Electrophoretic and Mass Spectrometric Strategies for the Identification of Lipopolysaccharides and Immunodeterminants in Pathogenic Strains of Haemophilus influenzae; Application to Clinical Isolates
Bibliographic record
Abstract
The application of capillary electrophoresis coupled to electrospray mass spectrometry (CE-ES-MS) for the analysis of complex lipopolysaccharides (LPS) is presented. Electrophoretic conditions conducive to both negative and positive ion detection were developed, and facilitated the separation of closely related glycoforms and isoforms from 0-deacylated LPS of different strains of Haemophilus influenzae . To aid the identification of specific functionalities and immunodeterminants of LPS such as pyrophosphoethanolamine, phosphocholine and N-acetyl neuraminic acid, a mixed scan function was used to promote the in-source formation of selected fragment ions under high orifice voltage conditions, while enabling the detection of multiply-charged ions using low orifice voltage. By using such scanning functions, trace levels of 0-deacylated LPS containing a single phosphocholine group were detected at an estimated level of 5% of the overall LPS population. The sensitivity and specificity of the mixed scan function also facilitated the identification of trace levels of sialylated LPS from isolates originally obtained from otitis media. The detection of positive ions from anionic O-deacylated LPS was made possible in CE-ES-MS experiments using ammonium acetate buffers, thereby enabling the structural characterization of oligosaccharide branching by on-line tandem mass spectrometry using a quadrupole/time-of-flight instrument. The combination of high resolution with high sensitivity mass spectrometric detection provided an efficient analytical tool for probing the subtle structural changes occurring in the diverse population of LPS from H. influenzae . These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".