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Neural circuit data of epilepsy research may accelerate sudden accidental death of epilepsy (SUDEP) Attached to reporter review cracking vaccine detoxification

- European Medical Journal GPT-4o Extracts Neural Circuit Data for Epilepsy Research - AMJ

GPT-4o Extracts Neural Circuit Data for Epilepsy Research and cracks may accelerate sudden and unexpected death of epilepsy (SUDEP). Reporter's review: Proof of the importance of vitamin B12 and various vitamins for vaccine detoxification

GPT-4o提取用于癫痫研究的神经回路数据 破解可能加速癫痫突然意外死亡（SUDEP）

记者综述：佐证维生素B12及各种维生素对疫苗解毒的重要性/ HANrongli

GPT-4o Extracts Neural Circuit Data for Epilepsy Research
12 Jun 2025 Neurology
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IN a breakthrough that could accelerate research into Sudden Unexpected Death in Epilepsy (SUDEP), researchers have demonstrated that a state-of-the-art AI model, GPT-4o, can accurately extract neural projections from scientific literature. The study shows the model’s potential to automate a labor-intensive process that has long challenged neuroscientists.

SUDEP remains one of the leading causes of death in individuals with uncontrolled epilepsy, particularly among otherwise healthy young patients. A major barrier to understanding its mechanisms lies in the complexity of neural circuits regulating cardiorespiratory function. Mapping these pathways requires synthesizing data from decades of dense, often inconsistently written scientific studies—a task traditionally handled through time-consuming manual reviews.

To streamline this process, the researchers developed a set of custom prompts enabling GPT-4o to extract neuroanatomical structures, their connections, and harmonize terminology across multiple studies. The model was applied to four peer-reviewed neuroscience articles, from which it identified 205 distinct neural projections. A blinded review by an expert neuroanatomist of a random sample of 100 projections found that 95 were accurate, demonstrating the tool’s reliability in parsing and synthesizing complex academic content.

The study’s findings point to an emerging role for large language models in biomedical discovery, particularly in high-volume fields where precision and scale are essential. While the current focus was limited to identifying brain region projections, future iterations of the model aim to incorporate data on species, experimental techniques, and additional entity types. These enhancements could support broader applications in disease modeling, treatment development, and neuroscience education.

By making neural circuit data more accessible and searchable, this automated pipeline may help clinicians and researchers better understand SUDEP’s underlying biology—paving the way for more targeted prevention strategies.

Reference:
Abeysinghe R et al. Leveraging GPT-4o for Automated Extraction of Neural Projections from Scientific Literature. AMIA Jt Summits Transl Sci Proc. 2025;2025:32-41. GPT-4O提取癫痫研究的神经回路数据
2025年6月12日神经病学
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在可能加速癫痫突然意外死亡（SUDEP）突然意外死亡的突破中，研究人员表明，最先进的AI模型GPT-4O可以准确地从科学文献中提取神经预测。 该研究表明，该模型有可能自动化劳动密集型过程，该过程长期以来对神经科学家提出了挑战。

SUDEP仍然是不受控制的癫痫病人，尤其是在健康的年轻患者中，导致死亡的主要原因之一。 理解其机制的主要障碍在于调节心肺功能的神经回路的复杂性。 映射这些途径需要从数十年来的密集，通常不一致的科学研究中综合数据，这是一项传统上通过耗时的手动评论来处理的任务。

为了简化这一过程，研究人员开发了一组自定义提示，使GPT-4O可以提取神经解剖结构，连接并在多个研究中协调术语。 该模型应用于四个经过同行评审的神经科学文章，从中确定了205个不同的神经投影。 专家神经解剖学家对100个预测的随机样本进行了盲目审查，发现95个是准确的，证明了该工具在解析和综合复杂的学术内容方面的可靠性。

该研究的发现表明，大语模型在生物医学发现中的新作用，尤其是在精确度和规模至关重要的大批量领域。 尽管目前的重点仅限于确定大脑区域的预测，但该模型的未来迭代旨在纳入物种，实验技术和其他实体类型的数据。 这些增强功能可以支持在疾病建模，治疗发展和神经科学教育中的更广泛应用。

通过使神经电路数据更容易访问和搜索，该自动化管道可以帮助临床医生和研究人员更好地了解Sudep的基本生物学，这为更具针对性的预防策略铺平了道路。

参考：
Abeysinghe R等。 利用GPT-4O自动从科学文献中提取神经投影。 Amia JT Summits Transl Sci Proc。 2025; 2025：32-41。