Regulation of the phagocytic activity of astrocytes by neuroimmune mediators endogenous to the central nervous system
Bibliographic record
Abstract
The phagocytic activity of glial cells is essential for maintaining normal brain activity, and its dysfunction may contribute to the central nervous system (CNS) pathologies, including neurodegenerative diseases. Phagocytic activity is one of the well-established neuroimmune functions of microglia. Although emerging evidence indicates that astrocytes can also function as CNS phagocytes in humans and rodents, limited information is available about the molecular mechanism regulating this function. To address this knowledge gap, we studied modulation of the phagocytic activity of human U118 MG astrocytic cells and murine primary astrocytes by four CNS inflammatory mediators and bacterial endotoxin lipopolysaccharide (LPS). LPS and cytochrome c (CytC) upregulated, while interferon (IFN)-γ downregulated, phagocytosis of latex beads by human astrocytic cells and phagocytosis of synaptosomes by murine primary astrocytes. Interleukin (IL)-1β and tumor necrosis factor (TNF)-α had no effect on the phagocytic activity of human astrocytic cells but upregulated this function in murine astrocytes. Varying effects of combinations of the above inflammatory mediators were observed in these two cell types. LPS- and CytC-induced phagocytic activity of human astrocytic cells was partially mediated by activation of toll-like receptor 4 (TLR4). By monitoring other functions of astrocytes, we concluded there were no correlations between the effects of the mediators studied on astrocyte phagocytic activity and their secretion of cytokines, cytotoxins, or glutamate. Our study identified four candidate CNS regulators of astrocyte phagocytic activity. Future investigation of molecular mechanisms behind this regulation could identify novel therapeutic targets allowing modulation of this astrocyte-mediated clearance mechanism in CNS pathologies.
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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) | 0.000 | 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 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".