Diversity, Complexity, and Specificity of Bacterial Lipopolysaccharide (LPS) Structures Impacting Their Detection and Quantification
Bibliographic record
Abstract
Endotoxins are toxic lipopolysaccharides (LPSs), extending from the outer membrane of Gram-negative bacteria and notorious for their toxicity and deleterious effects. The comparison of different LPSs, isolated from various Gram-negative bacteria, shows a global similar architecture corresponding to a glycolipid lipid A moiety, a core oligosaccharide, and outermost long O-chain polysaccharides with molecular weights from 2 to 20 kDa. LPSs display high diversity and specificity among genera and species, and each bacterium contains a unique set of LPS structures, constituting its protective external barrier. Some LPSs are not toxic due to their particular structures. Different, well-characterized, and highly purified LPSs were used in this work to determine endotoxin detection rules and identify their impact on the host. Endotoxin detection is a major task to ensure the safety of human health, especially in the pharma and food sectors. Here, we describe the impact of different LPS structures obtained under different bacterial growth conditions on selective LPS detection methods such as LAL, HEK-blue TLR-4, LC-MS2, and MALDI-MS. In these various assays, LPSs were shown to respond differently, mainly attributable to their lipid A structures, their fatty acid numbers and chain lengths, the presence of phosphate groups, and their possible substitutions.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".