In‐source fragmentation and analysis of polysaccharides by capillary electrophoresis/mass spectrometry
Bibliographic record
Abstract
In order to develop a robust and easy-to-use technique for characterization of bacterial polysaccharides, a pseudo-hydrolysis strategy was investigated. Based on in-source collision-induced dissociation, polysaccharide molecular ions were fragmented within the orifice-skimmer region of an electrospray ionization (ESI) mass spectrometer. The fragment ions thus generated were then analyzed similarly to the conventional ESI mass spectrometry approach. MS/MS scanning was applied to obtain product-ion spectra of the primary fragments for sequencing. To further improve the sensitivity and separation of polysaccharides from other components in the samples, a pressure-assisted capillary electrophoresis/mass spectrometry (CE/MS) system was employed. Using bacterial polysaccharides as model compounds, the mass spectra obtained for polysaccharide repeating units generated through chemical hydrolysis and in-source fragmentation were directly compared, both in positive and negative ion modes. With the additional separation of impurities provided by CE, the success of this technique has been demonstrated for structural analysis of O-chain polysaccharides (O-PS) and capsular polysaccharides (CPS). In-source fragmentation was applied to promote the formation of structurally relevant repeating units of heterogeneous CPS that would remain undetected using conventional ESI conditions. This approach was proven to be particularly useful for probing the subtle structural differences in monosaccharide composition and functionalities arising across bacterial serotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".