<em>Galleria mellonella</em> as an Infection and Antibiotic Treatment Model for <em>Acinetobacter baumannii</em>
Bibliographic record
Abstract
Galleria mellonella, commonly known as the greater wax moth or waxworm, is an insect infection model that provides researchers with an informative, simple, and economically feasible way to test the virulence of bacterial pathogens and potential treatment regimens against them. One such pathogen is Acinetobacter baumannii, a World Health Organization top-priority pathogen and a global health threat. The prevalence of deadly, multidrug-resistant A. baumannii in hospital settings, causing > 100,000 deaths in 2021, makes finding new treatment options paramount. A crucial piece of information needed to help eradicate a bacterial infection using antimicrobial compounds is the minimum inhibitory concentration-the lowest dose of a compound that can clear the infection. This value can be determined initially in vitro but then must be tested in a relevant infection model in vivo. Using the waxworm infection model and three different strains of A. baumannii-a virulent type strain, a hypervirulent clinical strain, and a virulent environmental strain-we demonstrate how to use minimum inhibitory concentration data to guide initial antibiotic treatment testing. We also compare two assay styles: infection followed by treatment (infect-wait-treat) and infection and treatment together (infect-and-treat). The results, showing similar trends in waxworm survival between both methods, demonstrate that the infect-and-treat protocol can be as informative as the more traditional infect-wait-treat method, with the benefit of saving valuable time and resources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".