Endotoxin contamination of engineered nanomaterials: Overcoming the hurdles associated with endotoxin testing
Bibliographic record
Abstract
Nanomaterials are highly susceptible to endotoxin contamination due their large surface-to-volume ratios and endotoxins propensity to associate readily to hydrophobic and cationic surfaces. Additionally, the stability of endotoxin ensures it cannot be removed efficiently through conventional sterilization techniques such as autoclaving and ionizing radiation. In recent times, the true significance of this hurdle has come to light with multiple reports from the United States Nanotechnology Characterization Laboratory, in particular, along with our own experiences of endotoxin testing from multiple Horizon 2020-funded projects which highlight the importance of this issue for the clinical translation of nanomaterials. Herein, we provide an overview on the topic of endotoxin contamination of nanomaterials intended for biomedical applications. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".