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Record W4378213245 · doi:10.1038/s42003-023-04932-w

Spinal muscular atrophy-like phenotype in a mouse model of acid ceramidase deficiency

2023· article· en· W4378213245 on OpenAlexafffund
Murtaza S. Nagree, Jitka Rybová, Annie Kleynerman, Carissa J. Ahrenhoerster, Jennifer T. Saville, TianMeng Xu, Maxwell J. Bachochin, William M. McKillop, Michael W. Lawlor, Alexey V. Pshezhetsky, Elena Isaeva, Matthew D. Budde, Maria Fuller, Jeffrey A. Medin

Bibliographic record

VenueCommunications Biology · 2023
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversity of Toronto
FundersCanadian Institutes of Health ResearchChildren’s Hospital of Wisconsin Research InstituteMidwest Athletes Against Childhood CancerMedical College of Wisconsin
KeywordsSMA*Spinal muscular atrophyPhenotypeAtaxiaProgressive myoclonus epilepsyAtrophyPathologicalPathologySphingolipidMedicineBiologyDiseaseNeuroscienceGenetics

Abstract

fetched live from OpenAlex

Mutations in ASAH1 have been linked to two allegedly distinct disorders: Farber disease (FD) and spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME). We have previously reported FD-like phenotypes in mice harboring a single amino acid substitution in acid ceramidase (ACDase), P361R, known to be pathogenic in humans (P361R-Farber). Here we describe a mouse model with an SMA-PME-like phenotype (P361R-SMA). P361R-SMA mice live 2-3-times longer than P361R-Farber mice and have different phenotypes including progressive ataxia and bladder dysfunction, which suggests neurological dysfunction. We found profound demyelination, loss of axons, and altered sphingolipid levels in P361R-SMA spinal cords; severe pathology was restricted to the white matter. Our model can serve as a tool to study the pathological effects of ACDase deficiency on the central nervous system and to evaluate potential therapies for SMA-PME.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.336

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 science0.0010.001
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 head0.089
GPT teacher head0.374
Teacher spread0.285 · 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 designBench or experimental
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

Citations11
Published2023
Admission routes2
Has abstractyes

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