Differential Regulation of CD4+ T Cell Adhesion to Cerebral Microvascular Endothelium by the β-Chemokines CCL2 and CCL3
Bibliographic record
Abstract
In Multiple sclerosis (MS), circulating lymphocytes cross the blood-brain barrier (BBB) and accumulate at sites of antigenic challenge. This process depends on specific interactions between lymphocytes and cerebral microvascular endothelium that involve endothelial activation by cytokines and the presence of chemokines. Chemokines play a key role in the orchestration of immune responses, acting both as chemoattractants and activators of leukocyte subsets. In the present study, we investigated the effects of the beta-chemokines, CCL2 and CCL3, on the adhesion of CD4+ T cell subsets to human brain microvessel endothelial cells (HBMEC). Chemokines added to the lower compartment of a two-chamber chemotaxis system under confluent resting or cytokine-activated HBMEC, diffused through the culture substrate and bound to the basal surface of HBMEC. The low rate of adhesion of naïve, resting and memory CD4+ T cells to resting HBMEC was significantly upregulated following treatment of HBMEC with TNF-alpha and IFN-g. Recently activated CD4+ T cells readily adhered to resting monolayers. Concentration gradients of CCL2 upregulated the adhesion of activated CD4+ T cells to cytokine treated but not resting HBMEC. The presence of CCL3 in the lower chamber increased the adhesion of memory T cells to both unstimulated and cytokine-treated HBMEC. These findings emphasize the importance of brain endothelial cell activation and the role of CCL2 and CCL3 in regulating the adhesion of CD4+ T cell subsets to BBB endothelium, thus contributing to the specificity of immune responses in MS.
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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".