O-antigen polysaccharides in <i>Klebsiella pneumoniae</i> : structures and molecular basis for antigenic diversity
Bibliographic record
Abstract
SUMMARY Klebsiella pneumoniae is a gram-negative species, whose isolates are found in the environment and as commensals in the human gastrointestinal tract. This bacterium is among the leading causes of a range of nosocomial and community-acquired infections, particularly in immunocompromised individuals, where it can give rise to pneumonia, urinary tract infections, septicemia, and liver abscesses. Treatment of K. pneumoniae infections is compromised by the emergence of isolates producing carbapenemase and extended-spectrum β-lactamase enzymes, making it a high priority for new therapeutic approaches including vaccination and immunoprophylaxis. One potential target for these strategies is the O-antigen polysaccharide component of lipopolysaccharides, which are important virulence determinants for K. pneumoniae . Consideration of immunotherapeutic opportunities requires a comprehensive and fundamental understanding of O-polysaccharide structures, distribution of particular O serotypes in clinical isolates, and the potential for antigenic diversification. The number of recognized K. pneumoniae O-polysaccharide antigens has varied over time, complicated by the observation that some examples share similar structural (and potentially antigenically cross-reactive) elements, and by the existence of genetic loci for which corresponding O-polysaccharide structures have yet to be determined. Here, we provide a comprehensive integration of the current carbohydrate structures and genetic information, together with a proposal for an updated classification system for K. pneumoniae O-antigens, that is being implemented in Kaptive for molecular serotyping. The accumulated insight into O-polysaccharide assembly pathways is used to describe the molecular basis for O-antigen diversity in K. pneumoniae .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".