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Record W2125994231 · doi:10.1021/nl2036047

Remotely Activated Protein-Producing Nanoparticles

2012· article· en· W2125994231 on OpenAlexaff
Avi Schroeder, Michael S. Goldberg, Christian J. Kastrup, Yingxia Wang, Shan Jiang, Brian Joseph, Christopher G. Levins, Sneha T. Kannan, Róbert Langer, Daniel G. Anderson

Bibliographic record

VenueNano Letters · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity of British ColumbiaCanada's Michael Smith Genome Sciences Centre
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of General Medical SciencesNational Cancer Institute
KeywordsNanotechnologyNanomaterialsNanoparticleGreen fluorescent proteinRibosomeChemistryProtein biosynthesisIn vivoBiophysicsNanoscopic scaleTranslation (biology)Drug deliveryLuciferaseFluorescenceMaterials scienceMessenger RNATransfectionBiochemistryBiologyRNAGene

Abstract

fetched live from OpenAlex

The development of responsive nanomaterials, nanoscale systems that actively respond to stimuli, is one general goal of nanotechnology. Here we develop nanoparticles that can be controllably triggered to synthesize proteins. The nanoparticles consist of lipid vesicles filled with the cellular machinery responsible for transcription and translation, including amino acids, ribosomes, and DNA caged with a photolabile protecting group. These particles served as nanofactories capable of producing proteins including green fluorescent protein (GFP) and enzymatically active luciferase. In vitro and in vivo, protein synthesis was spatially and temporally controllable, and could be initiated by irradiating micrometer-scale regions on the time scale of milliseconds. The ability to control protein synthesis inside nanomaterials may enable new strategies to facilitate the study of orthogonal proteins in a confined environment and for remotely activated drug delivery.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 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

Citations113
Published2012
Admission routes1
Has abstractyes

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