Identification and treatment of the <i>Staphylococcus aureus</i> reservoir in vivo
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is reaching epidemic proportions causing morbidity, mortality, and chronic disease due to relapses, suggesting an intracellular reservoir. Using spinning-disk confocal intravital microscopy to track MRSA-GFP in vivo, we identified that within minutes after intravenous infection MRSA is primarily sequestered and killed by intravascular Kupffer cells (KCs) in the liver. However, a minority of the Staphylococci overcome the KC's antimicrobial defenses. These bacteria survive and proliferate for many days within this intracellular niche, where they remain undetected by recruited neutrophils. Over time, the KCs lyse, releasing bacteria into the circulation, enabling dissemination to other organs such as the kidneys. Vancomycin, the antibiotic of choice to treat MRSA bacteremia, could not penetrate the KCs to eradicate intracellular MRSA. However, based on the intravascular location of these specific macrophages, we designed a liposomal formulation of vancomycin that is efficiently taken up by KCs and diminished the intracellular MRSA. Targeting the source of the reservoir dramatically protected the liver but also dissemination to other organs, and prevented mortality. This vancomycin formulation strategy could help treat patients with Staphylococcal bacteremia without a need for novel antibiotics by targeting the previously inaccessible intracellular reservoir in KCs.
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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".