Inflammation of Gill Epithelia in Fish Causes Increased Permeation of Petrogenic Polar Organic Chemicals via Disruption of Tight Junctions
Bibliographic record
Abstract
The epithelial cell layer that lines the gills of fish controls paracellular permeation of chemicals through tight junctions. The integrity of tight junctions can be affected by inflammation, which likely affects the bioavailability of chemicals. Here, the inflammation of the rainbow trout gill cell line RTgill-W1 was induced via exposure to bacterial lipopolysaccharides (LPS). Cells were then coexposed to extracts of oil sands process-affected water (OSPW), which contain complex mixtures of chemicals. After 24 h of exposure, cells exposed to LPS showed a reduction in transepithelial electrical resistance, an indicator of tight junction integrity. Quantitative reverse-transcription polymerase chain reaction (RT-PCR) analysis determined that abundances of transcripts of genes coding for tight junction proteins were significantly less in cells exposed to 20, 50, or 100 mg L –1 LPS. Chemical analysis revealed increased permeation of constituents of OSPW across epithelia at all studied LPS concentrations. These in vitro findings were confirmed in vivo in rainbow trout exposed to LPS and OSPW for 48 h, which resulted in greater accumulation of chemicals relative to that for fish exposed to OSPW alone. Our results demonstrated that inflammation and disruption of tight junctions could lead to greater uptake of potentially harmful chemicals from the environment, which has implications for mixture risk assessment.
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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) | 0.001 | 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".