Cytokines, chemokines, and cytokine receptors in human microglia
Bibliographic record
Abstract
Enriched populations of human microglial cells were isolated from mixed cell cultures prepared from embryonic human telencephalon tissues. Human microglial cells exhibited cell type-specific antigens for macrophage-microglia lineage cells including CD11b (Mac-1), CD68, B7-2 (CD86), HLA-ABC, HLA-DR and ricinus communis aggulutinin lectin-1 (RCA-1), and actively phagocytosed latex beads. Gene expression and protein production of cytokines, chemokines and cytokine/chemokine receptors were investigated in the purified populations of human microglia. Normal unstimulated human microglia expressed constitutively mRNA transcripts for interleukin- 1beta (IL-1beta) -6, -8, -10, -12, -15, tumor necrosis factor-alpha (TNF-alpha), macrophage inflammatory protein-1alpha (MIP-1alpha), MIP-1beta, and monocyte chemoattractant protein-1 (MCP-1), while treatment with lipopolysaccharide (LPS) or amyloid beta peptides (Abeta) led to increased expression of mRNA levels of IL-8, IL-10, IL-12, TNF-alpha, MIP-1alpha, MIP-1beta, and MCP-1. Human microglia, in addition, expressed mRNA transcripts for IL-1RI, IL-1RII, IL-5R, IL-6R, IL-8R, IL-9R, IL-10R, IL-12R, IL-13R, and IL-15R. Enzyme-linked immunosorbent assays (ELISA) showed increased protein levels in culture media of IL-1beta, IL-8, TNF-alpha, and MIP-1alpha in human microglia following treatment with LPS or Abeta. Increased TNF-alpha release from human microglia following LPS treatment was completely inhibited with IL-10 pretreatment, but not with IL-6, IL-9, IL-12, IL-13, or transforming growth factor-beta (TGF-beta). Present results should help in understanding the basic microglial biology, but also the pathophysiology of activated microglia in neurological diseases such as Alzheimer disease, Parkinson disease, Huntington disease, amyotrophic lateral sclerosis, stroke, and neurotrauma.
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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 | 0.001 |
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".