Phosphatidylinositol 3-kinase and mTOR mediate lipopolysaccharide-stimulated nitric oxide production in macrophages via interferon-β
Bibliographic record
Abstract
Bacterial lipopolysaccharide (LPS) elicits responses by macrophages that help the body repel infections. Recent evidence indicates that phosphatidylinositol 3-kinase (PI 3-kinase) may mediate some of these responses. Here, we show that exposing macrophages to LPS rapidly increased membrane-associated PI 3-kinase activity and also elevated p70 S6 kinase activity. Inhibitors of PI 3-kinase or the mammalian target of rapamycin (mTOR) fully blocked p70 S6 kinase activation, implying that this kinase is controlled by PI 3-kinase and mTOR. These inhibitors also substantially reduced LPS-induced nitric oxide (NO) production. This inhibition was, in part, attributable to impaired LPS-stimulated secretion of interferon-beta, an autocrine co-factor for NO production. However, the addition of exogenous interferon-beta did not fully restore NO production, indicating that the NO response was being inhibited by another mechanism as well. Together, these data suggest that PI 3-kinase, mTOR, and possibly p70 S6 kinase mediate LPS-induced NO production by regulating the secretion of interferon-beta and by a second undefined mechanism.
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 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 | 0.001 |
| 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".