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Andrey V Kuznetsov
(A V Kuznetsov, I A Kuznetsov)
Loop profile
ORCID
Publication Activity (10 Years)
Years Active: 2017-2023
Publications (10 Years): 8
Top Topics
Reactive Oxygen Species
Endoplasmic Reticulum
Sars Cov
Spinal Cord Injury
Top Venues
Proceedings. Mathematical, physical, and engineering sciences
Biomaterials science
bioRxiv : the preprint server for biology
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This page only lists publications with an associated author ORCID identifier.
Publications
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Ivan A Kuznetsov
,
Andrey V Kuznetsov
Mitochondrial Transport in Axons with Multiple Branching Junctions: A Computational Study.
bioRxiv : the preprint server for biology
(2023)
Ivan A Kuznetsov
,
Andrey V Kuznetsov
Mitochondrial Transport in Axons with Multiple Branching Junctions: A Computational Study.
bioRxiv : the preprint server for biology
(2023)
Madelyn VanBlunk
,
Vishal Srikanth
,
Sharda S Pandit
,
Andrey V Kuznetsov
,
Yevgeny Brudno
Absorption rate governs cell transduction in dry macroporous scaffolds.
Biomaterials science
(2023)
Madelyn VanBlunk
,
Vishal Srikanth
,
Sharda S Pandit
,
Andrey V Kuznetsov
,
Yevgeny Brudno
Absorption rate governs cell transduction in dry macroporous scaffolds.
Biomaterials science
(2023)
Ivan A Kuznetsov
,
Andrey V Kuznetsov
How old are dense-core vesicles residing in en passant boutons: simulation of the mean age of dense-core vesicles in axonal arbours accounting for resident and transiting vesicle populations.
Proceedings. Mathematical, physical, and engineering sciences
476 (2241) (2020)
Ivan A Kuznetsov
,
Andrey V Kuznetsov
Modelling transport and mean age of dense core vesicles in large axonal arbours.
Proceedings. Mathematical, physical, and engineering sciences
475 (2228) (2019)
Andrey V Kuznetsov
,
A V Kuznetsov
How the formation of amyloid plaques and neurofibrillary tangles may be related: a mathematical modelling study.
Proceedings. Mathematical, physical, and engineering sciences
474 (2210) (2018)
Andrey V Kuznetsov
,
A V Kuznetsov
Simulating tubulin-associated unit transport in an axon: using bootstrapping for estimating confidence intervals of best-fit parameter values obtained from indirect experimental data.
Proceedings. Mathematical, physical, and engineering sciences
473 (2201) (2017)