Bibliographic record
Abstract
Drug delivery by inhalation is used to maximize the therapeutic effect in the lung while minimizing systemic exposure. However, to achieve the best possible treatment we need to understand where a targets is expressed in the lung and assure retention of drugs at the relevant location. Currently, our models regard the lung as a uniform unit and do not distinguish between different tissue structures and hence we need to increase our understanding of what structures are targeted by inhalation and if all benefit from inhaled delivery equally. In the present study, we used RNAscope in-situ hybridization to systemically examine how the administration route influences which part of the lung respond to fluticasone propionate (FP). Together with an automated image analysis, developed specifically to adress the different sub-compartments of the lung, we were able to measure mRNA expression of Zbtb16 in epithelium, sub-epithelium, alveolar bed and blood vessels. Spleenic Zbtb16 expression, measured by qPCR, was used to assess systemic responses. The results show that inhaled and i.v. administration give different distinct spatial responses. Inhalation of FP provides superior targeting of the airway epithelium and sub-epithelium regions but not alveolar space or vasculature compared to systemic administration. Finally, the analysis shows that exclusive targeting of lung epithelium by inhalation can be achieved with very low doses of FP.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".