The Effects of Tetracycline and Ibuprofen on Common Duckweed, Lemna minor L
Bibliographic record
Abstract
tudies are showing that waste water is contaminated with various types of chemicals, such as pesticides, flame retardants, and pharmaceuticals. The contamination of water with pharmaceuticals is of growing concern, as water treatment plants are not able to fully remove these chemicals from drinking water sources, and their effects on aquatic organisms and humans are unknown. This study looks at how water contaminated with tetracycline and ibuprofen affect the growth of common duckweed (Lemna minor L). Plants were placed in test tubes, treated with solutions of the medications, and growth was noted by the number of plants in each test tube after a five week period. Tetracycline was found to have no effect on plant growth, while ibuprofen had negative effects. Further testing may need to be conducted to find why this occurred. INTRODUCTION An increasing number of anthropogenically generated chemicals are being found in waste water and sludge (Herberer 2002). Studies have tested the quality of treated waste water released into streams, and have found that they still contain small amounts of chemicals. These chemicals cannot be fully removed during a normal filtration process, as most water treatment plants are not designed to remove persistent chemicals (Vergili 2013). There is concern over the growing amount of pharmaceuticals found in treated waste water (Jones et al. 2005). Lipid regulators, anti-epileptic, animal growth hormone, and psychotic drugs have all tested positive in samples of treated waste water. Usually the treated waste water is either released into nearby surface waters or used for human consumption (Houtman et al. 2013). However, the effects that pharmaceuticals in drinking water have on humans and aquatic organisms are unknown (Stackelberg et al. 2004). This experiment used common duckweed (Lemna minor L., Lemnaceae), an aquatic plant that is distributed worldwide, as an indicator species to explore how tetracycline and ibuprofen affect aquatic species. The objective of this experiment was to examine how levels of tetracycline and ibuprofen similar to those found in drinking water affect duckweed growth. METHODS Tetracycline (Mars Fishcare North America, Inc., USA) and ibuprofen (Pfizer Inc., Kings Mountain, NC0) were the pharmaceuticals used in the treatments for this experiment, and the experiment was conducted over a period of five weeks. On the first day, 48 test tubes were filled with 55ml of water from a fish tank and three randomly selected duckweed plants were placed into each test tube. Water from a fish tank was chosen to replicate pond water. Test tubes were divided evenly into a control, tetracycline, and ibuprofen group. Concentrations of 50μg/ml of tetracycline and ibuprofen were created to simulate amounts found in drinking water in their respective category. The plants were then placed on a window sill for the duration of the experiment. Plants were treated twice with the chemical solutions and were recounted per replicate after five weeks. Mann-Whitney rank tests were used to compare counts of the chemical treatments to the control as assumptions of parametric testing could not be met (Zar 1984). S 1 Cole: The Effects of Tetracycline and Ibuprofen on Common Duckweed Published by DigitalCommons@COD, 2014 41 RESULTS AND DISCUSSION Counts of duckweed did not vary significantly between the control and tetracycline treatment, but did between the control and ibuprofen treatment (Table 1). Significance was determined at P<0.05. Overall, plants treated with tetracycline had growth similar to the control, with no decay in plant numbers. Ibuprofen treated plants exhibited either a decay or showed no growth in all but two test tubes. Therefore, neutral effects could not be safely rejected. There are several reasons why these results could have occurred. First, ibuprofen is composed of various chemicals, including cyclooxygenase inhibitors (Rainsford 2012). Cyclooxygenase is an enzyme that speeds up the production of chemical messengers called prostaglandins. Thus, an inhibitor