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Record W3033369584 · doi:10.1002/mus.26995

Whole blood survival motor neuron protein levels correlate with severity of denervation in spinal muscular atrophy

2020· article· en· W3033369584 on OpenAlexaff
Christiano R. R. Alves, Ren Zhang, Alec J. Johnstone, Reid Garner, Eric J. Eichelberger, Salomé Da Silva Duarte Lepez, Vivian Yi, Victoria Stevens, Rebekah Poxson, Rachel Schwartz, Phillip G. Zaworski, Kathryn J. Swoboda

Bibliographic record

VenueMuscle & Nerve · 2020
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsPortage College
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentCure SMABiogen
KeywordsSpinal muscular atrophyDenervationMotor neuronMedicineAtrophyProgressive muscular atrophyLower motor neuronPhysical medicine and rehabilitationAmyotrophic lateral sclerosisInternal medicineDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: We sought to determine whether survival motor neuron (SMN) protein blood levels correlate with denervation and SMN2 copies in spinal muscular atrophy (SMA). METHODS: Using a mixed-effect model, we tested associations between SMN levels, compound muscle action potential (CMAP), and SMN2 copies in a cohort of 74 patients with SMA. We analyzed a subset of 19 of these patients plus four additional patients who had been treated with received gene therapy to examine SMN trajectories early in life. RESULTS: Patients with SMA who had lower CMAP values had lower circulating SMN levels (P = .04). Survival motor neuron protein levels were different between patients with two and three SMN2 copies (P < .0001) and between symptomatic and presymptomatic patients (P < .0001), with the highest levels after birth and progressive decline over the first 3 years. Neither nusinersen nor gene therapy clearly altered SMN levels. DISCUSSION: These data provide evidence that whole blood SMN levels correlate with SMN2 copy number and severity of denervation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.266
Teacher spread0.230 · 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 teacher head, 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

Citations27
Published2020
Admission routes1
Has abstractyes

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