3D Tissue Models as an Effective Tool for Studying Viruses and Vaccine Development
Bibliographic record
Abstract
The recent SARS-CoV-2 outbreak has researchers working tirelessly to understand the virus' pathogenesis and develop an effective vaccine. The urgent need for rapid development and deployment of such a vaccine has illustrated the limitations of current practices, and it has highlighted the need for alternative models for early screening of such technologies. Traditional 2D cell culture does not accurately capture the effects of a physiologically relevant environment as they fail to promote appropriate cell-cell and cell-environment interactions. This inability to capture the intricacies of the in vivo microenvironment prevents 2D cell cultures from demonstrating the necessary properties of native tissues required for the standard infection mechanisms of the virus, thus contributing the high failure rate of drug discovery and vaccine development. 3D cell culture models can bridge the gap between conventional cell culture and in vivo models. Methods such as 3D bioprinting, spheroids, organoids, organ-on-chip platform, and rotating wall vessel bioreactors offer ways to produce physiologically relevant models by mimicking in vivo microarchitecture, chemical gradients, cell–cell interactions and cell–environment interactions. The field of viral biology currently uses 3D cell culture models to understand the interactions between viruses and host cells, which is crucial knowledge for vaccine development. In this review, we discuss how 3D cell culture models have been used to investigate disease pathologies for coronaviruses and other viruses such as Zika Virus, Hepatitis, and Influenza, and how they may apply to drug discovery and vaccine development.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".