Novel Route of Toxicant Exposure in an Ancient Extant Vertebrate: Nickel Uptake by Hagfish Skin and the Modifying Effects of Slime
Bibliographic record
Abstract
Utilizing an in vitro technique, the skin of Pacific hagfish (Eptatretus stouti) was shown to take up nickel from the water via a high affinity, low capacity transport pathway. Uptake was biphasic, with saturation occurring at low nickel exposure concentrations, superseded by linear, diffusive uptake at levels greater than 50 μM. In vivo exposures showed that nickel accumulated mainly in the gill, heart, and brain, representing a tissue distribution distinct from that found in teleosts. Slime on the epidermal surface was shown to significantly reduce the uptake of low concentrations (10 μM) of the metals zinc and nickel, but slime had no effect on organic nutrient (the amino acid l-alanine) absorption. At a higher metal exposure concentration (1 mM), slime was no longer protective, indicating saturation of metal-binding sites. This is the first study to show that metals can be taken up by the integument of hagfish. The ability of the skin to act as a transport epithelium may be of particular importance for a burrowing, benthic scavenger, such as hagfish, which are likely to be exposed to relatively enriched levels of metal toxicants through their habitat and lifestyle, and this may have consequences for human health through hagfish consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".