Nuclear count data.

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration.真菌生长量化是有效治疗重症真菌感染的核心基础。现有方法通过时序过程中的特定形态学数据（如菌丝长度数据）量化真菌生长速率。然而，此类数据的自动化大规模采集，远超多数临床微生物实验室的工作范畴。本文提出一种真菌生长数学模型，能够通过易于采集的间接测量指标——烟曲霉（Aspergillus fumigatus，丝状真菌）生长的600nm光密度（OD600）数据——来估算特定形态学的真菌生长速率。本方法将间接OD600测量值与模型中用于校准的真实真菌生长状态之间的关联明确纳入考量，以此适配OD600作为间接测量指标的特性。因此，该方法无需全新构建校准数据。本模型在拟合与预测OD600生长曲线方面的表现优于参考模型，同时解决了未纳入校准环节的参考模型中，从OD600数据推断出的特定形态学生长速率与直接测量数据之间存在的观测差异。