Mucopenetrating Janus Nanoparticles For Field-Coverage Oral Cancer Chemoprevention
Bibliographic record
Abstract
INTRODUCTION: Oral squamous cell carcinoma (OSCC), is associated with high morbidity and mortality. Preemptive interventions have been postulated to provide superior therapeutic options, but their implementation has been restricted by the availability of broadly applicable local delivery systems. METHODS: We address this challenge by engineering a delivery vehicle, Janus nanoparticles (JNP), that combine the dual mucoadhesive properties of a first cationic chitosan compartment with a second hydrophobic poly(lactide-co-glycolide) release compartment. JNP are designed to avoid rapid mucus clearance while ensuring stable loading and controlled release of the IL-6 receptor antagonist, tocilizumab (TCZ). RESULTS: The JNP featured defined and monodispersed sizes with an average diameter of 327 nm and a PDI of 0.245, high circularities above 0.90 and supported controlled release of TCZ and effective internalization by oral keratinocytes. TCZ released from JNP retained its biological activity and effectively reduced both, soluble and membrane-bound IL-6Rα (71% and 50%). In full-thickness oral mucosal explants, 76% of the JNP breached the stratum corneum and in 41% were observed in the basal cell layer indicating excellent mucopenetrating properties. When tested in an aggressive OSCC xenograft model, TCZ-loaded JNP showed high levels of xenograft inhibition and outperformed all control groups with respect to inhibition of tumor cell proliferation, reduction in tumor size and reduced expression of the proto-oncogene ERG. CONCLUSION: By combining critically required, yet orthogonal properties within the same nanoparticle design, the JNP in this study, demonstrate promise as precision delivery platforms for intraoral field-coverage chemoprevention, a vastly under-researched area of high clinical importance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".