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Record W4401506687 · doi:10.1093/braincomms/fcae267

Timing of SMN replacement therapies in mouse models of spinal muscular atrophy: a systematic review and meta-analysis

2024· review· en· W4401506687 on OpenAlexfundno aff
Helena Chaytow, Anna A. L. Motyl, Yu-Ting Huang, Charis Wong, Gillian L. Currie, Zsanett Bahor, Emily S. Sena, Thomas H. Gillingwater

Bibliographic record

VenueBrain Communications · 2024
Typereview
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council CanadaLifeArcScottish Government Health and Social Care Directorate
KeywordsSpinal muscular atrophyMeta-analysisMedicinePhysical medicine and rehabilitationNeurosciencePathologyBiologyDisease

Abstract

fetched live from OpenAlex

Abstract Mutations in the Survival of Motor Neuron 1 gene lead to a loss of survival motor neuron protein in patients with spinal muscular atrophy. Revolutionary advances in gene therapy have led to survival motor neuron-replacement therapies that significantly prolong life expectancy and improve neuromuscular function. However, accumulating evidence suggests that the timing of survival motor neuron-replacement therapies is a critical determinant of success. We performed a systematic review and meta-analysis of all pre-clinical studies testing survival motor neuron replacement therapies in mouse models of spinal muscular atrophy to assess the impact of timing of delivery on therapeutic effectiveness. We incorporated four databases in this pre-registered study (PROSPERO 2020 CRD42020200180): EMBASE, PubMed, Scopus and Web of Science. Inclusion criteria were; primary research article, a measure of survival analysis, use of survival motor neuron mouse model and evaluation of survival motor neuron-targeting therapy. Exclusion criteria included; use of therapies not known to directly target survival motor neuron, genetic manipulations and/or lack of appropriate controls. We screened papers using the SyRF platform. The main outcome we assessed was survival in treated groups compared to untreated groups. We performed meta-analysis of survival using median survival ratio and the random effects model and measured heterogeneity using the I2 statistic. Subgroup analyses were performed to assess treatment efficacy based on timing of intervention (embryonic delivery, day of birth, postnatal day 2 and postnatal day 3 or later) and treatment type. If detailed in the studies, body weight compared to untreated spinal muscular atrophy models and motor neuron number were included as secondary outcomes for meta-analysis. 3469 studies were initially identified, with 78 ultimately included. Survival motor neuron-replacement therapies significantly affected survival in favour of treatment by a factor of 1.20 (95% CI 1.10–1.30, P < 0.001) with high heterogeneity (I2 = 95%). Timing of treatment was a significant source of heterogeneity (P < 0.01), with earlier treatment having a greater impact on survival. When stratified by type of treatment, earlier treatment continued to have the strongest effect with viral vector replacement therapy and antisense oligonucleotide therapy. Secondary outcome measures of body weight and spinal motor neuron counts were also positively associated with early treatment. Earlier delivery of survival motor neuron replacement therapies is therefore a key determinant of treatment efficacy in spinal muscular atrophy.

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)0.014
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.030
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.296
GPT teacher head0.461
Teacher spread0.166 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

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