Silver spray deposition for AgLDI imaging MS of cholesterol and other olefins on thin tissue sections
Bibliographic record
Abstract
Olefins such as cholesterol and unsaturated fatty acids play important biological roles. Silver-assisted laser desorption ionization (AgLDI) takes advantage of the strong affinity of silver to conjugate with double bonds to selectively ionize these molecules for imaging mass spectrometry (IMS) experiments. For IMS studies, two main approaches for silver deposition have been described in the literature: fine coating by silver sputtering and spray deposition of silver nanoparticles. While these approaches allow for extremely high resolution IMS experiments to be conducted, they are not readily available to all laboratories. Herein, we present a silver nitrate spray deposition approach as an alternative to silver sputtering and nanoparticle deposition for routine IMS analysis. The silver nitrate spray has the same level of specificity and sensitivity for olefins, particularly cholesterol, and has shown to be capable of IMS experiments down to 10-μm spatial resolution. Minimal sample preparation and the affordability of silver nitrate make this a convenient and accessible technique worth considering.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".