Comparing Imaging Depth of Intravital Lung Imaging Using Perfluorocarbon‐Based Liquid Ventilation With Tissue Clearing for Deep‐Tissue Imaging
Bibliographic record
Abstract
Intravital lung imaging has been employed to study physiological and pathophysiological processes related to nanoparticle deposition in the alveolar lung, particularly in the context of air pollution and drug delivery. However, optical imaging depth is limited, often attributed to the refractive index (RI) mismatch at the alveolar air-tissue interface. To investigate this, we evaluated two complementary strategies. First, we demonstrated that eliminating the RI mismatch via partial liquid ventilation with oxygenated perfluorocarbon (PFC) did not enhance the imaging depth. A second approach, utilizing ex vivo optical tissue clearing (with RI matching), was only successful in improving imaging penetration depth if it included removal of scattering lipids such as pulmonary surfactant. Nevertheless, partial liquid ventilation with PFC in vivo enabled the homogeneous delivery of nanoparticles to the alveoli, allowing real-time observation of their interactions with lung epithelium. This finding opens new avenues for studying inhaled particulates and optimizing inhalation-based drug delivery.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".