Expression of the β-Chemokines RANTES and MIP-1β by Human Brain Microvessel Endothelial Cells in Primary Culture
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it