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Record W4414190607 · doi:10.1101/2025.09.12.25335644

Fluid and Neuroimaging Biomarkers in Microgliopathy Colony-Stimulating Factor-1 Receptor-Related Disorders

2025· preprint· en· W4414190607 on OpenAlexaboutno aff
Tomasz Chmiela, Karen Jansen‐West, Judith A. Dunmore, Yuping Song, Audrey Strongosky, Sunil Gandhi, Gilana Pikover, Robert C. Spitale, Erik H. Middlebrooks, Leonard Petrucelli, Mercedes Prudencio, Zbigniew K. Wszołek

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsnot available
FundersRobert Packard Center for ALS Research, Johns Hopkins UniversityNational Institute of Neurological Disorders and StrokeNational Institute on AgingNational Institutes of HealthTarget ALSJohns Hopkins UniversityBrightFocus Foundation
KeywordsNeuroimagingBiomarkerAsymptomaticDiseaseCerebrospinal fluidAsymptomatic carrier

Abstract

fetched live from OpenAlex

Abstract Colony-stimulating factor 1 receptor-related disorder (CSF1R-RD) is a neurodegenerative condition characterized by rapid progression, leading to profound functional decline and ultimately resulting in a persistent vegetative state. Although an effective treatment option exists, there remains a lack of identified biomarkers capable of monitoring disease progression and detecting the earliest symptom onset in CSF1R pathogenic variant carriers, limiting the ability of clinicians to make informed decisions regarding patient care. This study aims to identify both fluid and neuroimaging biomarkers for CSF1R-RD that can inform the optimal timing of treatment administration to maximize therapeutic benefit, while also providing sensitive quantitative measurements to monitor disease progression. Our study compared neuroimaging and fluid (plasma and cerebrospinal fluid (CSF)) biomarkers across three distinct populations: asymptomatic CSF1R pathogenic variant carriers (N=14), symptomatic CSF1R pathogenic variant carriers (N=17), and healthy controls (N=30). We evaluated biomarker correlations with both an established (Montreal Cognitive Assessment (MoCA)) and a novel (CSF1R Clinical Severity Score (CCSS)) clinical diagnostic scale to investigate potential clinical utility. Additionally, we tested the relationship between select biomarkers and cortical thickness using 3D T1-weighted MPRAGE scans, providing a highly valuable physiological component to our analyses. Our results demonstrate that while plasma glial fibrillary acidic protein (GFAP) displays a high sensitivity for distinguishing early-stage CSF1R-RD patients from healthy controls, plasma neurofilament light chain (NfL) is more effective for tracking disease progression following the onset of symptoms. Overall, our study provides evidence for plasma NfL and GFAP as valuable biomarkers of earliest symptom onset and disease progression for CSF1R-RD One Sentence Summary This study identifies plasma biomarkers NfL and GFAP as promising tools to detect CSF1R-RD onset and progression, with potential to improve patient outcomes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.308
Teacher spread0.289 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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