Macrophage-augmented organoids recapitulate the complex pathophysiology of viral diseases and enable development of multitarget therapeutics
Bibliographic record
Abstract
Abstract The pathophysiology of viral diseases is complex, and often evokes strong inflammatory responses and tissue damage. Currently available in vitro models mainly recapitulate the viral life cycle per se , but fail to model immune cell-mediated pathogenesis. Here we build macrophage-augmented organoids (MaugOs) by integrating macrophages into organoids that are cultured from human liver tissues. We test the infections of two RNA viruses—hepatitis E virus (HEV) and SARS-CoV-2, and one DNA virus—monkeypox virus (MPXV), which either primarily or secondarily affect the human liver. In all three viral disease modalities, MaugOs recapitulate both infection and the resulting inflammatory response, albert to different levels. Intriguingly, this system showcases the ability to dissect the multifunctional role of human bile on HEV replication and inflammatory response through distinct mechanisms of action. MaugOs especially when integrated with pro-inflammatory macrophages recapitulate a prominent feature of inflammatory cell death triggered by HEV infection. Furthermore, we demonstrate a proof-of-concept in MaugOs to develop multitarget therapeutic strategies that simultaneously target the virus, inflammatory response, and the resultant inflammatory cell death.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".