of cyclooxygenase should slow the production of prostaglandins (Groenewald et al. 1997). Studies have found prostaglandins to have roles in flowering, photosynthesis, and regulating of cell membrane permeability. The cyclooxygenase inhibitors present in ibuprofen could have affected the duckweed’s ability for sunlight absorption or nutrient absorption, resulting in the overall decline in plant numbers. The negative effect of ibuprofen on duckweed does imply that if waters that supply to irrigation fields get contaminated, it may decrease crop yields. Also, ibuprofen is an enantiomer, which means that its isomers differ in shape as a result of an asymmetric carbon. This causes only one of the two isomers to be biologically active because only molecules of that form can bind to molecules of that organism. Ibuprofen is normally sold as a mixture of its two enantiomers, both the effective and less effective (Reece et al. 2011). This could have also influenced the duckweed growth, as some test tubes may have had more ineffective enantiomer than effective, which could account for replicate test tubes that showed neither growth nor decay. However, the exact nature of ibuprofen’s negative effect on duckweed remained unknown and needs to be further explored through continued studies. Finally, ibuprofen has been shown to cause gastritis in children (Young 1995), impairment of color vision and visual acuity in adults (Grant 1986), and premature closure of ductus arteriosus if taken after 34-35 weeks of pregnancy (Young 1995). Because this study indicates that ibuprofen in drinking water has negative effects on organisms, further studies investigating its effects on humans is necessary. LITERATURE CITED Grant, W.M. 1986. Toxicology of the Eye, 3 ed. Charles C. Thomas, Springfield, IL, USA. Groenewald, E.G. and A.J. Van Der Westhuizen. 1997. Prostaglandins and related substances in plants. The Botanical Review 63: 199-220. Herberer, T. 2002. Tracking persistent pharmaceutical residues from municipal sewage to drinking water. Journal of Hydrology 266: 175-189. Houtman, A., S. Karr, and J. Interlandi. 2013. Environmental Science. W.H. Freeman and Company, New York, NY, USA. Jones, O.A., J. Lester, and N. Voulvoulis. 2005. Pharmaceuticals: a threat to drinking water. Trends in Biotechnology 23: 163-167. Rainsford, K.D. 2013. Ibuprofen: from invention to an OTC therapeutic mainstay. International Journal of Clinical Practice 67: 9-20. Reece, J., L. Urry, M. Cain, S. Wasserman, P. Minorsky, and R. Jackson. 2011. Campbell Biology, 9 ed. Pearson Benjamin Cummings, San Francisco, CA, USA. 2 ESSAI, Vol. 12 [2014], Art. 13 http://dc.cod.edu/essai/vol12/iss1/13 42 Stackelberg, P., E. Furlong, M. Meyer, S. Zaugg, A. Henderson, and D. Reissman. 2004. Persistence of pharmaceutical compounds and other organic wastewater contaminants in a conventional drinking water treatment plant. Science of Total Environment 329: 199-113. Vergili, I. 2013. Application of nanofiltration for the removal of carbamazepine, diclofenac, and ibuprofen from drinking water sources. Journal of Environmental Management 127: 177-187. Young, L.Y. and M.A. Koda-Kimble. 1995. Applied Therapeutics: the Clinical Use of Drugs, 6 ed. Applied Therapeutics, Inc., Vancouver, WA, USA. Zar, J.H. 1984. Biostatistical Analysis, 2 ed. Prentice-Hall, Inc., Englewood Cliffs, NJ, USA. 3 Cole: The Effects of Tetracycline and Ibuprofen on Common Duckweed Published by DigitalCommons@COD, 2014 43 Table 1. Summary (mean + standard error; all n=16) of average plant counts per treatment. Also provided are Mann-Whitney statistics and probability values from comparisons of the control to the chemical treatment. Significance was determined at P<0.05. ______________________________________________________________ Treatment Mean + standard error U P ______________________________________________________________ Control 3.875 + 0.256 Tetracycline 4.063 + 0.266 119 0.734 Ibuprofen 2.500 + 0.398 46 <0.002 ______________________________________________________________ 4 ESSAI, Vol. 12 [2014], Art. 13 http://dc.cod.edu/essai/vol12/iss1/13
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".