Imaging of whole zebra fish ( <i>Danio rerio</i> ) by desorption electrospray ionization mass spectrometry
Bibliographic record
Abstract
RATIONALE: To demonstrate the potential use of zebra fish (Danio rerio) as a model vertebrate organism by producing two-dimensional ion images of the whole zebra fish, and being able to distinguish particular areas of interest such as the brain, spinal cord, and stomach region using a desorption electrospray ionization (DESI) ion source coupled to a linear ion trap. METHODS: Imaging experiments are performed on 45 µm sagittal slices of zebra fish (Danio rerio), which are thaw-mounted onto microscope glass slides. The slides are then analyzed using a solvent of acetonitrile/dimethylformamide (50:50) (ACN/DMF), with a solvent flow rate of 1.5 μL/min; data are acquired in negative ion mode. Raw mass spectrum data files are converted into a readable file for Biomap. The images produced are then analyzed for ion distributions. RESULTS: We are able to create clear, distinct, chemical intensity images of the brain, spinal cord, and stomach based on lipid content as well as bile salt. The identities of these compounds were confirmed by tandem mass spectrometric (MS/MS) experiments and comparisons with literature. CONCLUSIONS: Imaging of whole zebra fish is possible using ambient ionization techniques such as DESI. Analyses are fast and reliable. For most of the compounds observed, the identification by MS/MS can be performed directly from the fish tissue sample.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".