Analysis of Bacterial Glycolipids by Capillary Electrophoresis-Electrospray Mass Spectrometry: Haemophilus influenzae and Neisseria meningitidis Lipopolysaccharides
Bibliographic record
Abstract
A number of invasive diseases are caused by human mucosal pathogens such as those of the genera Haemophilus, Neisseria, Moraxella, Campylobacter, and Bordetella. The bacterium N. gonorrhoeae infects mucosal surfaces of the genital tract, and can enter the bloodstream, survive, and cause secondary infections if protective antibodies are not raised in time (1). Both N. gonorrhoea and H. ducreyi are highly pathogenic, causing sexually transmitted diseases such as gonorrrhea and genital ulcers, respectively (2). The pathogens H. influenzae and N. meningitidis are uniquely adapted to colonize the human respiratory tracts and can lead to disseminated infections including otitis, and bacterial meningitis in young children (3,4). Through evolution, a number of these Gram-negative bacteria have elaborated surface antigens that mimic those found in human glycosphingolipids, thereby providing a mechanism for evading the innate immune system and enhancing their survival in the challenging environmental conditions of the host mucosa. While the exact mechanisms of colonization and invasion of H. influenzae and N. meningitidis are still poorly understood, it is generally recognized that lipopolysaccharides (LPS) play an important role in the virulence and pathogenicity of these organisms (5,6) and can associate with mucus and damaged epithelium of the human nasopharyngeal tissue (7).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".