Abstracts of the 15th International Symposium on bioluminescence and chemiluminescence
Bibliographic record
Abstract
The nucleoside analogue cytosine arabinoside (Ara-C) is the cornerstone of acute myeloid leukaemia (AML) therapy, but approximately 70% of patients fail to achieve complete remission with Ara-C.Current methods of testing drug sensitivity of AML are time consuming and unsuitable for routine screening.This project has developed a bioluminescent bacterial biosensor for use in a simple and rapid assay to determine the Ara-C sensitivity of patient cells, enabling clinicians to consider drug efficacy prior to treatment.An Ara-C-sensitive Escherichia coli was constructed and made self-bioluminescent by introducing the pBBR1MCS-2 plasmid carrying the lux operon (luxCDABE).The biosensor was evaluated with AML cell lines exposed to therapeutic levels of Ara-C.A significant increase in light level was observed in cell-lines known to be Ara-C sensitive.An Ara-C-resistant cell line showed no significant increase in light production in the presence of Ara-C compared to untreated controls.The assay was completed within 8-10 h and correlated well with a standard cytotoxicity assay taking 3 -4 days.Samples of bone marrow and peripheral blood from AML patients were tested by both methods and the rapid bacterial biosensor assay correlated well with standard tests and also patient outcomes, indicating potential for clinical use.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.114 | 0.075 |
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".