What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Cell biology, Apoptosis, Autophagy, Programmed cell death and Mitochondrion.
His studies in Cell biology integrate themes in fields like Ubiquitin, Biochemistry, Transgene and Bcl-2-associated X protein.
His research in the fields of Cytochrome c, Fas receptor and Truncated BID overlaps with other disciplines such as Cathepsin B.
His research integrates issues of Liver injury, Pharmacology and Fatty liver in his study of Autophagy.
Xiao Ming Yin focuses mostly in the field of Programmed cell death, narrowing it down to matters related to Signal transduction and, in some cases, Bcl-2 family, Cell loss and Ischemia.
His work on Inner mitochondrial membrane as part of general Mitochondrion research is frequently linked to Calpastatin, thereby connecting diverse disciplines of science.
His most cited work include:
- Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (4170 citations)
- Guidelines for the use and interpretation of assays for monitoring autophagy (3242 citations)
- Bcl-2 functions in an antioxidant pathway to prevent apoptosis (3160 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Cell biology, Autophagy, Apoptosis, Mitochondrion and Cancer research.
Xiao Ming Yin has researched Cell biology in several fields, including Biochemistry and Programmed cell death.
The concepts of his Programmed cell death study are interwoven with issues in Cancer cell, Cell and Proteasome inhibitor, Proteasome.
His study of Autophagosome is a part of Autophagy.
His Mitochondrion research is multidisciplinary, incorporating elements of Molecular biology, Reactive oxygen species, Mitophagy and Mitochondrial permeability transition pore.
The Cancer research study which covers Carcinogenesis that intersects with Liver tumor.
He most often published in these fields:
- Cell biology (63.00%)
- Autophagy (57.50%)
- Apoptosis (31.00%)
What were the highlights of his more recent work (between 2017-2021)?
- Autophagy (57.50%)
- Cancer research (25.50%)
- Cell biology (63.00%)
In recent papers he was focusing on the following fields of study:
Xiao Ming Yin spends much of his time researching Autophagy, Cancer research, Cell biology, HMGB1 and Fatty liver.
The Autophagy study combines topics in areas such as Carcinogenesis, Liver injury and Homeostasis.
His Cancer research research includes elements of Cancer, Pathogenesis, Inflammation, Fibrosis and Acute kidney injury.
His Cell biology research incorporates elements of Programmed cell death and PINK1, Mitophagy.
His Programmed cell death research is multidisciplinary, relying on both Autolysosome, Multicellular organism and In vivo.
He combines topics linked to Apoptosis with his work on Mitochondrion.
Between 2017 and 2021, his most popular works were:
- PINK1-PRKN/PARK2 pathway of mitophagy is activated to protect against renal ischemia-reperfusion injury (84 citations)
- Autophagy is a gatekeeper of hepatic differentiation and carcinogenesis by controlling the degradation of Yap (47 citations)
- HMGB1 promotes ductular reaction and tumorigenesis in autophagy-deficient livers (39 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Autophagy, Cell biology, Cancer research, Programmed cell death and Mitochondrion.
His Autophagy research incorporates themes from Cytoplasm, Steatosis, Homeostasis, Liver injury and Fatty liver.
His Cell biology research includes themes of Cell, Hepatic stellate cell and Cell cycle checkpoint.
His studies in Cancer research integrate themes in fields like Cancer, Cancer therapy, Gene, Fibrosis and Acute kidney injury.
His research in Programmed cell death intersects with topics in ATG8, In vitro, Cytotoxicity, Cancer cell and In vivo.
His Mitochondrion study combines topics from a wide range of disciplines, such as Apoptosis, Reactive oxygen species and Organelle.
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.
