SIMULATING ACUTE REJECTION IN ARTIFICIAL LIFE USING A COMPUTER PROGRAM
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».