Validating tyrosinase homologue <i>Mel</i>A as a photoacoustic reporter gene for imaging<i> Escherichia coli</i>
Bibliographic record
Abstract
Antibiotic drug resistance is a major worldwide issue. Development of new therapies against pathogenic bacteria requires appropriate research tools for replicating and characterizing infections. Previously fluorescence and bioluminescence modalities have been used to image infectious burden in animal models but scattering significantly limits imaging depth and resolution. We hypothesize that photoacoustic imaging, which has improved depth-toresolution ratio, could be useful for visualizing MelA-expressing bacteria since MelA is a bacterial tyrosinase homologue involved in melanin production. Using an inducible expression system, E. coli expressing MelA were visibly black in liquid culture. Phosphate buffered saline (PBS), MelA-expressing bacteria (at different dilutions in PBS), and chicken embryo blood were injected in plastic tubes which were imaged using a VisualSonics Vevo LAZR system. Photoacoustic imaging at 6 different wavelengths (680, 700, 750, 800, 850 and 900nm) enabled spectral de-mixing to distinguish melanin signals from blood. The signal to noise ratio of 9x diluted MelA bacteria was 55, suggesting that ~20 bacteria cells could be detected with our system. When MelA bacteria were injected as a 100 μL bolus into a chicken embryo, photoacoustic signals from deoxy- and oxy- hemoglobin as well as MelA-expressing bacteria could be separated and overlaid on an ultrasound image, allowing visualization of the bacterial location. Photoacoustic imaging may be a useful tool for visualizing bacterial infections and further work incorporating photoacoustic reporters into infectious bacterial strains is warranted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".