TOWARD MOLECULAR IMAGING PROBES TO DETECT CRYPTIC BACTERIAL INFECTIONS
Bibliographic record
Abstract
Infectious diseases represent one of the leading causes of death globally. Prompt diagnosis is essential for the onset of clinical treatment but certain cases of underlying bacterial infection deep in the body can remain undiagnosed for weeks. Hidden bacterial infection is the leading cause of fever of unknown origin (FUO), which is observed in 2 % of all hospital admissions around the world. Molecular imaging of bacterial infections is the ideal non-invasive diagnostic tool, but all available probes also detect inflammation. Two targets were selected for development of bacteria-specific molecular imaging probes, namely iron-uptake pathways and peptidoglycans involved in the synthesis of the cell wall. Both, Gram-positive and Gram-negative bacteria use iron-binding molecules called siderophores to scavenge iron from their surroundings. The structural similarities between Fe3+ and Ga3+ allow siderophores to be radiolabelled with 67/68Ga and visualized by nuclear medicine techniques. The clinically proven siderophore Deferoxamine (Dfo) has a plasma half-life of only 5.5 min that does not favor its direct use as a probe. Dfo derivatives with improved pharmacokinetics properties were designed and tested on Staphylococcus aureus cultures. The ciprofloxacin and the ethyloxycarbonyl derivatives of DFO at the primary amino position were among the most successful conjugates targeting the siderophore active-transport mechanism and reaching high relative uptake rates. Furthermore, the peptidoglycan pathway of Gram-positive bacteria was in vitro targeted with vancomycin conjugated to 67Ga-Dfo which showed even higher labelling capacity than 67Ga-Dfo within a few minutes of exposure. In vitro siderophore studies remain challenging due to the lack of methods for the preparation of rigorously iron-depleted media. We developed an iron chelating method with the goal of creating iron-free growth media.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".