IFN‐β regulates CD73 and adenosine expression at the blood–brain barrier
Bibliographic record
Abstract
IFN-beta treatment reduces the relapse rate in MS but its mechanism of action remains incompletely understood. Our aim was to clarify the beneficial effect of IFN-beta in the treatment of MS. We assessed the influence of IFN-beta treatment on (i) CD73 expression on the surface of primary cultures of human blood-brain barrier endothelial cells (BBB-EC) and human astrocytes using immunofluorescence staining and flow cytometry, (ii) transmigration of CD4+ T lymphocytes using an in vitro model of BBB and (iii) CD73 enzyme activity, i.e. ecto-5'-nucleotidase activity in the serum of MS patients using a radiochemical assay. IFN-beta increases the expression of ecto-5'-nucleotidase both on BBB-EC and astrocytes. As a consequence, lymphocyte transmigration through BBB-EC is reduced. Importantly, this reduction can be reversed using alpha,beta-methyleneadenosine-5'-diphosphate, a specific inhibitor of ecto-5'-nucleotidase. CD73 is strongly expressed in microvasculature in samples of postmortem MS brain and, moreover, in the majority of MS patients there was a clear upregulation both in the soluble serum ecto-5'-nucleotidase activity and skin microvascular CD73 expression after IFN-beta treatment. Upregulation of ecto-5'-nucleotidase and a subsequent increase in adenosine production might contribute to the beneficial effects of IFN-beta on MS via enhancing the endothelial barrier function.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".