Phagocytosis of Apoptotic Cells by Macrophages From NOD Mice Is Reduced
Bibliographic record
Abstract
Macrophages limit inflammatory responses by clearing apoptotic cells. Deficiencies in apoptotic cell phagocytosis have been linked to autoimmunity. In this study, we determined the efficiency with which macrophages from diabetes-prone NOD and diabetes-resistant NOR, Idd5, Balb/c, and C57BL/6 mice phagocytose apoptotic thymocytes and NIT-1 insulinoma cells. Peritoneal and bone marrow-derived macrophages from NOD mice engulfed fewer apoptotic thymocytes than macrophages from Balb/c mice (P < 0.05). Peritoneal macrophages from NOR and Idd5 NOD congenic mice were more proficient at engulfment than their NOD counterparts. Annexin V blockade diminished apoptotic thymocyte clearance and heat-labile serum factors augmented clearance. Binding of apoptotic thymocytes to NOD macrophages was also reduced, suggesting that the deficiency in phagocytosis may be partly attributable to a recognition defect. Peritoneal macrophages from female Balb/c and NOD mice were equally efficient in the engulfment of microspheres, suggesting that the phagocytic deficiency observed in NOD mice was specific for apoptotic cells. In summary, we have demonstrated a deficiency in phagocytic function of macrophages from NOD mice. Normal and diabetes-prone neonatal rodents have a wave of beta-cell apoptosis coincident with the onset of target organ inflammation. A constitutive defect in the clearance of apoptotic beta-cells may be contributory to the initiation of autoimmunity.
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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) | 0.001 | 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".