Investigating White Matter Inflammatory Cells and their Relationship with Beta‐Amyloid in Alzheimer's Disease
Notice bibliographique
Résumé
Alzheimer’s disease (AD) is a chronic neurodegenerative condition affecting millions of people worldwide. With an aging population, it is predicted that by 2050 the number of individuals living with AD will triple, resulting in an increased social and economic burden. Previous research has demonstrated that the pathological process leading to AD occurs years before a positive diagnosis. Therefore, identifying biomarkers that co‐occur within the early stage of AD progression and that predict worsening cognitive outcomes are critically needed. AD is characterized by the presence of beta‐amyloid plaques, neurofibrillary tangles and neuroinflammation. Clinically, being unable to recall new information is the most common outcome of AD. White matter inflammation is mediated by microglial activation and astrocytosis and are thought to be important underlying mechanisms involved in the pathogenesis of AD progression, while also being predictive of future cognitive decline. In a transgenic rat model of AD, it has been previously found that microglial activation within the white matter tracts was strongly associated with impairments in executive function. Age‐related differences in inflammatory cells have also been identified. One study found a significant increase in age‐associated white matter inflammatory cells in amyloid‐plaque negative rats at 7‐and‐8‐months of age. Sex‐and‐age related differences in inflammatory cells and amyloid‐beta have been identified, however, they remain poorly understood in the white matter. The purpose of this study was to investigate the sex‐specific‐age trajectory of white matter inflammatory cells in a wildtype rat model of normal aging and in two transgenic rat models of AD: one in an amyloid‐plaque negative, and one in an amyloid‐plaque positive environment. We hypothesized that microglial activation increases with normal aging and is further exacerbated by the presence of beta‐amyloid deposition. Male (n=5) and female (n=5) Fischer 344 wildtype, transgenic APP21 (amyloid‐plaque negative) and APP/PS1 (amyloid‐plaque positive) rats at 3, 9 and 15‐months of age were included in the experimental design. Immunohistochemical analyses were conducted to detect the presence of microglial activation, pro‐inflammatory M1 microglial activation, astrocytosis and beta‐amyloid. Results demonstrate unique age‐dependant trajectories in microglial activation, particularly in the corpus callosum, supraventricular corpus callosum and internal capsule of the wildtype, transgenic APP/21 and APP/PS1 rat genotypes. Given that only the APP/PS1 rats demonstrated age‐dependant plaque deposition, our results indicate that microglial activation within the white matter is associated with changes in amyloid‐plaque deposition. Future work will aim to better understand the mechanisms related to the relationship between white matter microglial activation, amyloid‐plaque deposition and cognitive impairment. A better understanding of this relationship can aid in the development of an earlier method of detecting AD, which in turn can better define a new therapeutic window of opportunity to target microglial activation and prevent neurodegeneration and cognitive decline associated with AD.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 ».