Longitudinal investigation of prostate tumor spheroid proliferation with dynamic line-field optical coherence tomography
Bibliographic record
Abstract
Abstract Recently, it has become widely recognized that culturing cancer cells in vitro in small, 3D aggregates known as tumor spheroids provides a more physiologically relevant model of in vivo tumor behavior compared to 2D monolayer cultures. Dynamic optical coherence tomography (dOCT) is a non-invasive imaging modality that, by analyzing temporal fluctuations in the light scattered from biological tissue, does not require exogenous contrast agents to visualize and quantify cellular activity within 3D cell cultures. However, recent volumetric dOCT studies have encountered challenges due to low acquisition speeds. In this study, we present morphological and dynamic analyses of prostate tumor spheroid growth over a two-week longitudinal period, utilizing volumetric imaging with a line-field dOCT platform. Our method clearly differentiated between active cellular metabolism in live spheroids and the lack of activity in spheroids fixed with formaldehyde. Quantitative validation of the dynamic signal was conducted using the Alamar Blue proliferation assay, while qualitative validation was provided by live/dead fluorescence microscopy.
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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 | 0.001 | 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) | 0.001 | 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".