Cover Picture: Identification of Labile UDP‐Ketosugars in <i>Helicobacter pylori</i>, <i>Campylobacter jejuni</i> and <i>Pseudomonas aeruginosa</i>: Key Metabolites used to make Glycan Virulence Factors (ChemBioChem 12/2006)
Bibliographic record
Abstract
The cover picture shows 3D models of the pyranose rings of UDP‐sugar metabolites that were identified by examining the PseB reaction from Helicobacter pylori by using NMR spectroscopy. PseB plays an important role in the biosynthesis of pseudaminic acid (Pse)—a sialic acid‐like sugar that is found on the flagella of H. pylori. Pse is essential for bacterial motility and as such is a virulence factor. Since, it is not found in humans, agents that interfere with Pse biosynthesis could offer therapeutic potential. Several bacteria produce sugars that are attractive therapeutic targets. However, their biosynthetic pathways can involve multiple enzymatic reactions that produce unstable metabolites in minute quantities. Examination of the WbjB and WbjC reactions from Pseudomonas aeruginosa, the PglF reaction from C. jejuni and the PseB reaction from C. jejuni and H. pylori with NMR clarified biosynthetic pathways led to the identification of unstable metabolites that had not been previously observed. Further details can be found in the article by D. McNally et al. on p. 1865 ff.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.365 | 0.076 |
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".