Glycogen for Smyd3-antisense oligonucleotide delivery and enhanced liver cancer gene therapy
Bibliographic record
Abstract
The overexpression of Smyd3 is closely related to the progression of various cancers. Smyd3 is overexpressed in liver cancers but is hardly detectable in normal tissues; consequently, it is attracting increasing attention as a target for liver cancer therapy. Therefore, silencing Smyd3 mRNA using antisense oligonucleotides (ASOs) offers a promising option for liver cancer therapy. However, their clinical application is hindered by their poor stability and cellular uptake. Although promising, strategies such as chemical modification of the ASOs as well as the synthetic nanocarriers raise safety concerns. The efficient delivery of ASOs into tumor cells remains a big challenge. In this study, we developed a novel glyco-nanovector for Smyd3-ASO delivery. Glycogen possesses an inherent dendritic nanostructure. Aminated glycogen (NG) was simply synthesized by grafting glycogen with diethylenetriamine (DETA). NG possessed good biocompatibility. Cationic NG efficiently formed a complex with Smyd3-ASOs and shielded them from enzymatic degradation. NG significantly enhanced the cellular uptake of Smyd3-ASOs in HepG2 cells. As a result, NG/ASOs decreased the translation of Smyd3 proteins from mRNA and thus inhibited the proliferation of HepG2 cells. This study underscores the potential of glycogen as an efficient nanovector for ASO delivery and cancer gene therapy.
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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".