The Beta Chemokines CCL4 and CCL5 Enhance Adhesion of Specific CD4+ T Cell Subsets to Human Brain Endothelial Cells
Bibliographic record
Abstract
Chemokines are key mediators of inflammation, acting as subset-specific chemoattractants and activators of leukocytes. In the present study we investigated the effects of chemokine concentration gradients on CD4+ T cell (TC4) adhesion to human brain microvessel endothelial cells (HBMECs) in vitro. CCL4 or CCL5 were placed in a double chamber chemotaxis system beneath confluent resting HBMEC monolayers or cultures co-incubated with TNF-alpha and IFN-gamma to mimic an inflammatory milieu. Chemokines readily diffused across activated HBMEC monolayers while binding to the sub-endothelial regions, establishing a chemotactic and haptotactic gradient. Naïve or resting TC4 adhered poorly to resting HBMECs compared to memory or recently activated TC4, but all subsets adhered more readily to cytokine-treated HBMECs. Chemokine gradients (10-100 ng/ml) of both CCL4 and CCL5 significantly enhanced the adhesion of memory and recently activated TC4 to cytokine-treated HBMECs, as much as doubling adhesion in a manner that correlated with chemokine receptor expression. Neither chemokine influenced adhesion to resting HBMEC monolayers nor the adhesion of resting or naïve TC4. These findings emphasize the role and importance of CNS-derived beta-chemokines in regulating the traffic of recently activated TC subsets (those previously localized to the CNS in vivo) across cytokine-activated cerebral endothelium in inflammatory diseases.
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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".