United States Patent and Trademark Office

Current insulin therapy fails to fully restore physiological glucose homeostasis in type 1 diabetes mellitus, with 75% of patients unable to achieve the desired management targets. While stem cell-derived islets offer promising therapy, they require an enhanced extracellular matrix support for optimal transplantation outcomes. To address this challenge, we developed biofunctional endocrine micro-pancreata using decellularized porcine lung scaffolds seeded with embryonic stem cell-derived islets. In vivo efficacy was evaluated following subcutaneous or intraperitoneal transplantation into NOD-SCID mice, followed by streptozotocin induction of diabetes, with the comprehensive assessment of human insulin secretion, glucose homeostasis, and graft integration over 3 months. Our results demonstrated that endocrine micro-pancreata exhibited 1.4-fold-increased glucose-stimulated insulin secretion in vitro compared to non-responsive free islets. In vivo, endocrine micro-pancreas recipients maintained significantly lower glucose levels than controls throughout the experiment. Subcutaneous endocrine micro-pancreata showed superior performance, with 46% improved glucose tolerance versus 31% improvement for intraperitoneal delivery. Extensive CD31-positive neovascularization as well as insulin staining confirmed successful graft integration and sustained insulin production. Endocrine micro-pancreata provide a scalable platform for diabetes cell therapy, demonstrating sustained insulin secretion and improved glycemic control. The preserved extracellular matrix microenvironment supports islet function and vascularization, offering significant potential for clinical translation.

Spatial domain identification, a pivotal task in spatial transcriptomics (ST) research, seeks to elucidate the spatial distribution relationships among diverse cell types and complex tissue architectures. Recent advancements in spatial transcriptomics technology have substantially improved our understanding of cellular spatial arrangements, intercellular interactions, and the local regulation of gene expression. However, conventional algorithms often face challenges in handling high-dimensional, sparse spatial transcriptomic data, which are highly susceptible to noise interference. To address these issues effectively, we propose a novel Transformer-based spatial domain recognition algorithm that synergizes the strengths of Transformer models and graph neural networks. This approach is specifically designed to overcome the limitations of high-dimensional sparse data. By representing spatial transcriptome data through a Transformer encoder structure, our model harnesses self-attention mechanisms and inter-layer dependencies to capture complex intercellular relationships. The self-attention mechanism enables global learning of dependencies across spatial regions, thereby enhancing the modeling of intricate tissue structures. Moreover, during training, we incorporate data augmentation and regularization techniques to mitigate overfitting risks and improve generalization capabilities across varied datasets. Experimental results indicate that our model achieves state-of-the-art performance on multiple datasets, significantly boosting the accuracy of spatial domain recognition and demonstrating heightened robustness in distinguishing cell types at different resolutions.

Following the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (AI), the U. S. Department of Commerce has issued guidance on AI-assisted inventions via the United States Patent and Trademark Office (USPTO). This framework ensures that human contributions are significant enough for patent eligibility based on the Federal Circuit's "significant contribution" test. The guidance clarifies that AI-assisted inventions are not categorically unpatentable and outlines criteria for naming human inventors. Additionally, the USPTO is collaborating with the U. S. Copyright Office on copyright issues related to AI. The USPTO's efforts include updating patent eligibility guidelines for AI innovations and engaging internationally to harmonize AI policies. These initiatives aim to balance the promotion of human ingenuity with the advancement of AI technologies, thus supporting a safe and innovative future.