Synthesis of Reductive Nanogels Cross-linked with Polyanions to Mimic Oligonucleotides
Bibliographic record
Abstract
Gene therapy with nucleic acid-based therapeutic agents has emerged as a promising strategy to treat various human diseases. However, the efficient delivery of nucleic acids faces a critical challenge as they are susceptible to hydrolytic and enzymatic degradation during blood circulation and in the body. The conventional delivery strategy for nucleic acids involves cationic polymers which bind with the anionic phosphate groups of the nucleic acid by ionic interaction to form polyplexes. These polyplexes are designed for effective endosomal escape of the nucleic acids through the proton-sponge effect, leading to release of their cargos, resulting in excellent gene transfection. However, the polyplex-based nanocarriers present critical drawbacks including cytotoxicity of cationic polymers, the degradation of nucleic acids in polyplexes, and the propensity to aggregate with serum proteins during blood circulation. My MSc thesis focuses on the exploration of a new paradigm for nucleic acid delivery which explores the design of reactive, but neutral hydrophilic copolymers and the use of nucleic acids as therapeutics and cross-linkers. A poly(phosphonic acid) with terminal thiol groups to mimic oligonucleotides is synthesized by reversible addition fragmentation chain-transfer polymerization. Consequently, the poly(phosphonic acid) is covalently conjugated with a water-soluble random copolymer bearing pendant pyridyl-disulfide functional groups through a thiol-disulfide exchange reaction in aqueous solution, resulting in the formation of cross-linked nanogels through the formation of new disulfide bonds. Consequently, the reductive cleavage of the disulfide linkages in the fabricated nanogels has the potential to prompt the detachment of therapeutic nucleic acids from the nanogel cores. The enhanced release of nucleic acids through degradation (or destabilization) of nanogel cores can occur in the presence of glutathione inside cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".