Ventilation and Perfusion at the Alveolar Level: Insights From Lung Intravital Microscopy
Bibliographic record
Abstract
. Especially for anatomically complex and dynamic organs such as the lung and its main functional unit, the alveolus, IVM provides exclusive advantages in terms of spatial and temporal resolution. By the use of lung windows, which have advanced and improved over time, direct access to the lung surface is provided. In this review we will discuss two main topics, namely alveolar dynamics and perfusion from the perspective of IVM-based studies. Of special interest are unanswered questions regarding alveolar dynamics such as: What are physiologic alveolar dynamics? How do these dynamics change under pathologic conditions and how do those changes contribute to ventilator-induced lung injury? How can alveolar dynamics be targeted in a beneficial way? With respect to alveolar perfusion IVM has propelled our understanding of the pulmonary microcirculation and its perfusion, as well as pulmonary vasoreactivity, permeability and immunological aspects. Whereas the general mechanism behind these processes are understood, we still lack a proper understanding of the complex, multidimensional interplay between alveolar ventilation and microvascular perfusion, capillary recruitment, or vascular immune responses under physiologic and pathologic conditions. These are only part of the unanswered questions and problems, which we still have to overcome. IVM as the tool of choice might allow us to answer part of these questions within the next years or decades. As every method, IVM has advantages as well as limitations, which have to be taken into account for data analysis and interpretation, which will be addressed in this review.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".