Epithelial barrier function properties of the 16HBE14o- human bronchial epithelial cell culture model
Bibliographic record
Abstract
The human bronchial epithelial cell line, 16HBE14o- (16HBE), is widely used as a model for respiratory epithelial diseases and barrier function. During differentiation, transepithelial electrical resistance (TER) increased to approximately 800 Ohms × cm2, while 14C-d-mannitol flux rates (Jm) simultaneously decreased. Tight junctions (TJs) were shown by diffusion potential studies to be anion-selective with PC1/PNa = 1.9. Transepithelial leakiness could be induced by the phorbol ester, protein kinase C (PKC) activator, 12-O-tetradecanoylphorbol-13-acetate (TPA), and the proinflammatory cytokine, tumor necrosis factor-α (TNF-α). Basal barrier function could not be improved by the micronutrients, zinc, or quercetin. Of methodological significance, TER was observed to be more variable and to spontaneously, significantly decrease after initial barrier formation, whereas Jm did not significantly fluctuate or increase. Unlike the strong inverse relationship between TER and Jm during differentiation, differentiated cell layers manifested no relationship between TER and Jm. There was also much greater variability for TER values compared with Jm. Investigating the dependence of 16HBE TER on transcellular ion conductance, inhibition of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) chloride channel with GlyH-101 produced a large decrease in short-circuit current (Isc) and a slight increase in TER, but no significant change in Jm. A strong temperature dependence was observed not only for Isc, but also for TER. In summary, research utilizing 16HBE as a model in airway barrier function studies needs to be aware of the complexity of TER as a parameter of barrier function given the influence of CFTR-dependent transcellular conductance on TER.
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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 | 0.001 |
| 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".