Changes in the microbiome and associated host tissue structure in the blue mussel (<i>Mytilus edulis</i>) following exposure to polystyrene microparticles
Bibliographic record
Abstract
Marine life is increasingly exposed to microplastics, which can be ingested and disrupt the relationship between host tissues and their microbiomes. We investigated the effects of microplastics (5 µm polystyrene beads) on the microbial community and host tissue structure in organs at high risk of exposure (digestive gland and gills) in blue mussels ( Mytilus edulis Linnaeus, 1758). We exposed mussels to concentrations of microplastic consistent with levels found in local coastal waters. High exposures (1000 particles per m3 per mussel) decreased the alpha and beta diversity in the microbiome of the digestive gland, with an increase in relative abundance of Polaribacter and a decrease in other species in the Flavobacteriaceae. Both low (10 particles per m3 per mussel) and high exposures to polystyrene also changed tissue structure in the hosts, with an increase in immune cells (hemocytes) and reactive lysosomes in the gills, and in the digestive gland, a loss of cell specialization in digestive cells and an increase in cell breakdown products. Thus, exposure to particles of polystyrene in concentrations consistent with levels detected in local coastal zones reduces microbial biodiversity of the digestive gland and disrupts host tissues, which may indicate a loss of the host–symbiont interactions that support tissue homeostasis.
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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".