The impact of LPS mutants on endotoxin masking in different detection systems
Bibliographic record
Abstract
Endotoxin masking poses a potential risk to patient safety by rendering endotoxin undetectable. While research often focuses on international endotoxin standards (RSE), the effects of LPS mutants on Low Endotoxin Recovery (LER) are poorly understood. Our study investigated S. minnesota and E. coli mutants with incomplete O-antigen chains (rough LPS) using Limulus amebocyte lysate (LAL), recombinant Factor C (rFC) and the monocyte activation test (MAT). All tested methods detected the mutants, with variations in activity observed. Measurements over time in a common drug formulation (10 mM sodium citrate and 0.05 % (w/v) polysorbate 20) showed different masking kinetics for the mutants using different methods. We were able to show that LAL and rFC have comparable kinetics, whereas MAT showed improved recovery of masked endotoxin. The study showed that the mutation of LPS have an effect on masking, independent of the assay system. We propose that polysaccharide length affects masking susceptibility, with lower hydrophilic/hydrophobic ratios caused by the shortened polysaccharide chain (rough LPS) reducing masking. In addition, the stronger negative charge of the rough mutants increases cation affinity and is suggested to contribute to the stabilisation of supramolecular structures, making the rough mutants less susceptible to masking than the smooth mutants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".