A Three-Dimensional Printable Hydrogel Formulation for the Local Delivery of Therapeutic Nanoparticles to Cervical Cancer
Bibliographic record
Abstract
Cervical cancer is the fourth most common malignancy among women. Compared to other types of cancer, therapeutic agents can be administrated locally at the mucosal vaginal membrane. Thermosensitive gels have been developed over the years for contraception or for the treatment of bacterial, fungal, and sexually transmitted infections. These formulations often carry therapeutic nanoparticles and are now being considered in the arsenal of tools for oncology. They can also be three-dimensionally (3D) printed for a better geometrical adjustment to the anatomy of the patient, thus enhancing the local delivery treatment. In this study, a localized delivery system composed of a Pluronic F127-alginate hydrogel with efficient nanoparticle (NP) release properties was prepared for intravaginal application procedures. The kinetics of hydrogel degradation and its NP releasing properties were demonstrated with ultrasmall gold nanoparticles (∼80% of encapsulated AuNPs released in 48 h). The mucoadhesive properties of the hydrogel formulation were assayed by the periodic acid/Schiff reagent staining, which revealed that 19% of mucins were adsorbed on the gel’s surface. The hydrogel formulation was tested for cytocompatibility in three cell lines (HeLa, CRL 2616, and BT-474; no sign of cytotoxicity revealed). The release of AuNPs from the hydrogel and their accumulation in vaginal membranes were quantitatively measured in vitro / ex vivo with positron emission tomography, a highly sensitive modality allowing real-time imaging of nanoparticle diffusion (lag time to start of permeation of 3.3 h, 47% of AuNPs accumulated in the mucosa after 42 h). Finally, the potential of the AuNP-containing Pluronic F127-alginate hydrogel for 3D printing was demonstrated, and the geometrical precision of the 3D printed systems was measured by magnetic resonance imaging (<0.5 mm precision; deviation from the design values <2.5%). In summary, this study demonstrates the potential of Pluronic F127-alginate formulations for the topical administration of NP-releasing gels applied to vaginal wall therapy. This technology could open new possibilities for photothermal and radiosensitizing oncology applications.
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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".