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Record W4407595212 · doi:10.1038/s41598-025-90083-3

Extracellular vesicles efficiently deliver survival motor neuron protein to cells in culture

2025· article· en· W4407595212 on OpenAlexafffund
Charlotte A. René, Robin J. Parks

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersOttawa Hospital Research InstituteUniversity of WindsorCanadian Institutes of Health ResearchCure SMA
KeywordsSpinal muscular atrophySMN1SMA*Cell biologyMotor neuronBiologyCell cultureChemistryGeneNeuroscienceBiochemistryGeneticsSpinal cord

Abstract

fetched live from OpenAlex

Spinal Muscular Atrophy (SMA) is a genetic neuromuscular disorder caused by homozygous mutation or deletion of the survival motor neuron 1 (SMN1) gene, leading to a low quantity of SMN protein in cells. This depletion of SMN protein preferentially leads to death of motor neurons and, consequently, muscle atrophy, in addition to defects in many other peripheral tissues. SMN protein is naturally loaded into extracellular vesicles (EVs), which are sub-micron-sized, membrane-bound particles released from all cell types. The innate ability of EVs to deliver cargo to recipient cells has caused these vesicles to gain interest as therapeutic delivery vehicles. In this study, we show that adenovirus-mediated overexpression of SMN protein in HepG2 cells leads to the release of EVs loaded with high levels of SMN protein into conditioned medium. Application of this medium to recipient cells in tissue culture led to uptake of the SMN protein, which subsequently transited to the nucleus and co-localized with Gemin2 protein, forming nuclear gem-like structures similar to the native SMN protein. Overall, this work demonstrates that SMN protein can be delivered to cells through EVs, which holds promise as a potential therapy for patients with 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 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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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