Development of a direct contact astrocyte-human cerebral microvessel endothelial cells blood–brain barrier coculture model
Bibliographic record
Abstract
OBJECTIVES: In conventional in-vitro blood-brain barrier (BBB) models, primary and immortalized brain microvessel endothelial cell (BMEC) lines are often cultured in a monolayer or indirect coculture or triculture configurations with astrocytes or pericytes, for screening permeation of therapeutic or potentially neurotoxic compounds. In each of these cases, the physiological relevancy associated with the direct contact between the BMECs, pericytes and astrocytes that form the BBB and resulting synergistic interactions are lost. We look to overcome this limitation with a direct contact coculture model. METHODS: We established and optimized a direct interaction coculture system where primary human astrocytes are cultured on the apical surface of a Transwell® filter support and then human cerebral microvessel endothelial cells (hCMEC/D3) seeded directly on the astrocyte lawn. KEY FINDINGS: The studies suggest the direct coculture model may provide a more restrictive and physiologically relevant model through a significant reduction in paracellular transport of model compounds in comparison with monoculture and indirect coculture. In comparison with existing methods, the indirect coculture and monoculture models utilized may limit cell-cell signaling between human astrocytes and BMECs that are possible with direct configurations. CONCLUSIONS: Paracellular permeability reductions with the direct coculture system may enhance therapeutic agent and potential neurotoxicant screening for BBB permeability better than the currently available monoculture and indirect coculture in-vitro models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".