Towards a Humanized Mouse Model of Liver Stage Malaria Using Ectopic Artificial Livers
Bibliographic record
Abstract
The malaria liver stage is an attractive target for antimalarial development, and preclinical malaria models are essential for testing such candidates. Given ethical concerns and costs associated with non-human primate models, humanized mouse models containing chimeric human livers offer a valuable alternative as small animal models of liver stage human malaria. The best available human liver chimeric mice rely on cellular transplantation into mice with genetically engineered liver injury, but these systems involve a long and variable humanization process, are expensive, and require the use of breeding-challenged mouse strains which are not widely accessible. We previously incorporated primary human hepatocytes into engineered polyethylene glycol (PEG)-based nanoporous human ectopic artificial livers (HEALs), implanted them in mice without liver injury, and rapidly generated human liver chimeric mice in a reproducible and scalable fashion. By re-designing the PEG scaffold to be macroporous, we demonstrate the facile fabrication of implantable porous HEALs that support liver stage human malaria (P. falciparum) infection in vitro, and also after implantation in mice with normal liver function, 60% of the time. This proof-of-concept study demonstrates the feasibility of applying a tissue engineering strategy towards the development of scalable preclinical models of liver stage malaria infection for future applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".