ArticlePLoS computational biology2024
Inferring fungal growth rates from optical density data.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Isolation and characterization of a potential heterotrophic nitrifying-aerobic denitrifying and phosphorus-accumulating bacteriumWater science and technology : a journal of the International Association on Water Pollution Research · 2026Article
- How temperature reshapes interactions in mixed cultures of mycotoxigenic fungi.Frontiers in microbiology · 2026Article
- Tempeh-type fermentation kinetics of tarwi (Frontiers in nutrition · 2026Article
- Modelling the Combined Effects of Oxalic Acid, Water Activity, and pH on the Growth and Mycotoxin Production ofFoods (Basel, Switzerland) · 2025Article
- Ecophysiological behavior of majorApplied and environmental microbiology · 2025Article
- A novel pan-fungal screening platform for antifungal drug discovery: proof of principle study.Antimicrobial agents and chemotherapy · 2025Article
- Development and optimization of large-scale approaches to identify iron-related genes inFrontiers in microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.