“Ependymal‐in” Gradient of Thalamic Damage in Progressive Multiple Sclerosis
Notice bibliographique
Résumé
Leptomeningeal and perivenular infiltrates are important contributors to cortical grey matter damage and disease progression in multiple sclerosis (MS). Whereas perivenular inflammation induces vasculocentric lesions, leptomeningeal involvement follows a subpial “surface‐in” gradient. To determine whether similar gradient of damage occurs in deep grey matter nuclei, we examined the dorsomedial thalamic nuclei and cerebrospinal fluid (CSF) samples from 41 postmortem secondary progressive MS cases compared with 5 non‐neurological controls and 12 controls with other neurological diseases. CSF/ependyma‐oriented gradient of reduction in NeuN + neuron density was present in MS thalamic lesions compared to controls, greatest (26%) in subventricular locations at the ependyma/CSF boundary and least with increasing distance (12% at 10 mm). Concomitant graded reduction in SMI31 + axon density was observed, greatest (38%) at 2 mm from the ependyma/CSF boundary and least at 10 mm (13%). Conversely, gradient of major histocompatibility complex (MHC)‐II + microglia density increased by over 50% at 2 mm at the ependyma/CSF boundary and only by 15% at 10 mm and this gradient inversely correlated with the neuronal ( R = −0.91, p < 0.0001) and axonal ( R = −0.79, p < 0.0001) thalamic changes. Observed gradients were also detected in normal‐appearing thalamus and were associated with rapid/severe disease progression; presence of leptomeningeal tertiary lymphoid‐like structures; large subependymal infiltrates, enriched in CD20 + B cells and occasionally containing CXCL13 + CD35 + follicular dendritic cells; and high CSF protein expression of a complex pattern of soluble inflammatory/neurodegeneration factors, including chitinase‐3‐like‐1, TNFR1, parvalbumin, neurofilament‐light‐chains and TNF. Substantial “ependymal‐in” gradient of pathological cell alterations, accompanied by presence of intrathecal inflammation, compartmentalized either in subependymal lymphoid perivascular infiltrates or in CSF, may play a key role in MS progression. Summary for Social Media Imaging and neuropathological evidences demonstrated the unique feature of “surface‐in” gradient of damage in multiple sclerosis (MS) since early pediatric stages, often associated with more severe brain atrophy and disease progression. In particular, increased inflammation in the cerebral meninges has been shown to be strictly associated with an MS‐specific gradient of neuronal, astrocyte, and oligodendrocyte loss accompanied by microglial activation in subpial cortical layers, which is not directly related to demyelination. To determine whether a similar gradient of damage occurs in deep grey matter nuclei, we examined the potential neuronal and microglia alterations in the dorsomedial thalamic nuclei from postmortem secondary progressive MS cases in combination with detailed neuropathological characterization of the inflammatory features and protein profiling of paired CSF samples. We observed a substantial “subependymal‐in” gradient of neuro‐axonal loss and microglia activation in active thalamic lesions of progressive MS cases, in particular in the presence of increased leptomeningeal and cerebrospinal fluid (CSF) inflammation. This altered graded pathology was found associated with more severe and rapid progressive MS and increased inflammatory degree either in large perivascular subependymal infiltrates, enriched in B cells, or within the paired CSF, in particular with elevated levels of a complex pattern of soluble inflammatory and neurodegeneration factors, including chitinase 3‐like‐1, TNFR1, parvalbumin, neurofilament light‐chains and TNF. These data support a key role for chronic, intrathecally compartmentalized inflammation in specific disease endophenotypes. CSF biomarkers, together with advance imaging tools, may therefore help to improve not only the disease diagnosis but also the early identification of specific MS subgroups that would benefit of more personalized treatments. ANN NEUROL 2022;92:670–685
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 ».