Imaging growth of thick engineered tissues with fluorescence diffuse optical tomography
Bibliographic record
Abstract
Recent advances in tissue engineering (TE) aim to grow 3D volumes of tissue in bioreactor conditions. This has proved to be a difficult task thus far, notably due to the lack of non-invasive diagnostic tools to monitor the growth of a tissue and ensure its appropriate development. To fulfill part of this need, we currently develop a non-invasive imaging technique based on fluorescence diffuse optical tomography (FDOT) to image in 3D, via fluorescent tracers, processes relevant to tissue growth in a bioreactor. More particularly, here we are interested in imaging the formation of micro-blood vessels in tissue cultures grown on biodegradable scaffolds. Blood vessels are thought to play a fundamental role in tissue growth. Since a bioreactor possesses a known geometry (by design), we propose an FDOT configuration that uses fiber optics brought in contact with the boundary of the bioreactor to collect tomographic optical data. We describe an optical fibers-based set-up and experimental measurements that demonstrate the possibility of localizing a fluorophore-filled 500&mgr;m capillary immersed in a scattering medium contained in a cylindrically-shaped glass tube. These conditions are representative of experiments to be carried on real tissue cultures. In our particular implementation, time-resolved scattering- fluorescence measurements are made via time-correlated single photon counting. Numerical constant fraction discrimination applied to our time-resolved data allows to extract primary localization information.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".