Dr. Adiv Johnson
Adiv Johnson obtained both his B.S. in Molecular and Cellular Biology and his Ph.D. in Physiological Sciences from the University of Arizona. As an undergraduate, he studied the effects of dietary restriction and different pharmaceuticals on mosquito lifespan. As a graduate student, he investigated the molecular mechanisms underlying various ocular diseases (including the age-related disease glaucoma and the inherited retinopathy Best disease, which is phenotypically similar to age-related macular degeneration). He then went on to become a Research Fellow in Ophthalmology at the Mayo Clinic in Minnesota. During his postdoctoral fellowship, he researched ocular regeneration in planarian flatworms and performed patient-specific disease modeling using induced pluripotent stem cells.
In 2015 he joined the advanced microscopy company Nikon Instruments, where he currently works as a national sales manager. He has consistently maintained active aging research projects and his primary research interests are lifespan regulation, healthspan extension, and rejuvenation.
""Aging clocks that can accurately measure a patient’s biological age have the potential to significantly accelerate clinical trials that test anti-aging interventions. Highly robust aging clocks that predict human age in a given tissue can be developed via machine learning analysis of large patient datasets. Moreover, bioinformatics analyses of transcriptomes and proteomes can identify thoughtful molecular targets for anti-aging therapies. They can additionally help us understand the biological processes that are most impacted by aging"", says Dr. Adiv Johnson.
Adiv was a speaker at the 2020 Undoing Aging Conference event.
In 2015 he joined the advanced microscopy company Nikon Instruments, where he currently works as a national sales manager. He has consistently maintained active aging research projects and his primary research interests are lifespan regulation, healthspan extension, and rejuvenation.
""Aging clocks that can accurately measure a patient’s biological age have the potential to significantly accelerate clinical trials that test anti-aging interventions. Highly robust aging clocks that predict human age in a given tissue can be developed via machine learning analysis of large patient datasets. Moreover, bioinformatics analyses of transcriptomes and proteomes can identify thoughtful molecular targets for anti-aging therapies. They can additionally help us understand the biological processes that are most impacted by aging"", says Dr. Adiv Johnson.
Adiv was a speaker at the 2020 Undoing Aging Conference event.