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Record W2064585559 · doi:10.1038/mtna.2014.16

DNA Ministrings: Highly Safe and Effective Gene Delivery Vectors

2014· article· en· W2064585559 on OpenAlexaff
Nafiseh Nafissi, Samih Alqawlaq, Eric A. Lee, Marianna Földvári, Paul A. Spagnuolo, Roderick Slavcev

Bibliographic record

VenueMolecular Therapy — Nucleic Acids · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPlasmidBiologyDNATransfectionGeneIn vitro recombinationGene deliveryMolecular biologyCell biologyGeneticsGene expressionMolecular cloning

Abstract

fetched live from OpenAlex

Conventional plasmid DNA vectors play a significant role in gene therapy, but they also have considerable limitations: they can elicit adverse immune responses because of bacterial sequences they contain for maintenance and amplification in prokaryotes, their bioavailability is compromised because of their large molecular size, and they may be genotoxic. We constructed an in vivo platform to produce ministring DNA-mini linear covalently closed DNA vectors-that are devoid of unwanted bacterial sequences and encode only the gene(s) of interest and necessary eukaryotic expression elements. Transfection of rapidly and slowly dividing human cells with ministring DNA coding for enhanced green fluorescent protein resulted in significantly improved transfection, bioavailability, and cytoplasmic kinetics compared with parental plasmid precursors and isogenic circular covalently closed DNA counterparts. Ministring DNA that integrated into the genome of human cells caused chromosomal disruption and apoptotic death of possibly oncogenic vector integrants; thus, they may be safer than plasmid and circular DNA vectors.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.006
GPT teacher head0.242
Teacher spread0.235 · 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

Citations35
Published2014
Admission routes1
Has abstractyes

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