Bacterial DNA promotes neutrophil adhesion to endothelial cells
Bibliographic record
Abstract
Bacterial DNA can be detected in the absence of bacteria in chronic inflammatory conditions. Bacterial DNA is emerging as an important regulator of functions of human neutrophil granulocytes. Bacterial DNA contains short sequences of nonmethylated CpG dinucleotides (CpG‐DNA) that are recognized by TLR‐9. We investigated the impact of CpG‐DNA on neutrophil adherence to human umbilical vein endothelial cells (HUVEC). HUVEC express TLR‐9 and respond to CpG‐DNA (0.2–20 μg/ml), but not to thymus DNA, with increased expression of E‐selectin and ICAM‐1 protein and mRNA. Methylation of cytosines in CpG‐DNA resulted in a complete loss of activity. Neutrophil adhesion to HUVEC activated with CpG‐DNA was markedly enhanced under low shear conditions. The number of adherent neutrophils was further enhanced when the adhesion assay was performed in the presence of CpG‐DNA, indicating that CpG‐DNA activates both neutrophils and HUVEC. The adhesive interactions were blocked by antibodies against CD18 (~70% inhibition), E‐selectin and L‐selectin. Combination of the three antibodies inhibited adhesion by 87%. These results identify HUVEC as a target for bacterial DNA and indicate that by enhancing neutrophil adhesion to endothelial cells bacterial DNA may contribute to maintenance and aggravation of the inflammatory response. (Supported by CIHR grant MOP‐67054).
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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) | 0.004 | 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".