Embryonic-Derived Myb− Macrophages Enhance Bacterial Clearance and Improve Survival in Rat Sepsis
Bibliographic record
Abstract
"peritoneal-like" macrophages attenuate abdominal fecal sepsis. Directed differentiation of rodent pluripotent stem cells (PSCs) was used in factor-defined media to produce embryonic-derived large "peritoneal-like" macrophages (Ed-LPM) that expressed peritoneal macrophage markers and demonstrated phagocytic capacity. Preclinical in vivo studies determined Ed-LPM efficacy in rodent abdominal fecal sepsis with or without Meropenem. Ex vivo studies explored the mechanism and effects of Ed-LPM on host immune cell number and function, including phagocytosis, reactive oxygen species (ROS) production, efferocytosis and apoptosis. Ed-LPM reduced sepsis severity by decreasing bacterial load in the liver, spleen and lungs. Ed-LPM therapy significantly improved animal survival by ~30% and reduced systemic bacterial burden to levels comparable to Meropenem therapy. Ed-LPM therapy decreased peritoneal TNFα while increasing IL-10 concentrations. Ed-LPMs enhanced peritoneal macrophage phagocytosis of bacteria, increased macrophage production of ROS and restored homeostasis via apoptosis and efferocytosis-induced clearance of neutrophils. In conclusion, Ed-LPM reduced systemic sepsis severity, improved survival and reduced bacterial load by enhancing peritoneal macrophage bacterial phagocytosis and killing and clearance of intra-peritoneal neutrophils. Macrophage therapy may be a potential strategy to address sepsis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".