Evaluation of the impact of L-Tryptophan on the toxicology of Perfluorooctanoic acid in Daphnia magna: Characterization and perspectives
Bibliographic record
Abstract
Perfluorooctanoic acid (PFOA) is a pervasive environmental contaminant with well-documented toxic effects on both humans and animals, attracting significant scientific concern. Due to its affinity for proteins, research has predominantly focused on PFOA's interactions with biological macromolecules. However, the specific role of smaller molecules, such as amino acids, remains underexplored. This study uniquely evaluates the potential of l-tryptophan (L-Trp) to mitigate PFOA toxicity and investigates the interaction mechanisms involved. Results indicate that the presence of L-Trp in PFOA-contaminated water reduces acute toxicity in Daphnia magna, with an optimal molar ratio of approximately 1:2 (Trp:PFOA). The findings reveal that non-covalent interactions, particularly van der Waals forces and hydrogen bonds, are central to the Trp-PFOA complex formation. Additional contributions from hydrophobic interactions at the indole group and electrostatic forces between carbonyl and amine groups further stabilize the complex. These interactions likely reduce PFOA's toxicity by altering its bioavailability and distribution. While this study offers valuable insights into the binding mechanisms between L-Trp and PFOA, it raises important questions about the reversibility of this interaction and its applicability to other per- and polyfluoroalkyl substances (PFASs).
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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".