Distribution of Effervescent Inhalable Nanoparticles After Pulmonary Delivery: an <i>In Vivo</i> Study
Bibliographic record
Abstract
BACKGROUND: Effervescent inhalable nanoparticles (NPs) have previously been shown to be a promising alternative to conventional lung cancer treatment in animals. This study investigates the biodistribution of effervescent inhalable NPs after a single dose administration via pulmonary route in lung cancer-bearing mice. METHODS & RESULTS: Whole-body autoradiography and confocal laser-scanning microscopy (CLSM) were used to investigate the distribution of inhalable NPs loaded in an effervescent microcarrier. Inhalable doxorubicin-loaded NPs were tagged with 14C for whole-body autoradiography, or with fluorescein isothiocyanate for CLSM imaging. After pulmonary delivery, NPs were widely disseminated in the lungs with a long retention time (24 h). The heart was radioactivity free at all time points of the study. CLSM images showed that inhalable NPs were taken up by cells and that doxorubicin was released to the cell nuclei. CONCLUSION: This is the first study to investigate the distribution of inhalable NPs in a lung cancer-bearing animal model. Inhalable NPs achieved deep lung deposition, were actively released from microcarrier particles, spread to different parts of the lung and released doxorubicin in vivo. These NP characteristics contribute to the efficacy of effervescent inhalable NPs as a lung cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".