Phagocytosis of apoptotic inflammatory cells by microglia and its therapeutic implications: Termination of CNS autoimmune inflammation and modulation by interferon‐beta
Bibliographic record
Abstract
Apoptosis of autoaggressive T-cells in the CNS is an effective, noninflammatory mechanism for the resolution of T-cell infiltrates, contributing to clinical recovery in T-cell-mediated neuroinflammatory diseases. The clearance of apoptotic leukocytes by tissue-specific phagocytes is critical in the resolution of the inflammatory infiltrate and leads to a profound downregulation of phagocyte immune functions. Adult human microglia from surgically removed normal brain tissue was used in a standardized, light-microscopic in vitro phagocytosis assay of apoptotic autologous peripheral blood-derived mononuclear cells (MNCs). Microglia from five different patients had a high capacity for the uptake of apoptotic MNCs in contrast to nonapoptotic target cells with the phagocytosis rate for nonapoptotic MNCs amounting to only 61.6% of the apoptotic MNCs. A newly described phosphatidylserine receptor, critical in the phagocytosis of apoptotic cells by macrophages, is also expressed at similar levels on human microglia. The effects of the therapeutically used immunomodulatory agent interferon-beta (IFNbeta) were investigated using Lewis rat microglia and apoptotic, encephalitogenic, myelin basic protein-specific autologous T-cells. Also, rat microglia had a high capacity to phagocytose apoptotic T-cells specifically. IFNbeta increased the phagocytosis of apoptotic T-cells to 36.8% above the untreated controls. The enhanced phagocytic activity was selective for apoptotic T-cells and was not mediated by increased IL-10 secretion. Apoptotic inflammatory cells may be efficiently and rapidly removed by microglial cells in the autoimmune-inflamed human CNS. The in vitro increase of phagocytosis by IFNbeta merits further investigations whether this mechanism could also be therapeutically exploited.
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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".