标题： 大型语言模型代理实现微孔微流控的自主设计与图像分析 显示英文标题

标题： Large Language Model Agents Enable Autonomous Design and Image Analysis of Microwell Microfluidics

摘要： 微孔微流控技术已被用于单细胞分析，以揭示基因表达、信号通路和表型反应的异质性，从而识别罕见细胞类型、理解疾病进展并开发更精确的治疗策略。 然而，设计微孔微流控系统是一项相当复杂的任务，需要知识、经验以及CAD软件，并且需要人工干预，这往往导致初始设计失败，需要多次昂贵且耗时的迭代。 在本研究中，我们建立了一个由大型语言模型（LLM）驱动的微孔设计框架，以生成基于代码的计算机辅助设计（CAD）脚本，从而实现具有多种几何形状和基于成像分析的微孔的快速可重复创建。 我们提出了一种基于将大型多模态语言模型（MLLM）生成的高层语义描述与图像嵌入相结合的多模态大型语言模型（MLLM）逻辑回归框架，用于图像分类任务，旨在识别微孔占用情况和微孔形状。 融合的多模态表示输入到一个既可解释又计算高效的逻辑回归模型中。 与仅依赖直接分类相比，我们在所有评估的MLLM中，在占用分类方面超过了0.92，在形状分类方面超过了0.99，而仅依赖直接分类时分别为0.50和0.55。 MLLM-逻辑回归框架是一种可扩展且高效的高通量微孔图像分析解决方案。 我们的研究通过将自然语言提示转化为优化的设备几何形状、CAD脚本和图像分析，展示了自主设计的微孔平台，促进了文献挖掘、自主设计和实验数据分析的整合，从而推动下一代数字发现的发展。 显示英文摘要

摘要： Microwell microfluidics has been utilized for single-cell analysis to reveal heterogeneity in gene expression, signaling pathways, and phenotypic responses for identifying rare cell types, understanding disease progression, and developing more precise therapeutic strategies. However, designing microwell microfluidics is a considerably complex task, requiring knowledge, experience, and CAD software, as well as manual intervention, which often fails initial designs, demanding multiple costly and time-consuming iterations. In this study, we establish an autonomous large language model (LLM)-driven microwell design framework to generate code-based computer-aided design (CAD) scripts, that enables the rapid and reproducible creation of microwells with diverse geometries and imaging-based analysis. We propose a multimodal large language model (MLLM)-logistic regression framework based on integrating high-level semantic descriptions generated by MLLMs with image embeddings for image classification tasks, aiming to identify microwell occupancy and microwell shape. The fused multimodal representation is input to a logistic regression model, which is both interpretable and computationally efficient. We achieved significant improvements, exceeding 0.92 for occupancy classification and 0.99 for shape classification, across all evaluated MLLMs, compared with 0.50 and 0.55, respectively, when relying solely on direct classification. The MLLM-logistic regression framework is a scalable, efficient solution for high-throughput microwell image analysis. Our study demonstrates an autonomous design microwell platform by translating natural language prompts into optimized device geometries, CAD scripts and image analysis, facilitating the development of next-generation digital discovery by integration of literature mining, autonomous design and experimental data analysis.