Assessing mucociliary transport of single particles in vivo shows variable speed and preference for the ventral trachea in newborn pigs
Bibliographic record
Abstract
Mucociliary transport (MCT) is an innate defense mechanism that removes particulates, noxious material, and microorganisms from the lung. Several airway diseases exhibit abnormal MCT, including asthma, chronic bronchitis, and cystic fibrosis. However, it remains uncertain whether MCT abnormalities contribute to the genesis of disease or whether they are secondary manifestations that may fuel disease progression. Limitations of current MCT assays and of current animal models of human disease have hindered progress in addressing these questions. Therefore, we developed an in vivo assay of MCT, and here we describe its use in newborn wild-type pigs. We studied pigs because they share many physiological, biochemical, and anatomical features with humans and can model several human diseases. We used X-ray multidetector-row-computed tomography to track movement of individual particles in the large airways of newborn pigs. Multidetector-row-computed tomography imaging provided high spatial and temporal resolution and registration of particle position to airway anatomy. We discovered that cilia orientation directs particles to the ventral tracheal surface. We also observed substantial heterogeneity in the rate of individual particle movement, and we speculate that variations in mucus properties may be responsible. The increased granularity of MCT data provided by this assay may provide an opportunity to better understand host defense mechanisms and the pathogenesis of airway disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".