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Record W2980610937 · doi:10.3390/molecules24203744

Biodegradable Polymers for Gene Delivery

2019· review· en· W2980610937 on OpenAlexafffund
Thresia Thomas, Heidar‐Ali Tajmir‐Riahi, C. K. S. Pillai

Bibliographic record

VenueMolecules · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGene deliveryBiodegradable polymerPolymerNanotechnologyGenetic enhancementChemistryGeneMaterials scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The cellular transport process of DNA is hampered by cell membrane barriers, and hence, a delivery vehicle is essential for realizing the potential benefits of gene therapy to combat a variety of genetic diseases. Virus-based vehicles are effective, although immunogenicity, toxicity and cancer formation are among the major limitations of this approach. Cationic polymers, such as polyethyleneimine are capable of condensing DNA to nanoparticles and facilitate gene delivery. Lack of biodegradation of polymeric gene delivery vehicles poses significant toxicity because of the accumulation of polymers in the tissue. Many attempts have been made to develop biodegradable polymers for gene delivery by modifying existing polymers and/or using natural biodegradable polymers. This review summarizes mechanistic aspects of gene delivery and the development of biodegradable polymers for gene 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.011

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 head0.047
GPT teacher head0.309
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations152
Published2019
Admission routes2
Has abstractyes

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