A toolbox for studying respiratory viral infections using air-liquid interface cultures of human airway epithelial cells
Bibliographic record
Abstract
Submerged cultures of primary human airway epithelial cells or human airway epithelial cell lines have been a mainstay of airway epithelial biology research for decades due to their robust in vitro proliferative capacity, relatively low maintenance culture conditions, and clinically translatable results to nasal or bronchial brushings. With the development and improvement of air-liquid interface (ALI) cultures of human airway epithelial cells, such cultures have been considered superior to immortalized cell lines and primary cell monolayers, as such cultures effectively recapitulate in vivo epithelial architecture and cell types. Although ALI culture growth protocols are well-established and widely available, many researchers have avoided their use, as ALI cultures not only take longer to grow but also present technical challenges and limitations that make in vitro intracellular and structural assays taxing. Challenges arise relating to their complex structure, requirements for air exposure, the constraints of transwell growth apparatus, and interference in assays caused by mucus secretion. Although few publications briefly describe technical adaptations for some assays, there is still considerable trial and error required for researchers to establish consistent and reliable assay adaptations, often becoming a deterrent for pursuing mechanistic investigation. We have created a user-friendly toolbox detailing comprehensive protocols for numerous techniques and assay adaptations, particularly focusing on respiratory virus infections. By expanding the repertoire of ALI culture-adapted in vitro assays, we hope to facilitate the widespread adoption of this valuable culture system for mechanistic investigations of respiratory viral infections or other epithelial-pathogen models.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.015 |
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".