Machine learning models reveal microbial signatures in healthy human tissues, challenging the sterility of human organs
Anargyros Skoulakis, Giorgos Skoufos, Armen Ovsepian, Artemis G. Hatzigeorgiou · 2025
Read Machine learning models reveal microbial signatures in healthy human tissues, challenging the sterility of human organs by Anargyros Skoulakis, Giorgos Skoufos, Armen Ovsepian, Artemis G. Hatzigeorgiou (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11808598.txt.
Description
Anargyros Skoulakis and co-authors used learning models to test whether healthy organs host microbes, in work published in 2024. Human inner tissues were long thought to be sterile. The team mined gene-read data from more than 13,000 normal tissue samples across 28 tissue types.
Models could sort many tissues by their microbe profiles alone, which hints at distinct local communities. For eight tissues the signal survived checks against contamination. Heart, colon, and liver models also held up in samples from living donors. Key marker microbes were named for each of those tissues. The authors argue the results challenge the sterile-organ view, while urging care about false signals.
Catalog record
| CREATOR | Anargyros Skoulakis, Giorgos Skoufos, Armen Ovsepian, Artemis G. Hatzigeorgiou |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |