Generation of apical-out nasal organoids to facilitate viral infection and drug screening
Bibliographic record
Abstract
Abstract Advanced culture systems such as organoids can serve as powerful platforms to study epithelial physiology, as they recapitulate the organisation and many key functions of the tissue of origin. The nasal epithelium is the first respiratory epithelium that is exposed to inhaled airborne pathogens. As a result, it is crucial to model host-pathogen interactions occurring in this tissue. To facilitate the efficient modelling of these interactions, we have developed a method to generate de novo apical-out nasal organoids from nasal epithelial cell aggregates. Optimisation of this method revealed a stark tissue-specific effect of the culture temperature, as apical-out nasal organoids were generated in much higher efficiency at 32.5 ° C, compared to more widely used temperatures of 37°C. These organoids are composed of ciliated, basal and goblet cells and are produced in a completely standardised and scalable manner, devoid of any extracellular matrix hydrogel. Moreover, they displayed high homogeneity in size and cellular composition, as well as susceptibility to viral infections and capability to model antiviral drug responses. Here, we describe a method for the efficient and reproducible generation of apical-out nasal organoids with high potential to be utilised in host-pathogen interaction studies and personalised medicine from easy-to-access nasal swabs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".