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Record W4386476155 · doi:10.21203/rs.3.rs-3296997/v1

Regional and cellular iron deposition patterns predict clinical subtypes of multiple system atrophy

2023· preprint· en· W4386476155 on OpenAlexaff
Seojin Lee, Iván Martínez-Valbuena, Anthony E. Lang, Gábor G. Kovács

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersEdmond J. Safra Philanthropic Foundation
KeywordsAtrophyDeposition (geology)PathologyMedicineBiologyPaleontology

Abstract

fetched live from OpenAlex

Abstract Background Multiple system atrophy (MSA) is a primary oligodendroglial synucleinopathy, characterized by elevated iron burden in early-affected subcortical nuclei. Although neurotoxic effects of brain iron deposition and its reciprocal relationship with α-synuclein pathology have been demonstrated, the exact role of iron dysregulation in MSA pathogenesis is unknown. In this regard, advancing the understanding of iron dysregulation at the cellular level is critical, especially in relation to α-synuclein cytopathology. Methods We performed the first cell type (α-synuclein-affected and -unaffected neurons, astroglia, oligodendrocytes, and microglia)-specific evaluation of MSA iron deposition in the globus pallidus (GP), putamen, and the substantia nigra (SN), using a combination of iron staining with immunolabelling on human post-mortem MSA brains. We evaluated selective regional and cellular vulnerability patterns to iron deposition distinctly in MSA-parkinsonian (MSA-P) and cerebellar (MSA-C) subtypes and explored possible underlying molecular pathways by mRNA expression analysis of key iron- and the closely related oxygen-homeostatic genes. Results MSA-P and MSA-C showed a distinct pattern of regional iron burden across the subcortical nuclei. We identified microglia as the major cell type accumulating iron in these regions of MSA brains, which was more distinct in MSA-P. MSA-C showed a more heterogenous cellular iron accumulation, in which astroglia showed greater or similar accumulation of iron. Notably, iron deposition was found outside the cellular bodies in the same regions and cellular iron burden minimally correlated with α-synuclein cytopathology. Gene expression analysis revealed dysregulation of oxygen, rather than of cellular iron, homeostatic genes. Importantly, hierarchal cluster analysis revealed pattern of cellular vulnerability to iron accumulation, rather than of α-synuclein pathology load in the subtype-related systems, to distinguish MSA subtypes. Conclusions We identified distinct regional, and for the first time, cellular distribution of subcortical iron deposition in MSA-P and MSA-C, and revealed cellular vulnerability pattern to iron deposition as a novel neuropathological characteristic that predicts MSA subtypes, distinctly from α-synuclein pathology. These findings support the role of iron dysregulation as an early effector of disease pathology in MSA. Our findings suggesting distinct iron-related pathomechanisms in MSA subtypes inform current efforts in iron chelation therapies at the disease and cellular-specific levels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.171
GPT teacher head0.400
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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