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Record W2203067829 · doi:10.2119/molmed.2009.00163

α-Galactosidase A-Tat Fusion Enhances Storage Reduction in Hearts and Kidneys of Fabry Mice

2010· article· en· W2203067829 on OpenAlexafffund
Koji Higuchi, Makoto Yoshimitsu, Xin Fan, Xiaoxin Guo, Vanessa I. Rasaiah, Jennifer Yen, Chuwa Tei, Toshihiro Takenaka, Jeffrey A. Medin

Bibliographic record

VenueMolecular Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsGlobotriaosylceramideFabry diseaseEnzyme replacement therapyGenetic enhancementAlpha-galactosidaseFusion proteinViral vectorLysosomal storage diseaseTransduction (biophysics)Substrate reduction therapyEnzymeRecombinant DNAIn vitroIn vivoMedicineBiologyVirologyPathologyDiseaseBiochemistryGene

Abstract

fetched live from OpenAlex

The protein transduction domain from human immunodeficiency virus (HIV) Tat allows proteins to penetrate the cell membrane. Enhanced cellular uptake of therapeutic proteins could benefit a number of disorders. This is especially true for lysosomal storage disorders (LSDs) where enzyme replacement therapy (ERT) and gene therapy have been developed. We developed a novel recombinant lentiviral vector (LV) that engineers expression of alpha-galactosidase A (alpha-gal A)-Tat fusion protein for correction of Fabry disease, the second-most prevalent LSD with manifestations in the brain, kidney and heart. In vitro experiments confirmed mannose-6-phosphate independent uptake of the fusion factor. Next, concentrated therapeutic LV was injected into neonatal Fabry mice. Analysis of tissues at 26 wks demonstrated similar alpha-gal A enzyme activities but enhanced globotriaosylceramide (Gb3) reduction in hearts and kidneys compared with the alpha-gal A LV control. This strategy might advance not only gene therapy for Fabry disease and other LSDs, but also ERT, especially for cardiac Fabry disease.

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)0.001
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 score0.101
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.293
Teacher spread0.283 · 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

Citations14
Published2010
Admission routes2
Has abstractyes

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