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Record W2980435789 · doi:10.1002/acn3.50920

Low fat diets increase survival of a mouse model of spinal muscular atrophy

2019· article· en· W2980435789 on OpenAlexafffund
Marc‐Olivier Deguise, Lucia Chehadé, Alexandra Tierney, Ariane Beauvais, Rashmi Kothary

Bibliographic record

VenueAnnals of Clinical and Translational Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health ResearchUniversity of OttawaCure SMANational Institutes of HealthVanderbilt UniversityMuscular Dystrophy Association
KeywordsSMA*Spinal muscular atrophyMedicineSteatosisDyslipidemiaAtrophyKetone bodiesFatty liverInternal medicineEndocrinologyPhysiologyPathologyBioinformaticsGastroenterologyDiseaseBiologyMetabolism

Abstract

fetched live from OpenAlex

Abstract Spinal muscular atrophy (SMA) is a neuromuscular disorder leading to paralysis and death. Recent evidence shows increased susceptibility to dyslipidemia and liver steatosis in patients. Here, we provide evidence that low fat diets nearly double survival in Smn2B/− mice, a model for SMA, independent of changes in SMN levels, liver steatosis, or enhanced hepatic functions. Liver damage and ketone levels were reduced, implying a lower reliance on fatty acid oxidation. This preclinical proof of concept study provides grounds for controlled clinical investigation of dietary needs and offers evidence to inform nutritional guidelines specific to SMA.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.120
GPT teacher head0.411
Teacher spread0.290 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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