Culturing of respiratory viruses in well‐differentiated pseudostratified human airway epithelium as a tool to detect unknown viruses
Bibliographic record
Abstract
BACKGROUND: Currently, virus discovery is mainly based on molecular techniques. Here, we propose a method that relies on virus culturing combined with state-of-the-art sequencing techniques. The most natural ex vivo culture system was used to enable replication of respiratory viruses. METHOD: Three respiratory clinical samples were tested on well-differentiated pseudostratified tracheobronchial human airway epithelial (HAE) cultures grown at an air-liquid interface, which resemble the airway epithelium. Cells were stained with convalescent serum of the patients to identify infected cells and apical washes were analyzed by VIDISCA-454, a next-generation sequencing virus discovery technique. RESULTS: Infected cells were observed for all three samples. Sequencing subsequently indicated that the cells were infected by either human coronavirus OC43, influenzavirus B, or influenzavirus A. The sequence reads covered a large part of the genome (52%, 82%, and 57%, respectively). CONCLUSION: We present here a new method for virus discovery that requires a virus culture on primary cells and an antibody detection. The virus in the harvest can be used to characterize the viral genome sequence and cell tropism, but also provides progeny virus to initiate experiments to fulfill the Koch's postulates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".