Monitoring cells in engineered tissues with optical coherence phase microscopy: Optical phase fluctuations as endogenous sources of contrast
Bibliographic record
Abstract
There is a need in tissue engineering to monitor cell growth and health within 3D constructs non-invasively and in a label-free manner. We have previously shown that optical coherence phase microscopy was sensitive enough to monitor intracellular motion. Here we demonstrate that intracellular motility can be used as an endogeneous contrast agent to image cells in various 3D engineered tissue architectures. Phase and intensity-based reconstruction algorithms are compared. In this study, we used an optical coherence phase microscope set up in a common path configuration, developed around a Callisto OCT engine (Thorlbas) centred at 930nm and an inverted microscope with a custom scanning head. Intensity data were used to perform in-depth microstructural imaging. In addition, phase fluctuations were measured by collecting several successive B scans at the same location, and the first time derivative of the phase, i.e. time fluctuations, was analysed over the acquisition time interval to map the motility. Alternative intensity-based Doppler variance algorithms were also investigated. Two distinct scaffold systems seeded with adult stem cells; algimatrix (Invitrogen) and custom microfabricated poly(D,L-lactic-co-glycolic acid) fibrous scaffolds, as well as cell pellets were imaged. We showed that optical phase fluctuations resulting from intracellular motility can be used as an endogenous source of contrast for optical coherence phase microscopy enabling the distinction of viable cells from the surrounding scaffold.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".