Insights into the Toxicity of Triclosan to Green Microalga <i>Chlorococcum sp.</i> Using Synchrotron-Based Fourier Transform Infrared Spectromicroscopy: Biophysiological Analyses and Roles of Environmental Factors
Bibliographic record
Abstract
This study investigated the toxicity of triclosan to the green microalga Chlorococcum sp. under multiple environmental stressors. The interactions between triclosan and environmental stressors were explored through full two-way factorial, synchrotron-based Fourier transform infrared spectromicroscopy and principal component analyses. Phosphorus concentration, pH * phosphorus concentration, and temperature * pH * NaCl concentration were the most statistically significant factors under triclosan exposure. The variation of those factors would have a huge impact on biophysiological performances. It is interesting to find Chlorococcum sp. may become more resistant against triclosan in phosphorus-enriched environment. Besides, particular significant factors from multiple environmental stressors showed the impacts of triclosan on the corresponding response of Chlorococcum sp. owing to the specific structure and performance of biomolecular components. Moreover, two high-order interactions of temperature * pH * NaCl concentration and temperature * pH * NaCl concentration * phosphorus concentration had more contributions than others at the subcellular level, which could be attributed to the interactive complexity of biomolecular components. Due to cellular self-regulation mechanism and short exposure time, the biophysiological changes of Chlorococcum sp. were undramatic. These findings can help reveal the interactive complexity among triclosan and multiple environmental stressors. It is suggested that multiple environmental stressors should be considered during ecological risk assessment and management of emerging pollutants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".