Epithelial stratification shapes infection dynamics
Bibliographic record
Abstract
Infections of stratified epithelia collectively represent a large burden on global health. Experimental models provide a means to understand how the cell dynamics themselves influence the outcomes of these infections. Mathematical approaches are needed to improve quantification and theoretical advancement of these complex systems. Here, we develop a general ecology-inspired model for stratified epithelial dynamics, which allows us to simulate infections and to estimate parameters that are difficult to measure with organotypic cell cultures. To explore how epithelial cell dynamics affect infection dynamics, we focus on two contrasting pathogens of the cervicovaginal epithelium: Chlamydia trachomatis and Human papillomaviruses. We find that key infection symptoms stem from differential interactions with the layers, while clearance and pathogen burden are bottom-up processes. Cell protective responses to infections (e.g. increased cell proliferation) generally lowered pathogen load but there were specific effects based on infection strategies. These generic responses by the epithelium, then, will have varying results depending on the pathogen’s infection strategy. Our modeling approach opens new perspectives for 3D tissue culture experimental systems of infections and, more generally, for developing and testing hypotheses related to infections of stratified epithelia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".