Characterization and Mathematical Modeling of Alginate/Chitosan-Based Nanoparticles Releasing the Chemokine CXCL12 to Attract Glioblastoma Cells
Bibliographic record
Abstract
Chitosan (Chit) currently used to prepare nanoparticles (NPs) for brain application can be complexed with negatively charged polymers such as alginate (Alg) to better entrap positively charged molecules such as CXCL12. A sustained CXCL12 gradient created by a delivery system can be used, as a therapeutic approach, to control the migration of cancerous cells infiltrated in peri-tumoral tissues similar to those of glioblastoma multiforme (GBM). For this purpose, we prepared Alg/Chit NPs entrapping CXCL12 and characterized them. We demonstrated that Alg/Chit NPs, with an average size of ~250 nm, entrapped CXCL12 with ~98% efficiency for initial mass loadings varying from 0.372 to 1.490 µg/mg NPs. The release kinetic profiles of CXCL12 were dependent on the initial mass loading, and the released chemokine from NPs after seven days reached 12.6%, 32.3%, and 59.9% of cumulative release for initial contents of 0.372, 0.744, and 1.490 µg CXCL12/mg NPs, respectively. Mathematical modeling of released kinetics showed a predominant diffusive process with strong interactions between Alg and CXCL12. The CXCL12-NPs were not toxic and did not promote F98 GBM cell proliferation, while the released CXCL12 kept its chemotaxis effect. Thus, we developed an efficient and tunable CXCL12 delivery system as a promising therapeutic strategy that aims to be injected into a hydrogel used to fill the cavity after surgical tumor resection. This system will be used to attract infiltrated GBM cells prior to their elimination by conventional treatment without affecting a large zone of healthy brain tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".