MétaCan
Menu
Back to cohort
Record W3159623463 · doi:10.1039/d1bm00273b

Hydrogel based lipid-oligonucleotides: a new route to self-delivery of therapeutic sequences

2021· article· en· W3159623463 on OpenAlexaff
Sébastien Benizri, Alexandra Gaubert, C. Soulard, Étienne Gontier, Isabelle Svahn, Palma Rocchi, Gaëlle Vacher, Philippe Barthélémy

Bibliographic record

VenueBiomaterials Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsCanadian Nautical Research Society
FundersInserm TransfertAgence Nationale de la Recherche
KeywordsOligonucleotideChemistryConjugateComputational biologyBiophysicsBiochemistryBiologyDNA

Abstract

fetched live from OpenAlex

Synthetic OligoNucleotides (ON) provide promising therapeutic tools for controlling specifically genetic expression in a broad range of diseases from cancers to viral infections. Beside their chemical stability and intracellular delivery, the controlled release of therapeutic sequences remains an important challenge for successful clinical applications. In this work, Lipid-OligoNucleotide (LON) conjugates stabilizing hydrogels are reported and characterized by rheology and cryo-scanning electron microscopy (cryo-SEM). These studies revealed that lipid conjugation of antisense oligonucleotides featuring partial self-complementarity resulted in entangled pearl-necklace networks, which were obtained through micelle-micelle interaction driven by duplex formation. Owing to these properties, the Lipid AntiSense Oligonucleotide (LASO) sequences exhibited a prolonged release after subcutaneous administration compared to the non-lipidic antisense (ASO) one. The LASO self-assembly based hydrogels obtained without adjuvant represent an innovative approach for the sustained self-delivery of therapeutic oligonucleotides.

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 score0.043
Threshold uncertainty score0.507

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 head0.015
GPT teacher head0.282
Teacher spread0.268 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBiomaterials ScienceSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207