{"title":"高效球体形态评估与ChatGPT数据分析：对细胞治疗的影响。","authors":"Takuya Sakamoto, Hiroto Koma, Ayane Kuwano, Tetsuhiro Horie, Atsushi Fuku, Hironori Kitajima, Yuka Nakamura, Ikuhiro Tanida, Yujiro Nakade, Hiroaki Hirata, Yoshiyuki Tachi, Hiroshi Sunami, Daisuke Sakamoto, Sohsuke Yamada, Naoki Yamamoto, Yusuke Shimizu, Yasuhito Ishigaki, Toru Ichiseki, Ayumi Kaneuji, Satoshi Osawa, Norio Kawahara","doi":"10.1080/07366205.2025.2493489","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities.</p><p><strong>Objective: </strong>SphereRing<sup>®</sup> is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. 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引用次数: 0
摘要
背景：脂肪源性干细胞（ADSCs）在治疗包括骨关节炎在内的多种疾病方面显示出良好的潜力。来源于ADSCs的球状体是一种可行的治疗选择，具有增强的抗炎作用和组织修复能力。目的：SphereRing®是一种旋转的环状管，可以有效地产生大量的球体。然而，准确测量球体尺寸以评估球体质量是一项挑战。本研究旨在通过ChatGPT Data Analyst进行图像识别和处理，利用深度学习开发一种自动测量球体尺寸的方法。方法：利用ChatGPT Data Analyst和ImageJ对SphereRing系统生成的球体的面积、周长和圆度进行分析。采用Bland-Altman分析和散点图相关系数验证测量精度。结果：ChatGPT Data Analyst与ImageJ在各参数上一致。布兰德和奥特曼的情节表现出强烈的一致性；大多数数据点在95%范围内。结论：ChatGPT数据分析为评估球体质量提供了可靠和有效的替代方法。该方法减少了人为误差，提高了再现性，加强了球体质量控制。因此，该方法在再生医学中具有潜在的应用前景。
Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy.
Background: Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities.
Objective: SphereRing® is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. This study aimed to develop an automated method for measuring spheroid size using deep learning through the ChatGPT Data Analyst for image recognition and processing.
Method: The area, perimeter, and circularity of spheroids generated with the SphereRing system were analyzed using ChatGPT Data Analyst and ImageJ. Measurement accuracy was validated using Bland-Altman analysis and scatter plot correlation coefficients.
Results: ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland-Altman plots demonstrated strong agreement; most data points were within the 95% limits.
Conclusion: The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. This method reduces human error and improves reproducibility to enhance spheroid quality control. Thus, this method has potential applications in regenerative medicine.
期刊介绍：
BioTechniques is a peer-reviewed, open-access journal dedicated to publishing original laboratory methods, related technical and software tools, and methods-oriented review articles that are of broad interest to professional life scientists, as well as to scientists from other disciplines (e.g., chemistry, physics, computer science, plant and agricultural science and climate science) interested in life science applications for their technologies.
Since 1983, BioTechniques has been a leading peer-reviewed journal for methods-related research. The journal considers:
Reports describing innovative new methods, platforms and software, substantive modifications to existing methods, or innovative applications of existing methods, techniques & tools to new models or scientific questions
Descriptions of technical tools that facilitate the design or performance of experiments or data analysis, such as software and simple laboratory devices
Surveys of technical approaches related to broad fields of research
Reviews discussing advancements in techniques and methods related to broad fields of research
Letters to the Editor and Expert Opinions highlighting interesting observations or cautionary tales concerning experimental design, methodology or analysis.
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