Stabilization and swelling of hagfish slime mucin vesicles
Bibliographic record
Abstract
When agitated, Atlantic hagfish (Myxine glutinosa) produce large quantities of slime that consists of hydrated bundles of protein filaments and membrane-bound mucin vesicles from numerous slime glands. When the slime exudate contacts seawater, the thread bundles unravel and the mucin vesicles swell and rupture. Little is known about the mechanisms of vesicle rupture in seawater and stabilization within the gland, although it is believed that the vesicle membrane is permeable to most ions except polyvalent anions. We hypothesized that the most abundant compounds within the slime gland exudate have a stabilizing effect on the mucin vesicles. To test this hypothesis, we measured the chemical composition of the fluid component of hagfish slime exudate and conducted functional assays with these solutes to test their ability to keep the vesicles in a condensed state. We found K(+) concentrations that were elevated relative to plasma, and Na(+), Cl(-) and Ca(2+) concentrations that were considerably lower. Our analysis also revealed high levels of methylamines such as trimethylamine oxide (TMAO), betaine and dimethylglycine, which had a combined concentration of 388 mmol l(-1) in the glandular fluid. In vitro rupture assays demonstrated that both TMAO and betaine had a significant effect on rupture, but neither was capable of completely abolishing mucin swelling and rupture, even at high concentrations. This suggests that some other mechanism such as the chemical microenvironment within gland mucous cells, or hydrostatic pressure is responsible for stabilization of the vesicles within the gland.
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) | 0.000 | 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".