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 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) | 0.000 | 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) | 0.001 | 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 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".