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Record W2799862683 · doi:10.3390/molecules23051085

Liposomes as Gene Delivery Vectors for Human Placental Cells

2018· article· en· W2799862683 on OpenAlexaff
Lucie Valéro, Khair Alhareth, Jenifer Espinoza Romero, Warren Viricel, Jeanne Leblond Chain, Audrey Chissey, Hélène Dhôtel, Caroline Roques, Danielle Campiol Arruda, Virginie Escriou, Nathalie Mignet, Thierry Fournier, Karine Andrieux

Bibliographic record

VenueMolecules · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLipofectamineInternalizationLiposomeCationic liposomeTransfectionSmall interfering RNANucleic acidGene deliveryConfocal microscopyChemistryBiologyCell biologyMolecular biologyCellBiochemistryGeneVector (molecular biology)

Abstract

fetched live from OpenAlex

Nanomedicine as a therapeutic approach for pregnancy-related diseases could offer improved treatments for the mother while avoiding side effects for the fetus. In this study, we evaluated the potential of liposomes as carriers for small interfering RNAs to placental cells. Three neutral formulations carrying rhodamine-labelled siRNAs were evaluated on an in vitro model, i.e., human primary villous cytotrophoblasts. siRNA internalization rate from lipoplexes were compared to the one in the presence of the lipofectamine reagent and assessed by confocal microscopy. Results showed cellular internalization of nucleic acid with all three formulations, based on two cationic lipids, either DMAPAP or CSL-3. Moreover, incubation with DMAPAP+AA provided a rate of labelled cells as high as with lipofectamine (53 ± 15% and 44 ± 12%, respectively) while being more biocompatible. The proportion of cells which internalized siRNA were similar when using DMAPAP/DDSTU (16 ± 5%) and CSL-3 (22 ± 5%). This work highlights that liposomes could be a promising approach for gene therapy dedicated to pregnant patients.

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 score0.015
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.262
Teacher spread0.251 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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