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Dive into Pancreatic Cancer Research with the Big Data Viewer
Pancreatic cancer, with a mortality rate near 40%, is challenging to treat due to its proximity to major organs. This story explores the complex biology of pancreatic ductal adenocarcinoma (PDAC),…
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Uncover the Hidden Complexity of Colon Cancer with the Big Data Viewer
Colorectal cancer poses a significant health burden. While surgery is effective initially, some patients develop recurrent secondary disease with poor prognosis, necessitating advanced therapies like…
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Introduction to 21 CFR Part 11 for Electronic Records of Cell Culture
This article provides an introduction to the recommendations of 21 CFR Part 11 from the FDA, specifically focusing on the audit trail and user management in the context of cell-culture laboratories.…
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Mapping Tumor Immune Landscape with AI-Powered Spatial Proteomics
Spatial mapping of untreated tumors provides an overview of the tumor immune architecture, useful for understanding therapeutic responses. Immunocompetent murine models are essential for identifying…
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Spatial Analysis of Neuroimmune Interactions in Alzheimer’s Disease
Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by neurofibrillary tangles, β-amyloid plaques, and neuroinflammation. These dysfunctions trigger or are exacerbated by…
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A Guide to Spatial Biology
What is spatial biology, and how can researchers leverage its tools to meet the growing demands of biological questions in the post-omics era? This article provides a brief overview of spatial biology…
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使用空间多重化探测人类阿尔茨海默病皮层切片
阿尔茨海默病(AD)是最常见的神经退行性疾病,其特征是认知功能的逐渐下降。对 AD 大脑的空间分析可能揭示细胞关系,从而促进对疾病病因的更好理解。本研究捕捉了 AD 皮层组织成分的全球概述,并强调了 Cell DIVE 成像的简化工作流程,从数据采集到使用 Aivia 软件的基于人工智能的分析,最终实现更快的洞察。
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您的 3D 类器官成像和分析工作流程效率如何?
类器官模型已经改变了生命科学研究,但优化图像分析协议仍然是一个关键挑战。本次网络研讨会探讨了类器官研究的简化工作流程,首先是实时的三维细胞培养检查,接下来是高速、高分辨率的三维成像,生成清晰的图像和更纯净的数据,以便对生长速率、细胞迁移和三维细胞相互作用等参数进行准确地人工智能分割和量化,从而实现更深入的洞察。
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利用AI实现细胞转染的高效分析
本文探讨了AI(AI)在优化 2D 细胞培养研究中转染效率测量中的关键作用。对于理解细胞机制而言,精确可靠的 2D 细胞培养转染效率测量至关重要。靶向蛋白的高转染效率对于包括活细胞成像和蛋白纯化在内的实验至关重要。手动估计存在不一致性和不可靠性。借助AI的力量,可以实现高效可靠的转染研究。