Hybrid Cellular Automaton for Field Cancerization
Notice bibliographique
Résumé
Field Cancerization is a hypothesis for the formation of cancer in certain types of tissues. It proposes the idea that a tumour can form in a “field” of cells that are predetermined for the development of cancer. Further, it is hypothesized that these fields are mainly caused by the onslaught of carcinogens on the tissue. Lastly, field cancerization proposes that tumour recurrence is related to the tumour being excised without fully removing the surrounding field. The model we propose is a hybrid cellular automaton (CA) used to verify the previously stated propositions and determine how long cancer development will take. The CA is considered to be hybrid due to its’ rule depending on the results of partial differential equations (PDEs) and a multi-layer perceptron (MLP). The PDEs represent the spread of carcinogens in the tissue, while the MLP computes the effects of the carcinogens on the gene expression of the genes related to cancer development in the tissue under consideration. We apply the model to field cancerization of the tongue. Most of the parameters of the model were chosen and are not based upon real data, as the necessary data was not available. This includes the choice of substituting nicotine, which is a mutagen but not currently listed as a carcinogen, to represent the carcinogen impacts of smoking tobacco. According to Health Canada tobacco contains over 4,000 chemicals, of which more than 70 are carcinogens. Many researchers are investigating how nicotine contributes to the development of cancer due to its use in non-tobacco products such as e-cigarettes and nicotine patches. One such study by Sanner & Grimsrud suggests that nicotine has several cancer-causing effects including speeding up cell growth, it is poisonous to cells, it kick-starts a process that is an important step in the path toward malignant cell growth, and it decreases the tumour suppressor CHK2. Therefore, we considered the readily available data with regards to nicotine as an appropriate choice to substitute for the over 70 carcinogens in tobacco. The other carcinogen considered in this thesis is ethanol to represent alcohol consumption. It was found that nicotine was a more potent carcinogen than ethanol. The combined impact of both ethanol and nicotine resulted in more aggressive cancer growth. It was verified that removing the field results in recurrence taking longer to occur than if the field is not removed. We also tracked cell lineages and found that as the field develops, the number of distinct cell lines decreases. Finally, we found that most tumour masses formed via polyclonal origin instead of monoclonal origin, though both occur within the simulations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 tête enseignante, 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 ».