Organotypic human ex vivo models for coronavirus disease 2019 research and drug development
Bibliographic record
Abstract
Since the discovery of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, intense research efforts on an unprecedented scale have focused on the study of viral entry mechanisms and adaptive immunity. While the identification of angiotensin-converting enzyme 2 (ACE2) and other co-receptors has elucidated the molecular and structural basis for viral entry, the pathobiological mechanisms of SARS-CoV-2 in human tissues are less understood. Recent advances in bioengineering have opened opportunities for the use of organotypic human tissue models to investigate host-virus interactions and test antiviral drug candidates in a physiological context. Although it is too early to accurately quantify the added value of these systems compared with conventional cell systems, it can be assumed that these advanced three-dimensional (3D) models contribute toward improved result translation. This mini-review summarizes recent work to study SARS-CoV-2 infection in human 3D tissue models with an emphasis on the pharmacological tools that have been developed to understand and prevent viral entry and replication.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".