Longitudinal, label-free, quantitative tracking of cell death and viability in a 3D tumor model with OCT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three-dimensional in vitro tumor models are highly useful tools for studying tumor growth and treatment response of malignancies such as ovarian cancer. Existing viability and treatment assessment assays, however, face shortcomings when applied to these large, complex, and heterogeneous culture systems. Optical coherence tomography (OCT) is a noninvasive, label-free, optical imaging technique that can visualize live cells and tissues over time with subcellular resolution and millimeters of optical penetration depth. Here, we show that OCT is capable of carrying out high-content, longitudinal assays of 3D culture treatment response. We demonstrate the usage and capability of OCT for the dynamic monitoring of individual and combination therapeutic regimens in vitro, including both chemotherapy drugs and photodynamic therapy (PDT) for ovarian cancer. OCT was validated against the standard LIVE/DEAD Viability/Cytotoxicity Assay in small tumor spheroid cultures, showing excellent correlation with existing standards. Importantly, OCT was shown to be capable of evaluating 3D spheroid treatment response even when traditional viability assays failed. OCT 3D viability imaging revealed synergy between PDT and the standard-of-care chemotherapeutic carboplatin that evolved over time. We believe the efficacy and accuracy of OCT in vitro drug screening will greatly contribute to the field of cancer treatment and therapy evaluation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it