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Record W4220860149 · doi:10.15252/emmm.202114297

Autologous, lentivirus‐modified, T‐rapa cell “micropharmacies” for lysosomal storage disorders

2022· article· en· W4220860149 on OpenAlexaff
Murtaza S. Nagree, Tania C. Felizardo, Mary L. Faber, Jitka Rybová, C. Anthony Rupar, S.R. Foley, Maria Fuller, Daniel H. Fowler, Jeffrey A. Medin

Bibliographic record

VenueEMBO Molecular Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsJuravinski HospitalWestern UniversityMcMaster UniversityUniversity of Toronto
FundersMidwest Athletes Against Childhood CancerHope Foundation
KeywordsEx vivoLentivirusIn vivoImmunologyBiologyCancer researchMedicinePharmacologyBiotechnologyVirus

Abstract

fetched live from OpenAlex

Abstract T cells are the current choice for many cell therapy applications. They are relatively easy to access, expand in culture, and genetically modify. Rapamycin‐conditioning ex vivo reprograms T cells, increasing their memory properties and capacity for survival, while reducing inflammatory potential and the amount of preparative conditioning required for engraftment. Rapamycin‐conditioned T cells have been tested in patients and deemed to be safe to administer in numerous settings, with reduced occurrence of infusion‐related adverse events. We demonstrate that ex vivo lentivirus‐modified, rapamycin‐conditioned CD4 + T cells can also act as next‐generation cellular delivery vehicles—that is, “micropharmacies”—to disseminate corrective enzymes for multiple lysosomal storage disorders. We evaluated the therapeutic potential of this treatment platform for Fabry, Gaucher, Farber, and Pompe diseases in vitro and in vivo . For example, such micropharmacies expressing α‐galactosidase A for treatment of Fabry disease were transplanted in mice where they provided functional enzyme in key affected tissues such as kidney and heart, facilitating clearance of pathogenic substrate after a single administration.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.023
GPT teacher head0.314
Teacher spread0.291 · 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.

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

Citations15
Published2022
Admission routes1
Has abstractyes

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