Reactive DESI-MS Imaging of Biological Tissues with Dicationic Ion-Pairing Compounds
Bibliographic record
Abstract
This work illustrates reactive desorption electrospray ionization mass spectrometry (DESI-MS) with a stable dication on biological tissues. Rat brain and zebra fish tissues were investigated with reactive DESI-MS in which the dictation forms a stable bond with biological tissue fatty acids and lipids. Tandem mass spectrometry (MS/MS) was used to characterize the dication (DC9) and to identify linked lipid-dication compounds formed. The fragment m/z 85 common to both DC9 fragmentation and DC9-lipid fragmentation was used to confirm that DC9 is indeed bonded with the lipids. Lipid signals in the range of m/z 250-350 and phosphoethanolamines (PE) m/z 700-800 observed in negative ion mode were also detected in positive ion mode with reactive DESI-MS with enhanced signal intensity. Reactive DESI-MS imaging in positive ion mode of rat brain and zebra fish tissues allowed enhanced detection of compounds commonly observed in the negative ion mode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".