An epithelial organoid model with Langerhans cells for assessing virus-host interactions
Bibliographic record
Abstract
Persistent infection with oncogenic human papillomavirus (HPV) may lead to cancer in mucosal and skin tissue. Consequently, HPV must have developed strategies to escape host immune surveillance. Nevertheless, most HPV infections are cleared by the infected host. Our laboratory investigates Langerhans cells (LCs), acting at the interface between innate and adaptive immunity. We hypothesize that this first line of defence is vital for potential HPV elimination. As an alternative to animal models, we use smaller-scale epithelial organoids grown from human primary keratinocytes derived from various anatomical sites. This approach is amenable to large sample sizes-an essential aspect for scientific rigour and statistical power. To evaluate LCs phenotypically and molecularly during the viral life cycle and onset of carcinogenesis, we have included an engineered myeloid cell line with the ability to acquire an LC phenotype. This model is accurately tailored for the crucial time-window of early virus elimination in a complex organism and will shed more light on our long-standing research question of how naturally occurring HPV variants influence disease development. It may also be applied to other microorganism-host interaction research or enquiries of epithelium immunobiology. Finally, our continuously updated pathogen-host analysis tool enables state-of-the-art bioinformatics analyses of next-generation sequencing data. This article is part of the theme issue 'Silent cancer agents: multi-disciplinary modelling of human DNA oncoviruses'.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".