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| Item Type: | Preprint |
|---|---|
| Title: | Paw posture is a robust indicator for injury, pain, and age |
| Creators: |
Pritz, Christian Oliver  ORCID: https://orcid.org/0009-0002-3227-0630, Dotta, Sofia ORCID: https://orcid.org/0009-0006-3647-5480, Freund, Philip Alexander ORCID: https://orcid.org/0000-0003-0021-3850, Musso, Giada ORCID: https://orcid.org/0009-0002-3713-4910, Bader, Michael ORCID: https://orcid.org/0000-0003-4780-4164, Okladnikov, Nataliya ORCID: https://orcid.org/0000-0002-7429-5644, Rother, Franziska ORCID: https://orcid.org/0000-0002-4417-2192 and Marvaldi, Letizia ORCID: https://orcid.org/0000-0002-2021-3949
|
| 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 |
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