Discontinuously Dewetting Solvent Arrays: Droplet Formation and Poly-Synchronous Surface Extraction for Mass Spectrometry Imaging Applications
Bibliographic record
Abstract
We describe a new liquid tissue stamping method called poly-synchronous surface extraction (PSSE) that utilizes an omniphobic substrate patterned with hydrophilic surface energy traps (SETs), which when wet with a solvent form a dense microdroplet array. When contacted with a tissue sample, each droplet locally extracts analytes from the tissue surface, which subsequentially can be analyzed by matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-IMS) or ambient ionization-MS techniques. Optimization of the patterned surface with six different solvents was carried out to increase the droplet density, height, and reproducibility of volume deposition. Once optimized, sister slices of a strawberry (Fragaria × ananassa) were spatially extracted using the PSSE technique and the chemical distribution of selected compounds was analyzed with both MALDI-IMS and a lower resolution but faster ambient liquid microjunction surface sampling probe (LMJ-SSP) approach. Heat maps for target analytes for the PSSE approach are compared to those produced using traditional MALDI-IMS analysis. The PSSE method aligned well with direct analysis and demonstrated the potential to increase the speed of ambient MS tissue imaging techniques by decreasing the number of steps required for sample preparation.
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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) | 0.001 | 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 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".