Development of a Zebrafish Sepsis Model for High-Throughput Drug Discovery
Bibliographic record
Abstract
Sepsis is a leading cause of death worldwide. Current treatment modalities remain largely supportive. Intervention strategies focused on inhibiting specific mediators of the inflammatory host response have been largely unsuccessful, a consequence of an inadequate understanding of the complexity and heterogeneity of the innate immune response. Moreover, the conventional drug-development pipeline is time-consuming and expensive, and the low success rates associated with cell-based screens underline the need for whole-organism screening strategies, especially for complex pathological processes. Here, we established a lipopolysaccharide (LPS)-induced zebrafish endotoxemia model, which exhibits the major hallmarks of human sepsis, including edema and tissue/organ damage, increased vascular permeability and vascular leakage accompanied by altered expression of cellular junction proteins, increased cytokine expression, immune cell activation and reactive oxygen species (ROS) production, reduced circulation and increased platelet aggregation. We tested the suitability of the model for phenotype-based drug screening using three primary readouts: mortality, vascular leakage and ROS production. Preliminary screening identified fasudil, a drug known to protect against vascular leakage in murine models, as a lead hit, thereby validating the utility of our model for sepsis drug screens. This zebrafish sepsis model has the potential to rapidly analyze sepsis-associated pathologies and cellular processes in the whole organism, as well as to screen and validate many compounds that can modify sepsis pathology in vivo.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".