SPARC expression by cerebral microvascular endothelial cells in vitro and its influence on blood-brain barrier properties
Bibliographic record
Abstract
BACKGROUND: SPARC (secreted protein acidic and rich in cysteine) is a nonstructural, cell-matrix modulating protein involved in angiogenesis and endothelial barrier function, yet its potential role in cerebrovascular development, inflammation, and repair in the central nervous system (CNS) remains undetermined. METHODS: This study examines SPARC expression in cultured human cerebral microvascular endothelial cells (hCMEC/D3)-an in vitro model of the blood-brain barrier (BBB)-as they transition between proliferative and barrier phenotypes and encounter pro-inflammatory stimuli. SPARC protein levels were quantified by Western blotting and immunocytochemistry and messenger RNA (mRNA) by RT-PCR. RESULTS: Constitutive SPARC expression by proliferating hCMEC/D3s is reduced as cells mature and establish a confluent monolayer. SPARC expression positively correlated with the proliferation marker Ki-67 suggesting a role for SPARC in cerebrovascular development. The pro-inflammatory molecules tumor necrosis factor-α (TNF-α) and endotoxin lipopolysaccharide (LPS) increased SPARC expression in cerebral endothelia. Interferon gamma (IFN-γ) abrogated SPARC induction observed with TNF-α alone. Barrier function assays show recombinant human (rh)-SPARC increased paracellular permeability and decreased transendothelial electrical resistance (TEER). This was paralleled by reduced zonula occludens-1 (ZO-1) and occludin expression in hCMEC/D3s exposed to rh-SPARC (1-10 μg/ml) compared with cells in media containing a physiological dose of SPARC. CONCLUSIONS: Together, these findings define a role for SPARC in influencing cerebral microvascular properties and function during development and inflammation at the BBB such that it may mediate processes of CNS inflammation and repair.
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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".