Effects of selected pharmaceuticals on riverine biofilm communities
Bibliographic record
Abstract
Although pharmaceutical and therapeutic products are widely found in the natural environment, there is limited understanding of their ecological effects. Here we used rotating annular bioreactors to assess the impact of 10 microg.L(-1) of the selected pharmaceuticals ibuprofen, carbamazepine, furosemide, and caffeine on riverine biofilms. After 8 weeks of development, community structure was assessed using in situ microscopic analyses, fluor-conjugated lectin binding, standard plate counts, fluorescent in situ hybridization, carbon utilization spectra, and stable carbon isotope analyses. The biofilm communities varied markedly in architecture although only caffeine treated biofilms were significantly thicker. Cyanobacteria were suppressed by all 4 compounds, whereas the nitrogen containing caffeine, furosemide, and carbamazepine increased algal biomass. Ibuprofen and carbamazepine reduced bacterial biomass, while caffeine and furosemide increased it. Exopolymer content and composition of the biofilms was also influenced. Significant positive and negative effects were observed in carbon utilization spectra. In situ hybridization analyses indicated all treatments significantly decreased the gamma-proteobacterial populations and increased beta-proteobacteria. Ibuprofen in particular increased the alpha-proteobacteria, beta-proteobacteria, cytophaga-flavobacteria, and SRB385 probe positive populations. Caffeine and carbamazepine additions resulted in significant increases in the high GC354c and low GC69a probe positive cells. Live-dead analyses of the biofilms indicated that all treatments influenced the ratio of live-to-dead cells with controls having a ratio of 2.4, carbamazepine and ibuprofen being 3.2 and 3.5, respectively, and furosemide and caffeine being 1.9 and 1.7, respectively. Stable isotope analyses of the biofilms indicated delta 13C values shifted to more negative values relative to control biofilms. This shift may be consistent with proportional loss of cyanobacteria and relative increase in algal biomass rather than incorporation of pharmaceutical carbon into microbial biofilm. Thus, at 10 microg.L(-1) levels pharmaceuticals exhibit both nutrient-like and toxic effects on riverine microbial communities.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".