Long-term monitoring in a microfluidic system to study tumour spheroid response to chronic and cycling hypoxia
Bibliographic record
Abstract
Abstract We demonstrate the application of a microfluidic platform combining spatiotemporal oxygen control and long-term microscopy monitoring to observe tumour spheroid response to hypoxia. The platform is capable of recreating physiologically-relevant low and cycling oxygen levels not attainable in traditional cell culture environments, while image-based monitoring visualizes cell response to these physiologically-relevant conditions. Monitoring spheroid cultures during hypoxic exposure allows us to observe, for the first time, that spheroids swell and shrink in response to time-varying oxygen profiles switching between 0% and 10% O2; this swelling-shrinkage behaviour appears to be driven by swelling of individual cells within the spheroids. We also apply the system to monitoring tumour models during anticancer treatment under varying oxygen conditions. We observe higher uptake of the anticancer agent doxorubicin under a cycling hypoxia profile than under either chronic hypoxia or in vitro normoxia, and the two-photon microscopy monitoring facilitated by our system also allows us to observe heterogeneity in doxorubicin uptake within spheroids at the single-cell level. Combining optical sectioning microscopy with precise spatiotemporal oxygen control and 3D culture opens the door for a wide range of future studies on microenvironmental mechanisms driving cancer progression and resistance to anticancer therapy. These types of studies could facilitate future improvements in cancer diagnostics and treatment.
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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".