The BET inhibitor apabetalone decreases neuroendothelial proinflammatory activation<i>in vitro</i>and in a mouse model of systemic inflammation
Bibliographic record
Abstract
Abstract Brain vascular inflammation is characterized by endothelial activation and immune cell recruitment to the blood vessel wall, potentially causing a breach in the blood – brain barrier, brain parenchyma inflammation, and a decline of cognitive function. The clinical-stage small molecule, apabetalone, reduces circulating vascular endothelial inflammation markers and improves cognitive scores in elderly patients by targeting epigenetic regulators of gene transcription, bromodomain and extraterminal proteins. However, the effect of apabetalone on cytokine-activated brain vascular endothelial cells (BMVECs) is unknown. Here, we show that apabetalone treatment of BMVECs reduces hallmarks of in vitro endothelial activation, including monocyte chemoattractant protein-1 (MCP-1) and RANTES chemokine secretion, cell surface expression of endothelial cell adhesion molecule VCAM-1, as well as endothelial capture of THP-1 monocytes in static and shear stress conditions. Apabetalone pretreatment of THP-1 downregulates cell surface expression of chemokine receptors CCR1, CCR2, and CCR5, and of the VCAM-1 cognate receptor, integrin α4. Consequently, apabetalone reduces THP-1 chemoattraction towards soluble CCR ligands MCP-1 and RANTES, and THP-1 adhesion to activated BMVECs. In a mouse model of brain inflammation, apabetalone counters lipopolysaccharide-induced transcription of endothelial and myeloid cell markers, consistent with decreased neuroendothelial inflammation. In conclusion, apabetalone decreases proinflammatory activation of brain endothelial cells and monocytes in vitro and in the mouse brain during systemic inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".