Application of a TLR overexpression cell model in pyrogen detection
Bibliographic record
Abstract
Pyrogens are components derived from microorganisms that induce complex inflammatory responses. Current approaches to detect pyrogens are complex and difficult to replicate, thus there is a need for new methods to detect pyrogens. We successfully constructed a pyrogen-sensitive cell model by overexpressing Toll-like receptor (TLR)2, TLR4, MD2, and CD14 in HEK293 cells. Since the cytokine IL-6 is specifically released upon stimulation of the TLR2 and TLR4 signaling pathways in response to pyrogen stimulation, we used it as a read out for our assay. Our results show that IL-6 is released in response to trace amounts of pyrogens in our cell model. Pyrogen incubation times and concentrations were explored to determine the sensitivity of our cell model, and was found to be sensitive to 0.05 EU/ml of LPS and 0.05 ug/ml of LTA after stimulation for 5 hr. Our TLR overexpressing cell model, with IL-6 as readout, could be a new method for in vitro testing of pyrogens and applicable for evaluating the safety of drugs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".