Occurrence and tissue distribution of per- and polyfluoroalkyl substances (PFAS) in fishes from waterbodies with point and non-point sources in Massachusetts, USA
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with known bioaccumulative and toxic effects in aquatic ecosystems. This study assessed site-specific differences in PFAS contamination in fish from Ashumet Pond, Sudbury River, and Great Herring Pond (reference site) in Massachusetts. Fish from Ashumet Pond exhibited the highest PFAS concentrations, particularly perfluorooctane sulfonate (PFOS), which exceeded levels in plasma almost 650 times those at the reference site. Principal component analysis identified distinct PFAS profiles at each site, reflecting localized contamination sources. Temporal analysis at Ashumet Pond revealed a substantial increase in plasma PFOS and perfluorodecanoic acid (PFDA) from 2020 to 2022. Tissue distribution analyses showed the highest PFAS concentrations in plasma, followed by liver and muscle, consistent with PFAS binding affinity for blood proteins. Species-specific differences in PFAS bioaccumulation were observed, with largemouth bass (Micropterus nigricans) exhibiting higher body burdens than banded killifish (Diaphanus fundulus), likely due to trophic position and dietary exposure. Histopathological assessments and gene transcript analyses revealed associations between PFAS exposure and inflammatory responses, oxidative stress, endocrine disruption, and immune-related pathways, with the most pronounced molecular effects observed at the downstream site of the Sudbury River. This study underscores the importance of understanding site-specific contamination sources, exposure pathways, and biological effects of PFAS in fish. These findings would benefit from additional research on sediment contamination, temporal analyses at each site, trophic transfer, and transcriptomic analyses across multiple organs to further elucidate PFAS toxicity mechanisms and guide remediation efforts.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".