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利用新型可扩展的干细胞培养设计未来
具有远见卓识的生物技术初创企业 Uncommon Bio 正在应对世界上最大的健康挑战之一:食品可持续性。在这次网络研讨会上,干细胞科学家塞缪尔-伊斯特(Samuel East)将展示他们如何使细胞农业的干细胞培养基既安全又经济可行。了解他们如何将培养基成本降低 1000 倍,并开发出不含动物成分、食品安全的 iPSC 培养基。
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Explore Alzheimer's Spatial Proteome with Big Data
Alzheimer's disease, a genetic and sporadic neurodegenerative condition, leads to cognitive decline in mid to late life, marked by β-amyloid plaques and tau tangles. With limited treatment options,…
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Dive into Pancreatic Cancer Research with Big Data
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 Big Data
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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细胞培养电子记录的 21 CFR 第 11 部分简介
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。