocampus and Presenilin, which intersect with Amyloid precursor protein.
Between 2017 and 2021, his most popular works were:
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition) (345 citations)
- Amyloid β oligomers constrict human capillaries in Alzheimer’s disease via signaling to pericytes (139 citations)
- Transmission of amyloid-β protein pathology from cadaveric pituitary growth hormone (65 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Internal medicine
- Enzyme
Takashi Saito mainly investigates Neuroscience, Amyloid precursor protein, Cell biology, Internal medicine and Neurodegeneration.
His Neuroscience research is multidisciplinary, relying on both Extracellular, Neuroinflammation and Genetically modified mouse.
His studies deal with areas such as Mutation, Presenilin, Hippocampus and Transgene as well as Amyloid precursor protein.
His Cell biology research is multidisciplinary, incorporating perspectives in T cell, T-cell receptor and Receptor.
T cell is a subfield of Immunology that he tackles.
His work in Internal medicine covers topics such as Endocrinology which are related to areas like Voltage-dependent calcium channel, Thioflavin, Memantine and Glutathione.
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.
