In vivo imaging of inducible tyrosinase gene expression with an ultrasound array-based photoacoustic system
Bibliographic record
Abstract
Tyrosinase, a key enzyme in the production of melanin, has shown promise as a reporter of genetic activity. While green fluorescent protein has been used extensively in this capacity, it is limited in its ability to provide information deep in tissue at a reasonable resolution. As melanin is a strong absorber of light, it is possible to image gene expression using tyrosinase with photoacoustic imaging technologies, resulting in excellent resolutions at multiple-centimeter depths. While our previous work has focused on creating and imaging MCF-7 cells with doxycycline-controlled tyrosinase expression, we have now established the viability of these cells in a murine model. Using an array-based photoacoustic imaging system with 5 MHz center frequency, we capture interleaved ultrasound and photoacoustic images of tyrosinase-expressing MCF-7 tumors both in a tissue mimicking phantom, and in vivo. Images of both the tyrosinase-expressing tumor and a control tumor are presented as both coregistered ultrasound-photoacoustic B-scan images and 3-dimensional photoacoustic volumes created by mechanically scanning the transducer. We find that the tyrosinase-expressing tumor is visible with a signal level 12dB greater than that of the control tumor in vivo. Phantom studies with excised tumors show that the tyrosinase-expressing tumor is visible at depths in excess of 2cm, and have suggested that our imaging system is sensitive to a transfection rate of less than 1%.
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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) | 0.001 | 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".