A network of CD163 <sup>+</sup> macrophages monitors enhanced permeability at the blood-dorsal root ganglion barrier
Bibliographic record
Abstract
Abstract In dorsal root ganglia (DRG), macrophages reside in close proximity to sensory neurons, and their functions have largely been explored in the context of pain, nerve injury and repair. In this study, however, we discovered that the majority of macrophages in DRGs are in direct contact with the vasculature where they constantly monitor the circulation, efficiently phagocytosing proteins and macromolecules from the blood. Characterization of the DRG endothelium revealed a specialized vascular network spanning the arteriovenous axis, which gradually transformed from a barrier type endothelium in arteries to a highly permeable endothelium in veins. Macrophage phagocytosis spatially aligned with peak endothelial permeability and we identified caveolar transcytosis as a mechanism regulating endothelial permeability. Profiling of the DRG immune landscape revealed two subsets of perivascular macrophages with distinct transcriptome, turnover and function. CD163 expressing macrophages self-maintained locally, specifically participated in vasculature monitoring, displayed distinct responses during peripheral inflammation and were conserved in mouse and Man. Our work provides a molecular explanation for the permeability of the blood-DRG barrier and identifies an unappreciated role of macrophages as integral components of the DRG-neurovascular unit.
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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 | 0.000 |
| 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".