The usefulness of hydrazine derivatives for mass spectrometric analysis of carbohydrates
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Bibliographic record
Abstract
Over the last years, extensive studies have evaluated glycans from different biological samples and validated the importance of glycosylation as one of the most important post-translational modifications of proteins. Although a number of new methods for carbohydrate analysis have been published and there has been significant progress in their identification, the development of new approaches to study these biomolecules and understand their role in living systems are still vivid challenges that intrigue glycobiologists. In the last decade, the success in analyses of oligosaccharides has been driven mainly by the development of innovative, highly sensitive mass spectrometry techniques. For enhanced mass spectrometry detection, carbohydrate molecules are often derivatized. Besides, the type of labeling can influence the fragmentation pattern and make the structural analysis less complicated. In this regard, in 2003 we introduced the low scale, simple non-reductive tagging of glycans employing phenylhydrazine (PHN) as the derivatizing reagent. PHN-labeled glycans showed increased detection and as reported previously they can be analyzed by HPLC, ESI, or MALDI immediately after derivatization. Under tandem mass spectrometry conditions, PHN-derivatives produced useful data for the structural elucidation of oligosaccharides. This approach of analysis has helped to reveal new isomeric structures for glycans of known/unknown composition and has been successfully applied for the profiling of N-glycans obtained from serum samples and cancer cells. The efficacy of this labeling has also been evaluated for different substituted hydrazine reagents. This review summarizes all types of reducing-end labeling based on hydrazone-linkage that have been used for mass spectrometric analyses of oligosaccharides. This review is also aimed at correcting some past misconceptions or interpretations reported in the literature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it