Tyrosinase as a dual reporter gene for both photoacoustic and magnetic resonance imaging
Bibliographic record
Abstract
Reporter genes are useful scientific tools for analyzing promoter activity, transfection efficiency, and cell migration. The current study has validated the use of tyrosinase (involved in melanin production) as a dual reporter gene for magnetic resonance and photoacoustic imaging. MCF-7 cells expressing tyrosinase appear brown due to melanin. Magnetic resonance imaging of tyrosinase-expressing MCF-7 cells in 300 μL plastic tubes displayed a 34 to 40% reduction in T1 compared to normal MCF-7 cells when cells were incubated with 250 μM ferric citrate. Photoacoustic imaging of tyrosinase-expressing MCF-7 cells in 700 μm plastic tubes displayed a 20 to 57-fold increase in photoacoustic signal compared to normal MCF-7 cells. The photoacoustic signal from tyrosinase-expressing MCF-7 cells was significantly greater than blood at 650 nm, suggesting that tyrosinase-expressing cells can be differentiated from the vasculature with in vivo photoacoustic imaging. The imaging results suggest that tyrosinase is a useful reporter gene for both magnetic resonance and photoacoustic imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".