Regulation of Multidrug Resistance P‐Glycoprotein in the Developing Blood–Brain Barrier: Interplay between Glucocorticoids and Cytokines
Bibliographic record
Abstract
P-glycoprotein (P-gp) encoded by Abcb1 provides protection to the developing brain from xenobiotics. P-gp in brain endothelial cells (BECs) derived from the developing brain microvasculature is up-regulated by glucocorticoids and inhibited by pro-inflammatory cytokines in vitro. However, little is known about how prenatal maternal glucocorticoid treatment can affect Abcb1/P-gp function and subsequent cytokine regulation in foetal BECs. We hypothesised that glucocorticoid exposure increases Abcb1/P-gp in the foetal brain microvasculature and enhances the sensitivity of Abcb1/P-gp in BECs to the inhibitory effects of cytokines. BECs isolated from dexamethasone- or vehicle-exposed foetal guinea pigs were cultured and treated with interleukin-1β, interleukin-6 or tumour necrosis factor-α, and Abcb1/P-gp expression and function were assessed. Prenatal dexamethasone exposure significantly increased Abcb1/P-gp expression/activity and cytokine receptor levels in BECs of the foetal brain microvasculature. Foetal dexamethasone exposure in vivo also increased the subsequent responsiveness of BECs to pro-inflammatory cytokines in vitro. In conclusion, maternal treatment with synthetic glucocorticoids appears to prematurely mature P-gp mediated drug resistance at the foetal BBB in vivo and profoundly impact the subsequent responsiveness of P-gp to pro-inflammatory cytokines in the foetal BEC. The significance of these findings to foetal brain protection against xenobiotics and other P-gp substrates in vivo requires further elaboration. However, the results of the present study may have implications for human pregnancy and foetal brain protection, particularly in cases of preterm birth combined with infection.
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".