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Shan-Shan Dong

As seen in: Nature, Wiley Online Library, Genome Medicine, BioRxiv, Physical Review B, medRxiv, Diabetes Magazine
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Articles

Interfacial Modulation of Cu(I) Cluster-CNC Photonic Films for Efficient Circularly Polarized Luminescence and Multilevel Anticounterfeiting

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By Dao-Ming Wang, Shan-Shan Dong, Ke-Jin Jiang
| Wiley Online Library Verified
K.-J.Jiang, D.-M.Wang, S.-S.Dong, et al. “Interfacial Modulation of Cu(I) Cluster-CNC Photonic Films for Efficient Circularly Polarized Luminescence and Multilevel Anticounterfeiting.” Advanced Functional Materials (2026): e75613. https://doi.org/10.1002/adfm.75613 Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Genetic alternative splicing regulation mapping of cartilage and synovium reveals tissue-specific mechanisms of joint-related traits

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By Wen Tian, Shan-Shan Dong, Chen Wang, Jun-Qi Zhang, Feng Jiang, Zhi Yang, Tie-Lin Yang, Yan Guo, Shou-Ye Hu
| Nature Verified
Abstract Cartilage and synovium are essential tissues involved in joint-related diseases and traits, including osteoarthritis (OA), rheumatoid arthritis (RA), and human height. Although genome-wide association studies (GWAS) identify numerous risk loci for these traits, the molecular mechanisms underlying these associations, particularly those involving alternative splicing, remain poorly understood due to the lack of splicing-related genetic data in relevant tissues.
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Genetic Insights into Head-to-Body Ratios Via Deep Learning-Based Image Segmentation and Implications for Common Diseases - Nature Communications

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By Wei Shi, Shan-Shan Dong, Ren-Jie Zhu, Feng Jiang, Yan Guo
| Nature Verified
Abstract Head-to-body ratios (HBRs) are important anthropometric traits with direct relevance to human growth, development, and disease risk. However, the role of the proportions between head and body remains understudied, with the genetic basis of HBRs remaining largely unexplored. By applying deep learning models to 38,202 whole-body dual-energy X-ray absorptiometry images from the UK Biobank, we generated 10 distinct HBR phenotypes based on head (length/width) and various body dimensions.
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