AI digital pathology as a key tool providing in-depth understanding of the progression and regression of MASH and fibrosis in male mouse models
Ashmita Saigal, Desiree Abdurrachim, Emily Tso, Christopher Hendra, Charlene Zhi Lin Ong, Sonya Kimball, Wendy Blumenschein, Yang Liu, Xueyuan Jiang, Arun J Sanyal, Asad Abu Bakar Ali, Saswata Talukdar · 2026
Read AI digital pathology as a key tool providing in-depth understanding of the progression and regression of MASH and fibrosis in male mouse models by Ashmita Saigal, Desiree Abdurrachim, Emily Tso, Christopher Hendra, Charlene Zhi Lin Ong, Sonya Kimball, Wendy Blumenschein, Yang Liu, Xueyuan Jiang, Arun J Sanyal, Asad Abu Bakar Ali, Saswata Talukdar (2026) 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/PMC13392390.txt.
Description
Ashmita Saigal and a large team use, in 2026, AI digital pathology to profile mouse models of fatty liver disease and scarring in fine spatial detail. Sharper models are key to grasping this liver disease and to judging would-be drugs.
Past standard tissue work, the pipeline reads each liver zone on its own. It maps where key disease traits sit side by side in the organ's layout. Blood tests and gene profiles round out the profile of these diet-fed male mice. Liver drug developers gain a richer model benchmark, since a drug that lifts mean scores but misses the sick zones could be misjudged by a plain read.
Catalog record
| CREATOR | Ashmita Saigal, Desiree Abdurrachim, Emily Tso, Christopher Hendra, Charlene Zhi Lin Ong, Sonya Kimball, Wendy Blumenschein, Yang Liu, Xueyuan Jiang, Arun J Sanyal, Asad Abu Bakar Ali, Saswata Talukdar |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 81308 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |