Structural and functional variations in human bronchial epithelial cells cultured in air-liquid interface using different growth media
Bibliographic record
Abstract
The human bronchial epithelium is an important barrier tissue that is damaged or pathologically altered in various acute and chronic respiratory conditions. To represent the epithelial component of respiratory disease, it is essential to use a physiologically relevant model of this tissue. The human bronchial epithelium is a highly organized tissue consisting of a number of specialized cell types. Primary human bronchial epithelial cells (HBEC) can be differentiated into a mucociliated tissue in air-liquid interface (ALI) cultures using appropriately supplemented media under optimized growth conditions. We compared the histology, ciliary length, and function, diffusion, and barrier properties of HBEC from donors with no respiratory disease grown in two different media, PneumaCult-ALI or Bronchial Epithelial Differentiation Medium (BEDM). In the former group, HBEC have a more physiological pseudostratified morphology and mucociliary differentiation, including increased epithelial thickness, intracellular expression of airway-specific mucin protein MUC5AC, and total expression of cilia basal-body protein compared with cells from the same donor grown in the other medium. Baseline expression levels of inflammatory mediators, thymic stromal lymphopoietin (TSLP), soluble ST2, and eotaxin-3 were lower in PneumaCult-ALI. Additionally, the physiological cilia beat frequency and electrical barrier properties with transepithelial electrical resistance were significantly different between the two groups. Our study has shown that these primary cell cultures from the same donor grown in the two media possess variable structural and functional characteristics. Therefore, it is important to objectively validate primary epithelial cell cultures before experimentation to ensure they are appropriate to answer a specific scientific question.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".