标题： 泛感染基础框架实现多种病原体预测 显示英文标题

标题： Pan-infection Foundation Framework Enables Multiple Pathogen Prediction

摘要： 基于宿主反应的诊断可以提高诊断细菌和病毒感染的准确性，从而减少不当的抗生素处方。 然而，现有样本量有限且感染类型粗略的队列无法支持准确且可推广的诊断模型的探索。 在此，我们整理了最大的感染宿主反应转录组数据，包括来自13个国家和21个平台的89个血液转录组数据集中的11,247个样本。 我们构建了一个病原体预测诊断模型，以泛感染模型为基础（AUC = 0.97）基于泛感染数据集。 然后，我们利用知识蒸馏，将该“教师”模型的见解高效地转移到四个轻量级病原体“学生”模型中，即金黄色葡萄球菌感染（AUC = 0.99）、链球菌感染（AUC = 0.94）、HIV感染（AUC = 0.93）和RSV感染（AUC = 0.94），以及一个败血症“学生”模型（AUC = 0.99）。 提出的知识蒸馏框架不仅促进了使用泛感染数据进行病原体诊断，还实现了从泛感染到败血症的跨疾病研究。 此外，该框架实现了诊断模型的高度轻量化设计，预计可在临床环境中自适应部署。 显示英文摘要

摘要： Host-response-based diagnostics can improve the accuracy of diagnosing bacterial and viral infections, thereby reducing inappropriate antibiotic prescriptions. However, the existing cohorts with limited sample size and coarse infections types are unable to support the exploration of an accurate and generalizable diagnostic model. Here, we curate the largest infection host-response transcriptome data, including 11,247 samples across 89 blood transcriptome datasets from 13 countries and 21 platforms. We build a diagnostic model for pathogen prediction starting from a pan-infection model as foundation (AUC = 0.97) based on the pan-infection dataset. Then, we utilize knowledge distillation to efficiently transfer the insights from this "teacher" model to four lightweight pathogen "student" models, i.e., staphylococcal infection (AUC = 0.99), streptococcal infection (AUC = 0.94), HIV infection (AUC = 0.93), and RSV infection (AUC = 0.94), as well as a sepsis "student" model (AUC = 0.99). The proposed knowledge distillation framework not only facilitates the diagnosis of pathogens using pan-infection data, but also enables an across-disease study from pan-infection to sepsis. Moreover, the framework enables high-degree lightweight design of diagnostic models, which is expected to be adaptively deployed in clinical settings.