Complexing amphotericin B with gold nanoparticles improves fungal clearance from the brains of mice infected with Cryptococcal neoformans
Bibliographic record
Abstract
Amphotericin B (AmB) is used to treat cryptococcal meningoencephalitis. However, the mortality rate remains high. Higher doses of AmB in deoxycholate buffer (AmBd) are toxic to human red blood cells (hRBC) and have no effect on brain organism load in mice. Here we show that while AmBd lysed 96% of hRBC, AmB complexed with gold nanoparticles (AuNP-SA-AmB) lysed only 27% of hRBC. In vitro growth of C. neoformans was inhibited by 0.25 μg/ml AmBd and 0.04 μg/ml of AuNP-SA-AmB. In mice infected with C. neoformans, five daily treatments with AuNP-SA-AmB containing 0.25 mg/kg AmB significantly lowered the fungal burden in the brain tissue compared to either untreated or treatment with 0.25 mg/kg of AmBd. When a single dose of AmBd was injected intravenously into BALB/c mice, 81.61% of AmB cleared in the α-phase and 18.39% cleared in the β-phase at a rate of 0.34% per hour. In contrast, when AuNP-SA-AmB was injected, 49.19% of AmB cleared in the α-phase and 50.81% of AmB cleared in the β-phase at a rate of 0.27% per hour. These results suggest that AmB complexed with gold nanoparticles is less toxic to hRBC, is more effective against C. neoformans and persists longer in blood when injected into mice resulting in more effective clearing of C. neoformans from the brain tissue. LAY SUMMARY: Amphotericin B (AmB) was complexed with gold nanoparticles (AuNP-SA-AmB) to improve brain delivery. AuNP-SA-AmB was more effective than AmB alone in clearing of Cryptococcus neoformans from the brain tissue of infected mice. This may be due to longer plasma half-life of AmB as AuNP-SA-AmB.
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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".