Ultrasound and Microbubbles for Targeted Drug Delivery to the Lung Endothelium in ARDS: Cellular Mechanisms and Therapeutic Opportunities
Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) is characterized by increased permeability of the alveolar-capillary membrane, a thin barrier composed of adjacent monolayers of alveolar epithelial and lung microvascular endothelial cells. This results in pulmonary edema and severe hypoxemia and is a common cause of death after both viral (e.g., SARS-CoV-2) and bacterial pneumonia. The involvement of the lung in ARDS is notoriously heterogeneous, with consolidated and edematous lung abutting aerated, less injured regions. This makes treatment difficult, as most therapeutic approaches preferentially affect the normal lung regions or are distributed indiscriminately to other organs. In this review, we describe the use of thoracic ultrasound and microbubbles (USMB) to deliver therapeutic cargo (drugs, genes) preferentially to severely injured areas of the lung and in particular to the lung endothelium. While USMB has been explored in other organs, it has been under-appreciated in the treatment of lung injury since ultrasound energy is scattered by air. However, this limitation can be harnessed to direct therapy specifically to severely injured lungs. We explore the cellular mechanisms governing USMB and describe various permutations of cargo administration. Lastly, we discuss both the challenges and potential opportunities presented by USMB in the lung as a tool for both therapy and research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".