Invited review: Profiling structural elements of short-chain lipopolysaccharide of non-typeable <i>Haemophilus influenzae</i>
Bibliographic record
Abstract
Lipopolysaccharide (LPS) is a major virulence determinant of the human bacterial pathogen Haemophilus influenzae. A characteristic feature of H. influenzae LPS is the extensive intra- and inter-strain heterogeneity of glycoform structure which is key to the role of the molecule in both commensal and disease-causing behaviour of the bacterium. The chemical composition of non-typeable Haemophilus influenzae (NTHi) LPS is highly diverse. It contains a number of different monosaccharides (Neu5Ac, L-glycero-D-manno heptose, D-glycero-D-manno heptose, Kdo, D-Glc, D-Gal, D-GlcNAc, D-GalNAc) and non-carbohydrate substituents. Prominent non-carbohydrate components are O-acetyl groups, glycine and phosphates. We now know that sialic acid (N-acetylneuraminic acid or Neu5Ac) and certain oligosaccharide extensions are important in the pathogenesis of NTHi; however, the biological implications for many of the various features are still unknown. Electrospray ionization mass spectrometry in combination with separation techniques like CE and HPLC is an indispensable tool in profiling glycoform populations in heterogeneous LPS samples. Mass spectrometry is characterized by its extreme sensitivity. Trace amounts of glycoforms expressing important virulence determinants can be detected and characterized on minute amounts of material. The present review focuses on LPS structures and mass spectrometric methods which enable us to profile these in complex mixtures.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".