Microplastics serve as a potential vector for the transfer of naphthalene from freshwater to the human gastrointestinal system
Bibliographic record
Abstract
Ubiquitous microplastics (MPs) may act as potential carriers of pollutants by transferring them from aquatic environments to humans, thus posing risks to human health. However, little is known about the vector effects of MPs, which refers to their ability to sorb contaminants from the environment and to release them in the body of a sensitive receptor, (e.g., humans or other biota), after intake (e.g. by ingestion or inhalation). Therefore, this transfer of contaminants can constitute a risk to humans and the environments and must be quantified. Vector effects were investigated for three types of MPs, i.e., medium-density polyethylene (MDPE), polypropylene (PP), and polystyrene (PS) to transfer naphthalene (model contaminant) in freshwater. Firstly, kinetic and isotherm studies characterized the sorption behaviour of naphthalene into MPs. Then, a physiologically based extraction test (PBET) was conducted on naphthalene-loaded MPs to determine gastric and intestinal bioaccessibility (i.e., an estimate of solubility for an ingested dose of a contaminant). Results suggest that sorption kinetics follow a pseudo-second order model for all tested MPs. External mass transport is the rate-controlling step for naphthalene sorption on MPs. Furthermore, isotherm results indicate that Langmuir and linear models provide the best fit for this sorption, which is characterized by a hydrophobic interaction mechanism. The sorption capacities of naphthalene on MPs followed the decreasing order of MDPE > PP > PS. The bioaccessibility of naphthalene was higher in the intestinal phase (28–60 %) than in the gastric phase (24–40 %). These findings demonstrate that MPs can serve as vectors for naphthalene transport from freshwater systems to the human gastrointestinal tract (GIT). This vector effect should be investigated further and taken into consideration to estimate associated risks to human health.
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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 | 0.001 | 0.001 |
| 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".