Novel RP-HPLC based assay for selective and sensitive endotoxin quantification
Bibliographic record
Abstract
The paper presents a novel instrumental analytical endotoxin quantification assay. It uses common analytical laboratory equipment (HPLC-FLD) and allows quantifying endotoxins (ETs) in different matrices from about 109 EU per mL down to about 40 EU per mL (RSE based). Test results are obtained in concentration units (e.g. ng ET per mL), which can then be converted to commonly used endotoxin units (EU per mL) in case of known pyrogenic activity. During endotoxin hydrolysis, the endotoxin specific rare sugar acid KDO is obtained quantitatively. After that, KDO is stoichiometrically reacted with DMB, which results in a highly fluorescent derivative. The mixture is separated using RP-HPLC followed by KDO-DMB quantification with a fluorescence detector. Based on the KDO content, the endotoxin content in the sample is calculated. The developed assay is economic and has a small error. Its applicability was demonstrated in applied research. ETs were quantified in purified bacterial biopolymers, which were produced by Gram-negative bacteria. Results were compared to LAL results obtained for the same samples. A high correlation was found between the results of both methods. Further, the new assay was utilized with high success during the development of novel endotoxin specific depth filters, which allow efficient, economic and sustainable ET removal during DSP. Those examples demonstrate that the new assay has the potential to complement the animal-based biological LAL pyrogenic quantification tests, which are accepted today by the major health authorities worldwide for the release of commercial pharmaceutical products.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".