Photoacoustic imaging of a cyanine dye targeting bacterial infection
Bibliographic record
Abstract
The development of a non-invasive infection-specific diagnostic probe holds the potential to vastly improve early-stage detection of infection, enabling precise therapeutic intervention and potentially reducing the incidence of antibiotic resistance. Towards this goal, a commercially available bacteria-targeting Zinc(II)-dipicolylamine (ZnDPA)-derived fluorophore, PSVue794, was assessed as a photoacoustic (PA) imaging probe (PIP). A radiolabeled version of the dye, [ 99m Tc]Tc-PSVue794, was developed to facilitate quantitative biodistribution studies beyond optical imaging methods, which showed a target-to-non-target ratio of 10.1 ± 1.1, 12 h post-injection. The ability of the PIP to differentiate between bacterial infection, sterile inflammation, and healthy tissue in a mouse model, was then evaluated via PA imaging. The PA signal in sites of sterile inflammation (0.062 ± 0.012 a.u.) was not statistically different from that of the background (0.058 ± 0.006 a.u.). In contrast, high PA signal was detected at sites of bacterial infection (0.176 ± 0.011 a.u.) as compared to background (0.081 ± 0.04 a.u., where P ≤ 0.03). This work demonstrates the potential of utilizing established fluorophores towards PAI and utilizing PAI as a modality in the distinction of bacterial infection from sites of sterile inflammation.
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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".