Bimodal Tumor-Targeting from Microenvironment Responsive Hyaluronan Layer-by-Layer (LbL) Nanoparticles
Bibliographic record
Abstract
Active targeting of nanoscale drug carriers can improve tumor-specific delivery; however, cellular heterogeneity both within and among tumor sites is a fundamental barrier to their success. Here, we describe a tumor microenvironment-responsive layer-by-layer (LbL) polymer drug carrier that actively targets tumors based on two independent mechanisms: pH-dependent cellular uptake at hypoxic tumor pH and hyaluronan-directed targeting of cell-surface CD44 receptor, a well-characterized biomarker for breast and ovarian cancer stem cells. Hypoxic pH-induced structural reorganization of hyaluronan-LbL nanoparticles was a direct result of the nature of the LbL electrostatic complex, and led to targeted cellular delivery in vitro and in vivo, with effective tumor penetration and uptake. The nanoscale drug carriers selectively bound CD44 and diminished cancer cell migration in vitro, while co-localizing with the CD44 receptor in vivo. Multimodal targeting of LbL nanoparticles is a powerful strategy for tumor-specific cancer diagnostics and therapy that can be accomplished using a single bilayer of polyamine and hyaluronan that, when assembled, produce a dynamic and responsive cell-particle interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".