Dispersible oxygen microsensors map oxygen gradients in three-dimensional cell cultures
Bibliographic record
Abstract
Phase fluorimetry, unlike the more commonly used intensity-based measurement, is not affected by differences in light paths from culture vessels or by optical attenuation through dense 3D cell cultures and hydrogels thereby minimizing dependence on signal intensity for accurate measurements. This work describes the use of phase fluorimetry on oxygen-sensor microbeads to perform oxygen measurements in different microtissue culture environments. In one example, cell spheroids were observed to deplete oxygen from the cell-culture medium filling the bottom of conventional microwells within minutes, whereas oxygen concentrations remained close to ambient levels for several days in hanging-drop cultures. By dispersing multiple oxygen microsensors in cell-laden hydrogels, we also mapped cell-generated oxygen gradients. The spatial oxygen mapping was sufficiently precise to enable the use of computational models of oxygen diffusion and uptake to give estimates of the cellular oxygen uptake rate and the half-saturation constant. The results show the importance of integrated design and analysis of 3D cell cultures from both biomaterial and oxygen supply aspects. While this paper specifically tests spheroids and cell-laden gel cultures, the described methods should be useful for measuring pericellular oxygen concentrations in a variety of biomaterials and culture formats.
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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".