On the use of DHB/aniline and DHB/<i>N,N</i>-dimethylaniline matrices for improved detection of carbohydrates: Automated identification of oligosaccharides and quantitative analysis of sialylated glycans by MALDI-TOF mass spectrometry
Bibliographic record
Abstract
This study demonstrates the application of 2,5-dihydrohybenzoic acid/aniline (DHB/An) and 2,5-dihydroxybenzoic acid/N,N-dimethylaniline (DHB/DMA) matrices for automated identification and quantitative analysis of native oligosaccharides by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Both matrices are shown to be superior to pure DHB for native glycans in terms of signal intensities of analytes and homogeneity of sample distribution throughout the crystal layer. On-target formation of stable aniline Schiff base derivatives of glycans in DHB/An and the complete absence of such products in the mass spectra acquired in DHB/DMA matrix provide a platform for automated identification of reducing oligosaccharides in the MALDI mass spectra of complex samples. The study also shows how enhanced sensitivity is achieved with the use of these matrices and how the homogeneity of deposited sample material may be exploited for quick and accurate quantitative analysis of native glycan mixtures containing neutral and sialylated oligosaccharides in the low-nanogram to mid-picogram range.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".