Three-dimensional oxygen concentration monitoring in hydrogels using low-cost phosphorescence lifetime imaging for tissue engineering
Bibliographic record
Abstract
Oxygen concentration measurement in 3D hydrogels is vital in 3D cell culture and tissue engineering. However, standard 3D imaging systems capable of measuring oxygen concentration with adequate precision are based on advanced microscopy platforms, which are not accessible in many laboratories due to the system's complexity and the high price. In this work, we present a fast and low-cost phosphorescence lifetime imaging design for measuring the lifetime of oxygen-quenched phosphorescence emission with 0.25 µs temporal precision and sub-millimeter spatial resolution in 3D. By combining light-sheet illumination and the frequency-domain lifetime measurement using a commercial rolling-shutter CMOS camera in the structure of a conventional optical microscope, this design is highly customizable to accommodate application-specific research needs while also being low-cost as compared to advanced instruments. As a demonstration, we made a fluidic device with a gas-permeable film to create an artificial oxygen gradient in the hydrogel sample. Dye-embedded beads were distributed in the hydrogel to conduct continuous emission lifetime monitoring when nitrogen was pumped through the fluidic channel and changed oxygen distribution in the sample. The dynamics of the changes in lifetime co-related with their location in the gel of size 0.5 mm×1.5 mm×700 µm demonstrate the ability of this design to measure the oxygen concentration stably and precisely in 3D samples.
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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".