Projects / Programmes
Intelligent Content-Aware Nanospectroscopy (iCAN) of molecular events in nanoparticles-induced neurodegeneration
| Code |
Science |
Field |
Subfield |
| 1.02.07 |
Natural sciences and mathematics |
Physics |
Biophysics |
| 2.07.07 |
Engineering sciences and technologies |
Computer science and informatics |
Intelligent systems - software |
| Code |
Science |
Field |
| 1.03 |
Natural Sciences |
Physical sciences |
| 1.02 |
Natural Sciences |
Computer and information sciences |
nanoparticles, molecular initiating events, adverse-outcome pathways, neurodegeneration, inflammation, fluorescence, microscopy, spectroscopy, super-resolution optical methods, machine learning, computer vision
Data for the last 5 years (citations for the last 10 years) on
June 25, 2024;
A3 for period
2018-2022
Data for ARIS tenders (
04.04.2019 – Programme tender,
archive
)
| Database |
Linked records |
Citations |
Pure citations |
Average pure citations |
| WoS |
204 |
7,526 |
6,976 |
34.2 |
| Scopus |
220 |
9,763 |
9,085 |
41.3 |
Researchers (14)
Organisations (2)
Abstract
Inhalation of fine particulate matter (PM2.5) and ultrafine nanoparticles in polluted air is knowingly associated with neurodegenerative and other chronic diseases, which are one of the major contributors to the global death burden. Even though increasing amounts of engineered nanoparticles enter the environment, our limited understanding of the mechanisms of their action hinders efficient prevention and treatment of associated health conditions. To understand the causal relationship between exposure to nanoparticles and disease progression, it is crucial to discern Adverse Outcome Pathways (AOPs), which connect initiating events on the molecular level to the adverse outcome at the level of an organism via a complete sequence of causally linked key events. We have recently visualised such early supramolecular rearrangements at the NP-cell contact by advanced live-cell super-resolution microscopy and spectroscopy techniques. However, their slow acquisition process has precluded adequate sampling of such rare events to complete the AOPs. We aim to improve the statistics of the captured rare events by developing the Intelligent Content-Aware Nanospectroscopy (iCAN). We will employ intelligent state-of-the-art computer-vision algorithms to automatically identify the content of interest for targeted nanospectroscopic measurements, which will allow us to identify and quantify early events following NP exposure. By additional evaluation of the response of individual cells to their local dose, we aim to causally connect the early events leading towards neurodegenerative effects of exposure to nanoparticles.
