ArticleComputers & chemical engineering2022
Discerning in vitro pharmacodynamics from OD measurements: A model-based approach.
Article in Computers & chemical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 13 citations in OpenAlex.
- In Vitro Modulatory Effect of Stimulant Beverages on the Growth Kinetics of Intestinal Bacteria Associated with Colorectal Cancer.Foods (Basel, Switzerland) · 2026Article
- Using Machine Learning to Design Effective Antimicrobial Dosing Regimens.Computers & chemical engineering · 2026Article
- Impact of iron limitation on in vitro and in vivo activity of ceftazidime against A. baumannii.Diagnostic microbiology and infectious disease · 2025Article
- In vitro phytochemical, antioxidant activity and antimycobacterial potentials of selected medicinal plants commonly used for respiratory infections and related symptoms in the Limpopo Province, South Africa.BMC complementary medicine and therapies · 2025Article
- Antibacterial and antibiofilm activities of kaffir lime essential oils and their active constituents againstHeliyon · 2025Article
- Rapid Design of Combination Antimicrobial Therapy againstComputers & chemical engineering · 2025Article
- Rapid In Vitro Assessment of Antimicrobial Drug Effect Bridging Clinically Relevant Pharmacokinetics: A Comprehensive Methodology.Pharmaceutics · 2023Article
- Ecotoxicity ofToxics · 2023Article
- Growth of Acinetobacter baumannii impacted by iron chelation.Letters in applied microbiology · 2023Article
- Deciphering longitudinal optical-density measurements to guide clinical dosing regimen design: A model-based approach.Computer methods and programs in biomedicine · 2022Article
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 at 1 institution in 1 country.
Funding
Abstract
Time-kill experiments can discern the pharmacodynamics of infectious bacteria exposed to antibiotics in vitro, and thus help guide the design of effective therapies for challenging clinical infections. This task is resource-limited, therefore typically bypassed in favor of empirical shortcuts. The resource limitation could be addressed by continuously assessing the size of a bacterial population under antibiotic exposure using optical density measurements. However, such measurements count both live and dead cells and are therefore unsuitable for declining populations of live cells. To fill this void, we develop here a model-based method that infers the count of live cells in a bacterial population exposed to antibiotics from continuous optical-density measurements of both live and dead cells combined. The method makes no assumptions about the underlying mechanisms that confer resistance and is widely applicable. Use of the method is demonstrated by an experimental study on
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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.