Liposomal delivery of gene therapy for ovarian cancer: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically identify and narratively synthesize the evidence surrounding liposomal delivery of gene therapy and the outcome for ovarian cancer. METHODS: An electronic database search of the Embase, MEDLINE and Web of Science from inception until July 7, 2023, was conducted to identify primary studies that investigated the effect of liposomal delivery of gene therapy on ovarian cancer outcomes. Retrieved studies were assessed against the eligibility criteria for inclusion. RESULTS: The search yielded 564 studies, of which 75 met the inclusion criteria. Four major types of liposomes were identified: cationic, neutral, polymer-coated, and ligand-targeted liposomes. The liposome with the most evidence involved cationic liposomes which are characterized by their positively charged phospholipids (n = 37, 49.3%). Similarly, those with neutrally charged phospholipids, such as 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, were highly researched as well (n = 25, 33.3%). Eight areas of gene therapy research were identified, evaluating either target proteins/transcripts or molecular pathways: microRNAs, ephrin type-A receptor 2 (EphA2), interleukins, mitogen-activated protein kinase (MAPK), human-telomerase reverse transcriptase/E1A (hTERT/EA1), suicide gene, p53, and multidrug resistance mutation 1 (MDR1). CONCLUSION: Liposomal delivery of gene therapy for ovarian cancer shows promise in many in vivo studies. Emerging polymer-coated and ligand-targeted liposomes have been gaining interest as they have been shown to have more stability and specificity. We found that gene therapy involving microRNAs was the most frequently studied. Overall, liposomal genetic therapy has been shown to reduce tumor size and weight and improve survivability. More research involving the delivery and targets of gene therapy for ovarian cancer may be a promising avenue to improve patient outcomes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".