论文标题
将声学生物打印与AI辅助拉曼光谱结合起来，以高通量鉴定血液中的细菌
Combining acoustic bioprinting with AI-assisted Raman spectroscopy for high-throughput identification of bacteria in blood
论文作者
论文摘要
鉴定血液，尿液和废水等复杂样品中的病原体对于检测感染并为最佳治疗提供了关键。表面增强的拉曼光谱法（SER）和机器学习（ML）可以区分多种病原体，但是处理复杂的流体样品以灵敏和专门检测病原体仍然是一项重大挑战。在这里，我们开发了一种声学生物生产者，将样品数字化成数百万滴，每个液滴仅包含几个细胞，这些细胞用SERS和ML识别。我们证明了来自含有表皮链球菌，大肠杆菌和血液的溶液的2个PL液滴的快速打印。当与金纳米棒（GNR）混合时，可实现高达1500倍的SERS增强。然后，我们训练ML模型，并从细胞上的样品中实现> = 99％的分类精度，并且从细胞混合样品中获得了> = 87％的精度。我们还从病原体的液滴中获得> = 90％的精度：血细胞比<1。我们合并的生物打印和SERS平台可以在临床，环境和工业环境中加速快速，敏感的病原体检测。
Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when mixed with gold nanorods (GNRs), SERS enhancements of up to 1500x are achieved.We then train a ML model and achieve >=99% classification accuracy from cellularly-pure samples, and >=87% accuracy from cellularly-mixed samples. We also obtain >=90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.
