Generation of Macrophages from Cynomolgus‐Monkey Bone Marrow as a Model to Evaluate Effects of Drugs on Innate Immunity
Bibliographic record
Abstract
Macrophages are innate immune cells that play important roles in various physiological and pathological processes. Evaluation of pro-inflammatory effects of drugs on macrophages has become commonplace in preclinical drug development prior to human clinical trials. Despite their body-wide distribution, tissue macrophages are often difficult to collect from large animals and humans in a noninvasive manner. Therefore, in vitro-differentiated macrophages are important tools to facilitate cross-species analysis of macrophage function. Although cynomolgus monkeys are an essential non-rodent species for preclinical research, in vitro differentiation of cynomolgus-monkey macrophages has been poorly characterized. In the present unit, we describe a protocol to differentiate cynomolgus-monkey macrophages from isolated bone marrow mononuclear cells (BMMCs). In contrast to monocytes, cynomolgus-monkey BMMCs show robust expansion in the presence of macrophage colony-stimulating factor in vitro, which allows expansion of many cells from a single animal donor. Macrophages differentiated from BMMCs retain many of the macrophage phenotypes and functions, including phagocytosis and cytokine release, and therefore can be used as a surrogate to assess effects of drugs on cynomolgus-monkey macrophages. © 2019 by John Wiley & Sons, Inc.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".