Regulation of dendritic cell adhesion to human cerebral endothelium by endothelial cell adhesion molecules and their ligands
Bibliographic record
Abstract
Dendritic cells (DC) have recently been identified in central nervous system (CNS) inflammatory diseases and tumours, and are likely involved in the pathogenesis of CNS inflammation. The molecular mechanisms regulating the entry of DC into the brain are presently unknown. In this study we tested the hypothesis that DC are recruited to the brain across the blood brain barrier (BBB) and investigated the factors that mediate the in vitro adhesion of DC to human brain microvessel endothelial cells (HBMEC). DC were generated in vitro by culturing human blood monocytes in GM‐CSF and IL‐4. Maturation was induced by the addition of inflammatory cytokines. Immature and mature DC were characterized by flow cytometry and were incubated with resting or TNF‐α‐activated HBMEC for up to 1 hour. The results show that the adhesion of DC to resting HBMEC is minimal, and it significantly increases for both immature and mature DC upon cytokine activation (p<0.05). Furthermore, immature DC adhere to activated HBMEC to a greater extent than mature DC (p<0.05). This corresponds to a higher expression of adhesion receptors (CD209, CD11a, CD15s, CD49d) on immature DC compared to mature DC. These findings indicate that DC adhesion to HBMEC depends upon the maturation status of DC and is at least partly mediated by adhesion molecule‐ligand interactions at the BBB. Supported by MS Society of Canada and The Michael Smith Foundation for Health Research.
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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) | 0.002 | 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".