Comparative electron microscopic visualization of the lung alveolar epithelial glycocalyx with different staining and labeling methods
Bibliographic record
Abstract
The alveolar surface of the lung is lined by an epithelium consisting of type I (AECI) and type II alveolar epithelial cells (AECII). This epithelium is covered by a liquid alveolar lining layer (ALL). Besides intra-alveolar surfactant, ALL also contains the alveolar epithelial glycocalyx on the apical side of AECI and AECII. To better understand the alveolar epithelial glycocalyx, its ultrastructural visualization by transmission electron microscopy is required. The aim of this study was to systematically re-evaluate routine cytochemical methods for visualization of the alveolar epithelial glycocalyx and specifically its glycan components. For this purpose, we used chemical fixation by vascular perfusion with aldehydes as a common routine approach in mice. After fixation, staining is needed for glycocalyx visualization. Cytochemical staining agents such as alcian blue, ruthenium red, and lanthanum nitrate were compared. In addition, SNL (Sambucus nigra lectin) and UEA1 (Ulex europaeus agglutinin I) were used for sialic acid and fucose-specific labeling. Alcian blue showed the strongest staining, with cloud-like structures, whereas ruthenium red appeared as thread-like structures. On the other hand, lanthanum nitrate did not stain the alveolar epithelial glycocalyx. For specific sialic acid and fucose labeling, both lectins presented a specific signal. In conclusion, these methods can be used routinely for assessing ultrastructural changes of the alveolar epithelial glycocalyx in experimental in vivo models under different physiological and pathological conditions. In addition, cytochemical staining by tissue massage and post-embedding lectin labeling after vascular perfusion support 3R (reduction, refinement, replacement) principles of animal welfare.
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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 | 0.001 | 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 | 0.000 |
| 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".