Imaging the Role of Macrophage in Infection and Sterile Injury in Liver
Bibliographic record
Abstract
In the liver, populations of resident macrophage and recruited monocytes play key roles in both the clearance of infection and tissue repair. Kupffer cells are liver macrophage that reside directly within the bloodstream, adherent to the sinusoid walls, ideally positioned to detect and capture pathogens. Using intravital microscopy (IVM), we are able to directly visualize Kupffer cells in the liver of live mice and to study how these cells bind, and clear bacteria from the bloodstream. IVM has revealed a number of unique aspects of pathogen recognition including antibody‐dependent, complement‐dependent and antibody/complement‐independent mechanisms of pathogen capture. The specific mechanism used by the Kupffer cell appears to vary according to the pathogen involved and these findings provide great insight into the development of vaccination strategies and new antibody‐based therapies designed to target specific pathogens. In contrast to infectious disease, tissue repair following sterile injury in the liver does not depend on the resident Kupffer cells, but rather, is dependent on the recruitment of inflammatory monocytes. Using IVM and mice with fluorescent reporter genes for various monocyte populations, we have identified that these recruited monocytes clear cellular debris from the site of injury and then transition, in situ , from classic CCR2 high (Cx3CR1 low ) monocytes to reparative Cx3CR1 high (CCR2 low ) alternative monocytes that support tissue repair and wound healing. This transition of CCR2 high cells to Cx3CR1 high monocytes is driven by the presence specific cytokines including IL‐4 and IL‐10. Failure to recruit to the initial CCR2 high cells or inhibition of the CCR2 high to Cx3CR1 high phenotype transition results in abnormal tissue repair including defects in collagen deposition and delayed wound healing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".