Real-time detection of the early event of cytotoxicity of herbal ingredients on single leukemia cells studied in a microfluidic biochip
Bibliographic record
Abstract
A microfluidic approach has been developed for the real-time detection of drug effects, based on the quantitative measurement of calibrated cytosolic calcium ([Ca(2+)](i)) on single cancer cells. This microfluidic method is rapid by detecting the early event of cytotoxicity of drug candidates on cancer cells, without waiting for a couple of days needed for cell seeding and drug treatment by conventional assays. The miniaturized biochip consists of a V-shaped structure for the single-cell selection and retention. Various test reagents such as the chemotherapy drug (daunorubicin), an ionophore (ionomycin), and herbal ingredients from licorice (isoliquiritigenin or IQ) were investigated for their abilities to stimulate sustained cellular [Ca(2+)](i) elevations. The microfluidic results obtained in hours have been confirmed by conventional cytotoxicity assays which take days to complete. Moreover, any color or chemical interference problems found in the conventional assays of herbal compounds could be resolved. Using the microfluidic approach, IQ (50 microM) has been found to cause a sustained [Ca(2+)](i) elevation and cytotoxic effects on leukemia cells. The microfluidic single-cell analysis not only reduces reagent cost, and demands less cells, but also reveals some phenomena due to cellular heterogeneity that cannot be observed in bulk analysis.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".