Posaconazole-Loaded Leukocytes as a Novel Treatment Strategy Targeting Invasive Pulmonary Aspergillosis
Bibliographic record
Abstract
Background: Impaired delivery of antifungals to hyphae within necrotic lesions is thought to contribute to therapeutic failure in invasive pulmonary aspergillosis (IPA). We hypothesized that transfusion of leukocytes loaded ex vivo with the lipophilic antifungal posaconazole could improve delivery of antifungals to the sites of established infection and improve outcome in experimental IPA. Methods: The HL-60 leukemia cell line was differentiated to a neutrophil-like phenotype (differentiated HL-60 [dHL-60] cells) and then exposed to a range of posaconazole concentrations. The functional capacity and antifungal activity of these cells were assessed in vitro and in a mouse model of IPA. Results: Posaconazole levels in dHL-60 cells were 265-fold greater than the exposure concentration. Posaconazole-loaded cells were viable and maintained their capacity to undergo active chemotaxis. Contact-dependent transfer of posaconazole from dHL-60 cells to hyphae was observed in vitro, resulting in decreased fungal viability. In a neutropenic mouse model of IPA, treatment with posaconazole-loaded dHL-60 cells resulted in significantly reduced fungal burden in comparison to treatment with dHL-60 cells alone. Conclusions: Posaconazole accumulates at high concentrations in dHL-60 cells and increases their antifungal activity in vitro and in vivo. These findings suggest that posaconazole-loading of leukocytes may hold promise for the therapy of IPA.
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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".