[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2336":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":24,"paper":25,"created_at":41},2336,"System architecture and interactive LLM prompt framework for the integration of clinical and mitochondrial data in obesity: a software prototype and feasibility study","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1923426","Introduction Obesity is a critical global health challenge due to its close association with cardiovascular and metabolic risk. At the molecular level, the lipotoxic environment alters the structure, dynamics, and connectivity of the mitochondrial network. Despite advances in cell biology and digital health, there is a lack of lightweight computational tools capable of integrating clinical records with molecular and photomicrographic data in a standardized way. This study presents the design, development, and feasibility assessment of a decoupled software prototype and an interactive prompt engineering framework for integrating clinical and histological mitochondrial data in molecular obesity research. Methodology A client-side software architecture was designed using HTML5, CSS3, and the IndexedDB API for the transactional and anonymized persistence of medical records (coded using the ICD-10 classification) and micrographs of human placental tissue (obtained using transmission electron microscopy, immunofluorescence, and immunohistochemistry for the evaluation of mitochondrial fusion molecular markers such as Mitofusin-2, nitrotyrosine). For interpretive processing, a framework of prompts structured under the Chain-of-Thought paradigm was built, designed to interact with Large-Scale Language Models (LLMs). The interface’s usability, local storage performance, and the consistency of AI-assisted reasoning were evaluated. Results The developed platform enabled the seamless and instantaneous management of independent patient tabs (unique storage keys mitochondria_001 to 010), achieving local read\u002Fwrite latency without reliance on external servers or transmission of sensitive personal data. The prompt framework proved effective in guiding LLMs in translating mitochondrial biomarkers and clinical data into structured summaries of oxidative stress status and metabolic risk. The lack of a trained computer vision model for automatic segmentation is declared as the main limitation of the current study. Conclusion The software prototype and prompt interface provide a low-cost, secure (data privacy-oriented), and clinically intuitive solution for translational data management in obesity. This work demonstrates the feasibility of integrating heterogeneous biomedical parameters using conversational AI tools and local storage, laying the groundwork for the future incorporation of image segmentation algorithms.","引言 肥胖因其与心血管和代谢风险的密切关联，已成为全球性的重大健康挑战。在分子层面，脂毒性环境会改变线粒体网络的结构、动力学及连接性。尽管细胞生物学和数字健康领域取得了进展，但目前仍缺乏能够以标准化方式将临床记录与分子及显微图像数据整合的轻量级计算工具。本研究介绍了一种解耦软件原型和交互式提示工程框架的设计、开发及可行性评估，旨在整合分子肥胖研究中的临床与组织学线粒体数据。方法 采用HTML5、CSS3和IndexedDB API设计了客户端软件架构，用于医学记录（采用ICD-10分类编码）和人类胎盘组织显微图像（通过透射电子显微镜、免疫荧光和免疫组织化学获得，用于评估线粒体融合分子标志物如Mitofusin-2、硝基酪氨酸）的事务性匿名持久化存储。在解释性处理方面，构建了基于思维链（Chain-of-Thought）范式结构的提示框架，旨在与大语言模型（LLMs）进行交互。对界面的可用性、本地存储性能以及AI辅助推理的一致性进行了评估。结果 所开发的平台实现了独立患者标签页（唯一存储键mitochondria_001至010）的无缝即时管理，在无需依赖外部服务器或传输敏感个人数据的情况下，实现了本地读写延迟。提示框架在引导LLMs将线粒体生物标志物和临床数据转化为氧化应激状态和代谢风险的结构化摘要方面被证明有效。缺乏用于自动分割的经过训练的计算机视觉模型被声明为本研究的主要局限性。结论 该软件原型和提示界面为肥胖领域的转化数据管理提供了一种低成本、安全（以数据隐私为导向）且临床直观的解决方案。本研究证明了使用对话式AI工具和本地存储整合异构生物医学参数的可行性，为未来纳入图像分割算法奠定了基础。",null,"Frontiers in Artificial Intelligence","2026-09-10T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦肥胖临床与线粒体数据整合的软件原型，属医学信息学领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"农业人工智能","数字健康","数据隐私","10.3389\u002Ffrai.2026.1923426",{"doi":24,"openalex_id":26,"authors":27,"venue":10,"cited_by_count":15,"oa_url":33,"card":34,"direction":38,"ingested_from":40},"W7212155685",[28,31],{"name":29,"orcid":30},"Karenth Milena Rodríguez-Córdoba","https:\u002F\u002Forcid.org\u002F0000-0003-4010-2813",{"name":32,"orcid":9},"William Darío Ávila-Díaz","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1923426\u002Fpdf",{"tldr":35,"method":36,"finding":37,"direction":38,"opportunity":39},"开发轻量本地软件原型与LLM提示框架，整合肥胖临床与线粒体数据。","HTML5\u002FIndexedDB本地存储临床与电镜图像，结合思维链提示调用大语言模","实现无服务器、隐私安全的异构数据管理，提示框架可有效生成氧化应激与代谢风险摘要。","农业人工智能与决策模型","可迁移至农业场景，构建本地化多模态数据管理与LLM辅助决策工具，并补足自动图像分割能力。","openalex","2026-09-13T23:30:41.079545Z"]