Modelling neurodevelopment, neurodegeneration, and amyloid beta aggregation in the context of Alzheimer's using COBWEB
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease. It is a growing concern, demanding the attention of families, scientists, and pharmaceutical companies due to its devastating impacts on patients. The disease is believed to be triggered by pathogenic amyloid beta protein (Aβ) formations in the brain. In order to understand the protein production and the plaque formation in the AD brain, we specifically focused on the ‘Amyloid Cascade Hypothesis,’ which explains the biological pathways and the players in the disease. Focusing on the macro-side, we modelled the progression of AD from neurodevelopment (healthy brain) to neurodegeneration (the disease state) by using the agent based computer simulation program called COBWEB. Our model begins with healthy, developing neurons thriving in the hippocampus and cerebrospinal fluid (CSF) working efficiently. The brain ages throughout the adulthood phase. The onset of the disease and its progression is modelled with plaque formation, a decline in neuron counts, and an inefficient cleaning mechanism close to the end of the experiment. We conclude that our model fulfills its purpose: to provide a visual contrast between health and disease through the slow progression of AD in real time, increasing one’s understanding of this illness. Its accuracy is attributed to Aβ plaque formation, neuronal death, and CSF deterioration. Future projects include testing, designing, and refining new treatments using this model, diminishing the barrier to entry for new ideas, and providing a new tool for teaching AD.La maladie d’Alzheimer (MA) est une maladie neurodégénérative. Elle est un souci croissant, exigeant l’attention des familles, des scientifiques et des sociétés pharmaceutiques en raison de ses effets dévastateurs sur les patients. Nous pensons que la maladie est provoquée par la formation pathogénique de la protéine bêta amyloïde (Aß) dans le cerveau. Afin de comprendre la production de la protéine et la formation de plaques dans le cerveaud’un patient atteint de MA, nous avons mis l’accent spécifiquement sur l’hypothèse de la cascade amyloïde, ce qui explique les voies biologiques et les acteurs impliqués dans la maladie. En nous concentrant sur la macroscopie, nous avons modélisé la progression de la MA du début du neurodéveloppement (le cerveau en bonne santé) à la neurodégénérescence (l’état de la maladie) en utilisant le programme de simulation numérique basé sur un agent appelé COBWEB. Notre modèle commence avec des neurones qui se développent normalement, grandissant dans l’hippocampe et le liquide céphalo-rachidien (LCR) et travaillant efficacement. Le cerveau vieillit tout au long de la phase adulte. L’apparition de la maladie et de sa progression est modélisée avec la formation des plaques, une diminution du nombre neurones, et un mécanisme de nettoyage inefficace près de la fin. Nous concluons que notre modèle répond à son but de fournir un contraste visuel entre la santé et la maladie à travers la lente progression de la MA en temps réel, ce qui augmente notre compréhension de la MA. Sa précision est attribuée à la formation de plaques Aß, la mort neuronale et la détérioration du LCR. Les projets futurs incluent des tests, la conception et le raffinage de nouveaux traitements en utilisant ce modèle, ce qui diminue la barrière à l’entrée pour de nouvelles idées, et de fournir un nouvel outil pour enseigner la MA.
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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 | 0.001 | 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 | 0.001 |
| 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".