#AI reads Urine# Non-invasive giant panda pregnancy and pseudopregnancy biomonitoring by integrated metabolomics and steroidomics

The study aimed to achieve non-invasive biomonitoring of pregnancy and pseudopregnancy in giant pandas. It integrated polar metabolomics and steroidomics techniques to analyze 238 urine samples across 6 reproductive phases from 6 pregnant cycles and 7 pseudopregnant cycles. A total of 696 discriminative features were identified, including 174 features in the early luteal stage, which is earlier than the existing pregnancy diagnostic window. The study revealed differences in amino acid, energy, and steroid metabolism between pregnant and pseudopregnant individuals (e.g., significantly elevated androgen levels in pregnant individuals, which may be related to embryo implantation). Additionally, the constructed classification models based on biomarker panels achieved diagnostic accuracies ranging from 0.763 to 1.000 across different reproductive phases. These findings provide a new direction for optimizing immunoassay targets, improving the sensitivity and reliability of giant panda pregnancy monitoring, and also contribute to a deeper understanding of their reproductive biology to support conservation efforts.

Sci Rep. 2025 Oct 8;15(1):35187. doi: 10.1038/s41598-025-19067-7.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/