A quantitative comparison of rates of phagocytosis and digestion of apoptotic cells by macrophages from normal (BALB/c) and diabetes-prone (NOD) mice
Bibliographic record
Abstract
Macrophages play an important role in clearing apoptotic debris from tissue. Defective or reduced clearance, seen, for instance, in non-obese diabetic (NOD) mice, has been correlated with initiation of autoimmune (Type 1) diabetes (T1D) (O'Brien BA, Huang Y, Geng X, Dutz JP, Finegood DT. Diabetes 51: 2481-2488, 2002). To validate such a link, it is essential to quantify the reduced clearance (for example, by comparison to BALB/c control mice) and to determine which elements of that clearance are impaired. Recently, we fit data for the time course of in vitro macrophage feeding experiments to basic models of macrophage clearance dynamics, thus quantifying kinetics of uptake and digestion of apoptotic cells in both mouse strains (Marée AFM, Komba M, Dyck C, Łabeçki M, Finegood DT, Edelstein-Keshet L. J Theor Biol 233: 533-551, 2005). In the cycle of modeling and experimental investigation, we identified the importance of 1) measuring short-, intermediate-, and long-time data (to increase the accuracy of parameter fits), and 2) designing experiments with distinct observable regimes, including engulfment-only and digestion-only phases. Here, we report on new results from experiments so designed. In comparing macrophages from the two strains, we find that NOD macrophage engulfment of apoptotic cells is 5.5 times slower than BALB/c controls. Significantly, our new data demonstrate that digestion is at least two times slower in NOD, in contrast with previous conclusions. Moreover, new data enable us to detect an acceleration in engulfment (after the first engulfment) in both strains, but much smaller in NOD macrophages.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".