Researchers from the Perelman School of Medicine at the University of Pennsylvania in Philadelphia developed a tool that uses artificial intelligence to image cells and provide insight into gene activity.A paper published Jan. 2 in Nature Biotechnology by Daiwei 'David' Zhang, PhD, and Mingyao Li, PhD, explains the technology and aims of the Inferring Super-Resolution Tissue Architecture (iStar).

费城宾夕法尼亚大学佩雷尔曼医学院（Perelman School of Medicine）的研究人员开发了一种工具，该工具使用人工智能对细胞成像，并提供对基因活动的深入了解。Daiwei“David”Zhang博士和Mingyao Li博士于1月2日在Nature Biotechnology上发表的一篇论文解释了推断超分辨率组织结构（iStar）的技术和目标。

iStar's purpose is to detect 'tertiary lymphoid structures': anti-tumor formations that correlate with a patient's likelihood of survival and positive response to immunotherapy. Dr. Li said iStar 'can capture the overarching tissue structures and also focus on the minutiae in a tissue image,' similar to how a pathologist would examine a tissue sample — but with a highly detailed and powerful lens. 'The speed of iStar makes it possible to reconstruct this huge amount of spatial data within a short period of time,' Dr.

iStar的目的是检测“三级淋巴结构”：与患者生存可能性和免疫治疗阳性反应相关的抗肿瘤形成。博士。李说，iStar“可以捕捉总体组织结构，也可以关注组织图像中的细节”，类似于病理学家检查组织样本的方式，但具有高度详细和强大的镜头。他说：“iStar的速度使得在短时间内重建如此庞大的空间数据成为可能。”。

Li said in a press release. With this increased speed, researchers hope to further their understanding of tissue microenvironments in thousands more samples..

李在新闻稿中说。随着速度的提高，研究人员希望在数千个样本中进一步了解组织微环境。。