Expression of the β-Chemokines RANTES and MIP-1β by Human Brain Microvessel Endothelial Cells in Primary Culture
Bibliographic record
Abstract
The mechanisms that regulate inflammatory cell recruitment across the blood-brain barrier (BBB) during CNS inflammation have not been fully characterized. Likely players in this process include the chemokines, small secondary messengers of inflammation capable of subset-specific leukocyte activation and chemoattraction. Primary cultures of human brain microvessel endothelial cells (HBMEC) were examined for their in vitro expression of the beta chemokines RANTES and MIP-1beta. Untreated HBMEC expressed low levels of RANTES and MIP-1beta RNA that were significantly upregulated following cytokine treatment. Parallel studies performed on human umbilical vein endothelial cells (HUVEC) showed induction of RANTES but not MIP-1beta RNA. Following stimulation with LPS, TNF-alpha, IFN-gamma, and IL-1beta alone or in combination, HBMEC released significant amounts of RANTES and MIP-1beta into the culture supernatants. RANTES secretion by HUVEC could be induced only with TNF-alpha/IFN-gamma. Both RANTES and MIP-1beta were detected by immunocytochemistry on the apical and basal surfaces of HBMEC, as well as bound to basal lamina-like material under the basal cell surface. Cytokine stimulation induced significant increase of RANTES and MIP-1beta molecules associated with the EC surface and subendothelial matrix. The expression of RANTES and MIP-1beta by HBMEC suggests that these chemokines may play an important role in mediating inflammatory responses and leukocyte trafficking across the BBB.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".