# Amadee-24-Genes4Mars

Last modified by Hermann Hinterhauser on 2024/03/26 12:01
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### Details

Acronym | Genes4Mars
Description | Astronaut performance and health monitoring with automated ECG andinvestigation of the changes in health-risk-associated gene expression patterns in blood and urine samples of astronauts.
Principal Investigator (PI) | Dr. Arsen Arakelyan | arsen.arakelyan@rau.am
Organisation | Institute of Biomedicine and Pharmacy, Russian-Armenian University; Institute of Molecular Biology NAS RA
Co-Investigators | Karen Avetisyan | avetisyan2016@gmail.com

### Summary

Long-term space missions are known to be associated with health risks to astronauts because of environmental factors, such as radiation, microgravity, and lasting isolation. These risks could be more aggravating during space exploration missions, e.g. to Mars, especially with limited access to healthcare facilities. It is suspected that space flights and planetary missions could have performance degradation and negative effects on the cardiovascular health of the crew. Therefore, the development of biomarker-based and physiological predictors of health risks for the assessment of physiological conditions is of vital importance.

The main objective of Genes4Mars is a retrospective investigation of changes in risk-associated gene expression patterns in blood and urine samples from the Analog Astronauts. It will also investigate how well automated electrocardiogram (ECG) analysis can be used to monitor astronaut performance during planetary missions, and whether ECG can be used as a surrogate marker for dynamic changes in cardiac function-related genes. The members of the flight crew could be considered as a case group and the back-up flight crew members could function as a control group. The samples will be collected from each participant at multiple time points pre-, during and past-mission and will be analysed after the mission is completed. RNA will be extracted from the samples immediately upon their transfer to the lab and analysed for up-or downregulated genes. Additionally, 12-lead and single-lead ECGs of each participant should be taken one week prior to the start of the mission and each day before and after field assignments outside the habitat. After the end of the mission, the data will be analysed using deep learning methods and pre-trained models to detect heart pathologies. Correlation and regression analysis will be used to find the association between gene expression and physiological parameters.

### Experiment Data

Date | Files
--- | ---
2021-10-04 | types of files for each experiment day, size of the cells: width 1000px, height 10px

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Created by Hermann Hinterhauser on 2024/03/25 11:55

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