Fractalkine-induced activation of the phosphatidylinositol-3 kinase pathway attentuates microglial activation in vivo and in vitro.
Bibliographic record
Abstract
Several neurodegenerative disorders are associated with\nevidence of inflammation, one feature of which is increased\nactivation of microglia, the most likely cellular source of\ninflammatory cytokines like interleukin-1b. It is now recognized\nthat interaction of microglia with other cells contributes\nto maintenance of microglia in a quiescent state and the\ncomplementary distribution of the chemokine, fractalkine\n(CX3CL1) on neurons and its receptor (CX3CR1) on microglia,\nsuggests that this interaction may play a role in modulating\nmicroglial activation. Here we demonstrate that both\nsoluble and membrane-bound fractalkine attenuate lipopolysaccharide-\ninduced microglial activation in vitro. We also\nshow that fractalkine expression is reduced in the brain of\naged rats and this is accompanied by an age-related increase\nin microglial activation. Treatment of aged rats with\nfractalkine attenuates the age-related increase in microglial\nactivation and the evidence indicates that fractalkine-induced\nactivation of the phosphatidylinositol-3 kinase pathway is required\nto maintain microglia in a quiescent state both in vivo\nand in vitro.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".