Monitoring Toxic Ionic Liquids in Zebrafish (<i>Danio rerio</i>) with Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI)
Bibliographic record
Abstract
Ambient mass spectrometry imaging has become an increasingly powerful technique for the direct analysis of biological tissues in the open environment with minimal sample preparation and fast analysis times. In this study, we introduce desorption electrospray ionization mass spectrometry imaging (DESI-MSI) as a novel, rapid, and sensitive approach to localize the accumulation of a mildly toxic ionic liquid (IL), AMMOENG 130 in zebrafish ( Danio rerio ). The work demonstrates that DESI-MSI has the potential to rapidly monitor the accumulation of IL pollutants in aquatic organisms. AMMOENG 130 is a quaternary ammonium-based IL reported to be broadly used as a surfactant in commercialized detergents. It is known to exhibit acute toxicity to zebrafish causing extensive damage to gill secondary lamellae and increasing membrane permeability. Zebrafish were exposed to the IL in a static 96-h exposure study in concentrations near the LC 50 of 1.25, 2.5, and 5.0 mg/L. DESI-MS analysis of zebrafish gills demonstrated the appearance of a dealkylated AMMOENG 130 metabolite in the lowest concentration of exposure identified by a high resolution hybrid LTQ-Orbitrap mass spectrometer as the trimethylstearylammonium ion, [C 21 H 46 N] + . With DESI-MSI, the accumulation of AMMOENG 130 and its dealkylated metabolite in zebrafish tissue was found in the nervous and respiratory systems. AMMOENG 130 and the metabolite were capable of penetrating the blood brain barrier of the fish with significant accumulation in the brain. Hence, we report for the first time the simultaneous characterization, distribution, and metabolism of a toxic IL in whole body zebrafish analyzed by DESI-MSI. This ambient mass spectrometry imaging technique shows great promise for the direct analysis of biological tissues to qualitatively monitor foreign, toxic, and persistent compounds in aquatic organisms from the environment.
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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) | 0.000 | 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".