Cargando…
Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome
BACKGROUND: Antimicrobial resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space habitat due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is id...
Autores principales: | Madrigal, Pedro, Singh, Nitin K., Wood, Jason M., Gaudioso, Elena, Hernández-del-Olmo, Félix, Mason, Christopher E., Venkateswaran, Kasthuri, Beheshti, Afshin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400218/ https://www.ncbi.nlm.nih.gov/pubmed/35999570 http://dx.doi.org/10.1186/s40168-022-01332-w |
Ejemplares similares
-
Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces
por: Singh, Nitin Kumar, et al.
Publicado: (2018) -
Correction to: Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces
por: Singh, Nitin Kumar, et al.
Publicado: (2018) -
Metabolic modeling of the International Space Station microbiome reveals key microbial interactions
por: Kumar, Rachita K., et al.
Publicado: (2022) -
Draft Genome Sequences of Sphingomonas Species Associated with the International Space Station
por: Bijlani, Swati, et al.
Publicado: (2020) -
Draft Genome Sequences of Bacillaceae Strains Isolated from the International Space Station
por: Daudu, Robert, et al.
Publicado: (2020)