SIMULATING ACUTE REJECTION IN ARTIFICIAL LIFE USING A COMPUTER PROGRAM
Bibliographic record
Abstract
A315 Aims: Acute cellular allograft rejection (ACAR) is a well known, however, poorly understood phenomenon. The clinical and pathological characteristics are well described, however, why the phenomenon occurs has not been explained. ACAR has been studied in animals and the results of those studies have shown inconsistencies and paradoxes. Artificial Life simulations have succeeded in explaining some of the characteristics of complex systems such as economies, societies, etc. and it may offer an opportunity to explore the complex phenomenon of ACAR. Considering ACAR as the result of graft-host interaction, this can be simulated as two agents (graft, host) acting independently following simple rules. In this conception ACAR cannot be explained by the behavior of singular agents but rather by the result of their complex interactions, a phenomenon called “emergence”. Methods: The computer simulation program, “Star Logo 2.0” (Massachusetts Institute of Technology USA), was used to simulate the interaction between “allografts” and “leukocytes” in a desktop computer. In a two-dimensional space, two independent agents, G (Graft) and L (Leukocyte), were placed to interact following a program. This program resembled a simple “upstream” model of leukocyte migration to the host, and release of mediator (resembling lymphokine production). The variables were the number of host agents (lymphocytes) and the amount of mediator released (lymphokines). The system was run in an interative fashion, and the outcome was measured as the inflammatory activity (amount of accumulated mediator), the distribution of “inflammation”, and the ability or not of triggering a progressive “inflammatory” activity. Results: The program showed that the occurrence of inflammatory activity was determined by the system’s initial conditions. Once inflammation is triggered, even when local down regulation does exist, the process escalated in a logarithmic fashion. The distribution of activity was always focal. There were a number of agents and mediator thresholds below which there was no “inflammatory activity” seen. To downregulate the process, it was needed to decrease the number of agents and/or decrease the mediator in the system. This phenomenon was transient unless the effect of changing the conditions was kept for an undetermined period of time. In that case, the effect was permanent, even though the system was brought back to the initial (inflammatory) conditions. Conclusions: We presented a computer program that reproduced some of the most fundamental characteristics of acute cellular allograft rejection as the focal pattern of inflammation, the unpredictability of occurrence and the response to treatment. These results indicated that ACAR can be explained, at least in theory, as an emergent phenomenon, sensitive to initial conditions. The results pointed toward a “butterfly” phenomenon, described in Chaos Theory, as the trigger for ACAR and explained the response to treatment by changing the number and amount of the agents rather than changing their behavior (program).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".