Ciprofloxacin (CIP) is a pseudopersistent antibiotic detected in freshwater worldwide. As an ionizable chemical, its fate in freshwater is influenced by water chemistry factors such as pH, hardness, and dissolved organic carbon (DOC) content. We investigated the effect of pH, DOC, and Ca levels on the toxicity of CIP to Microcystis aeruginosa and developed a bioavailability model on the basis of these experimental results. We found that the zwitterion (CIP ) is the most bioavailable species of CIP to M. aeruginosa, whereas DOC is the most dominant factor reducing CIP toxicity, possibly via binding of both CIP and CIP to DOC. pH likely also regulates CIP-DOC binding indirectly through its influence on CIP speciation. In addition, higher tolerance to CIP by M. aeruginosa was observed at pH < 7.2, but the underlying mechanism is yet unclear. Calcium was identified as an insignificant factor in CIP bioavailability. When parameterized with the data obtained from toxicity experiments, our bioavailability model is able to provide accurate predictions of CIP toxicity because the observed and predicted total median effective concentrations deviated by <28% from each other. Our model predicts that changes in pH and DOC conditions can affect CIP toxicity by up to 10-fold, suggesting that CIP in many natural environments is likely less toxic than in standard laboratory toxicity experiments. Environ Toxicol Chem 2022;41:2835-2847. © 2022 SETAC.
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http://dx.doi.org/10.1002/etc.5454 | DOI Listing |
Syst Appl Microbiol
January 2025
Microbial Resource Research Infrastructure - European Research Infrastructure Consortium (MIRRI-ERIC), Braga, Portugal.
NPJ Antimicrob Resist
March 2024
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Comprehensive knowledge of mechanisms driving the acquisition of antimicrobial resistance is essential for the development of new drugs with minimized resistibility. To gain this knowledge, we combine experimental evolution in a continuous culturing device, the morbidostat, with whole genome sequencing of evolving cultures followed by characterization of drug-resistant isolates. Here, this approach was used to assess evolutionary dynamics of resistance acquisition against DNA gyrase/topoisomerase TriBE inhibitor GP6 in Escherichia coli and Acinetobacter baumannii.
View Article and Find Full Text PDFCarbohydr Polym
March 2025
Engineering Research Center for Eco-Dyeing and Finishing of Textiles, Key Laboratory of Advanced Textile Materials and Manufacturing Technology, Ministry of Education, College of Textile Science and Engineering (International Institute of Silk), Zhejiang Sci-Tech University, Hangzhou 310018, China. Electronic address:
Although there have been sporadic reports that the crystallinity of cellulose has a significant impact on photoluminescence (PL) properties, the degree and pattern of this effect have not been thoroughly explored and elucidated. Here, we assume that crystallinity is positively correlated with PL emission. Then, lyocell fiber (CLY), a common man-made cellulose fiber, is selected to solve the above problems by exploring the PL emission properties of different crystallinity systems.
View Article and Find Full Text PDFInorg Chem
January 2025
Department of Environmental Science and Engineering, Fuzhou University, Minhou, Fujian 350108, China.
Environmental concerns are driving the development of eco-friendly and effective methods for contaminant monitoring and remediation. In this study, a lanthanide porphyrin-based MOF with dual fluorescence sensing and photocatalytic properties was synthesized and applied for the detection and combined removal of Cr(VI) and ciprofloxacin (CIP). Using different excitation wavelengths, the material exhibited selective detection of Cr(VI) via fluorescence quenching and CIP through fluorescence enhancement.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biostatistics, Division of Health Sciences, ICMR-National Institute of Occupational Health, Ahmedabad, Gujarat, 380016, India.
Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care and reduce the burden on healthcare facilities. This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024.
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