Photoacoustic and fluorescent imaging GAF2 photoswitchable chromoproteins (Conference Presentation)
Bibliographic record
Abstract
Molecular photoacoustic imaging is hindered by hemoglobin background signal. Photoswitchable chromoproteins can be used to obtain images with significantly reduced background signal. Molecular imaging of multiple biological processes via multiple chromoprotiens is difficult due to overlapping imaging spectra. Using a new rate-of-change imaging methodology, we can obtain molecular images with multiple chromoprotiens with overlapping imaging spectra. We also present a new photoswitchable chromoprotein, GAF2, which is significantly smaller than the BphP1 which has shown promise for photoswitchable photoacoustic imaging [Yao et al., Nat. Meth. 13, 67–73 (2016)]. We use BphP1 and GAF2 with photoacoustic (Vevo LAZR, Fujifilm Visualsonics Inc) and fluorescence (In vivo Xtreme, Bruker) imaging systems to show background-free multiplexed images. We image before, after, and during photoconversion to obtain background-free rate-of-change images and compare our results to difference imaging and spectral demixing. After phantom imaging, we inject mice with different chromoprotein-expressing E. coli bacteria to show multiplexed images of bacterial infections. We show distinguishable differences in the rate-of-change between GAF2 and BphP1. We obtain rate-of-change feasibility images and in vivo images in mice showing the ability to differentiate between GAF2 and BphP1 even though they are spectrally similar. We photoconvert both GAF2 and BphP1 using 550nm and 735nm light. Phantom studies suggest a 10-20dB improvement in the rate-of-change and difference images in comparison to images with background. Multiplexed background-free molecular imaging using chromoproteins could prove to be a promising new imaging methodology especially when combined with spectral demixing.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".