Development of a Bacterial Endotoxin Test on a Novel Self-Contained Digital Microfluidics Platform
Bibliographic record
Abstract
This thesis introduces an approach to detect bacterial endotoxin, a potent pyrogen known to trigger severe immune responses, including death. Traditional methods relying on Limulus amoebocyte lysate (LAL) reagents sourced from horseshoe crabs face sustainability concerns due to escalating demand. We present a novel digital microfluidics (DMF) technique to address this challenge, drastically reducing LAL reagent volumes while maintaining assay sensitivity. By precisely controlling the gelation process on DMF devices, reagent use can be reduced by 100x-1000x compared to conventional assays. The study explores the concentration-dependent behavior of the DMF-LAL assay, aiming to establish its effectiveness in detecting endotoxins across a range of concentrations. Additionally, a novel DMF-LAL platform addresses the feasibility of implementing the DMF assay for streamlined and user-friendly operation. This research explores the concentration-dependent behavior of DMF-LAL assays, paving the way for automated endotoxin detection and underscoring its potential for widespread adoption in pharmaceutical and biomedical industries.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".