![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
Tools
Tools
Preview |
PDF (Preprint)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2MB |
Preview |
PDF (Supplementary Material)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB |
| Item Type: | Preprint |
|---|---|
| Title: | Paw posture is a robust indicator for injury, pain, and age |
| Creators Name: | Pritz, Christian Oliver, Dotta, Sofia, Freund, Philip Alexander, Musso, Giada, Bader, Michael, Okladnikov, Nataliya, Rother, Franziska and Marvaldi, Letizia |
| Abstract: | Inferring biological states from animal behavior is a crucial but challenging step in biomedical discovery that is constrained by variability and labour-intensive assays, even with AI-powered tools. Here, we show that simple images of static paws, analyzed by our custom keypoint segmentation AI-tool provide accurate read-outs for a wide array of physiological states. Without invasive testing, our method detects postural changes associated with nerve injury, acute pain, aging, and the genetic loss of kpna4, a regulator of paw innervation. Leveraging the toe-spread-reflex, a spinal-circuit driven response, the approach requires no habituation and shows low behavioral variability. Individual digits emerge as biomarkers for internal states with digit V indicating neuropathic pain during nerve damage and digit I reflecting loss of kpna4. Our model is freely available and can readily be adapted to other tasks or species. These findings establish unstimulated paw posture as a scaleable, low-cost, readout for biological states. |
| Keywords: | Animals, Mice |
| Source: | bioRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2025.11.13.688005 |
| Date: | 13 November 2025 |
| Official Publication: | https://doi.org/10.1101/2025.11.13.688005 |
| Related to: |
Repository Staff Only: item control page
