Innate immune response of human pluripotent stem cell-derived airway epithelium
Bibliographic record
Abstract
The acquisition of innate immune response is requisite to having bona fide differentiation of airway epithelium. Procedures developed to differentiate lung airway from human pluripotent stem cells (hPSCs) have demonstrated anecdotal evidence for innate immune response, but an in-depth exploration of response levels is lacking. Herein, using an established method of airway epithelial generation from hPSCs, we show that hPSC-derived epithelial cells are able to up-regulate expression of TNFα, IL8 and IL1β in response to challenge with bacterial endotoxin LPS, but lack response from genes associated with innate immune response in other cell types. Further, stimulation of cells with TNF-α resulted in auto-induction of TNFα transcript, as well as cytokine responses of IL8 and IL1β. The demonstration of innate immune induction in hPSC-derived airway epithelia gives further strength to the functionality of in vitro protocols aimed at generating differentiated airway cells that can potentially be used in a translational setting. Finally, we propose that innate immune challenge of airway epithelium from human pluripotent stem cell sources be used as a robust validation of functional in vitro differentiation.
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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".