发表机构
Zhejiang University School of Medicine; Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang University; Guizhou University; Fourth Affiliated Hospital of Zhejiang University School of Medicine; Liangzhu Laboratory（浙江大学医学院; 浙江大学医学院附属第二医院; 浙江大学; 贵州大学; 浙江大学医学院附属第四医院; 良渚实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对神经毒性评估难题，引入专用自监督视觉模型及多粒度共聚焦基准，提出尺度自适应掩码图像建模策略，在多任务中超越基础模型，融合特征可预测行为缺陷，筛选化学品发现新神经毒性决定因素，为评估和药物发现提供新方法。
AI 中文摘要
神经疾病是全球残疾的主要原因，且与环境化学暴露关联日益紧密。目前神经毒性评估依赖主观的人工评分形态学读数，预测行为结果能力差。秀丽隐杆线虫提供了遗传易处理、符合3R原则的替代方案，但大规模从共聚焦显微镜量化神经元表型仍具计算挑战。现有的视觉基础模型无法解决神经元成像的稀疏信号和多尺度损伤。本文引入针对秀丽隐杆线虫多巴胺能神经元的专用自监督视觉模型及27117张注释图像的多粒度共聚焦基准CeNeuMorph。提出尺度自适应掩码图像建模策略，有效解决了全谱神经退行性病变。该模型在分类、分割和检测任务中超越了通用和生物医学基础模型，融合视觉特征与形态描述符可预测多巴胺依赖的行为缺陷，筛选180种农用化学品发现苯并咪唑部分是多巴胺能神经毒性的新决定因素。工作展示了尺度自适应自监督学习如何将形态与功能联系起来，为神经毒性评估和药物发现提供了可扩展替代哺乳动物体内模型的方法。
英文摘要
Neurological disorders are a leading cause of global disability and are increasingly linked to environmental chemical exposures. Yet neurotoxicity assessment still relies on hand-scored morphological readouts that are subjective and poorly predictive of behavioral outcomes. Caenorhabditis elegans provides a genetically tractable, 3R-compliant alternative, but quantifying neuronal phenotypes from confocal microscopy at scale remains computationally challenging: existing vision foundation models, trained on natural or radiological images, cannot resolve the sparse signals and multi-scale lesions of neuronal imaging. Here, we introduce a dedicated self-supervised vision model for C. elegans dopaminergic neurons, together with CeNeuMorph, a multi-grained confocal benchmark of 27,117 annotated images. Specifically, moving beyond standard Masked Autoencoders, we propose a scale-adaptive masked image modeling strategy that jointly learns representations across resolutions and patch sizes under a fixed token budget. By decoupling structural semantic learning from rigid grid constraints, the model effectively resolves the full spectrum of neurodegenerative lesions - ranging from fine dendritic beading to gross soma shrinkage - within a tractable computational framework. Finally, our model surpasses both generalist and biomedical foundation models across classification, segmentation and detection tasks. Fusing visual features with morphological descriptors enables prediction of dopamine-dependent behavioral deficits ($R^2=0.498$). Screening 180 agrochemicals, we identify the benzimidazole moiety as a previously unrecognized determinant of dopaminergic neurotoxicity. Together, the work demonstrates how scale-adaptive self-supervised learning can connect morphology to function for a scalable alternative to mammalian in vivo models for neurotoxicity assessment and drug discovery.
