Bibliographic record
Abstract
Real-time imaging of the intact lung allows for the visualization and analysis of pulmonary vascular responses at a unique spatial and temporal resolution. For visual access to the pulmonary microvasculature under closed chest conditions, a series of thoracic window techniques has been developed. These approaches provide singular insights into basic physiological phenomena, such as capillary recruitment and hypoxic pulmonary vasoconstriction, and into the site, extent, and mechanisms of neutrophil margination in the pulmonary vasculature. Recent advancements, such as the murine thoracic window model and the oxygen saturation mapping technique, have expanded these applications to studies in genetically modified animals and to the analysis of regional gas exchange. Although intravital microscopy may visualize vascular responses in their most physiologic context, functional imaging in isolated perfused lungs allows for targeting of individual cell subsets with functional fluorescence probes and specific interventions. This approach has proven essential for studies on the spatial arrangement and trafficking of cytoskeletal proteins such as actin, organelles such as mitochondria, or vesicles such as Weibel-Palade bodies in lung endothelial cells. Functional imaging has generated unprecedented insights into the temporal and spatial profile of intra- and subcellular second messenger systems, including signaling cascades via Ca(2+), nitric oxide, or reactive oxygen species. The rapid advancement of bioimaging capabilities at the technical level, the increasing availability of protein-based fluorescent probes, and the ongoing refinement of in vivo and ex vivo lung models provide promising prospects for the application of real-time bioimaging to the further resolution of pulmonary vascular responses in lung health and disease.
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 | 0.001 | 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) | 0.002 | 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".