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Record W4389233268 · doi:10.1126/sciadv.adi0508

Genetically engineered transfusable platelets using mRNA lipid nanoparticles

2023· article· en· W4389233268 on OpenAlexafffund
Jerry Leung, Colton Strong, Katherine E. Badior, Madelaine Robertson, Xiaowu Wu, Michael A. Meledeo, Emma Kang, Manoj Paul, Yusuke Sato, Hideyoshi Harashima, P. Andrew, Dana V. Devine, Eric Jan, Pieter R. Cullis, Christian J. Kastrup

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCreative Research Institute, Hokkaido UniversityCanadian Blood ServicesU.S. Department of Defense
KeywordsPlateletMessenger RNATransfectionPlatelet activationPlatelet transfusionCell biologyImmunologyMedicineChemistryBiologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Platelet transfusions are essential for managing bleeding and hemostatic dysfunction and could be expanded as a cell therapy due to the multifunctional role of platelets in various diseases. Creating these cell therapies will require modifying transfusable donor platelets to express therapeutic proteins. However, there are currently no appropriate methods for genetically modifying platelets collected from blood donors. Here, we describe an approach using platelet-optimized lipid nanoparticles containing mRNA (mRNA-LNP) to enable exogenous protein expression in human and rat platelets. Within the library of mRNA-LNP tested, exogenous protein expression did not require nor correlate with platelet activation. Transfected platelets retained hemostatic function and accumulated in regions of vascular damage after transfusion into rats with hemorrhagic shock. We expect this technology will expand the therapeutic potential of platelets.

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)0.000
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 score0.040
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.300
Teacher spread0.277 · 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

Citations25
Published2023
Admission routes2
Has abstractyes

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