Hydroxyapatite Nanoparticles as a Potential Long-Term Treatment of Cancer of Epithelial Origin
Bibliographic record
Abstract
Among the various forms of cancer, non-small cell lung cancer is the most frequently diagnosed and the leading cause of deaths. CIMAvax-EGF has been introduced as a first-of-its-kind EGF immune-depleting therapy and shows promise for improvement of the survival rate and quality of life of patients with NSCLC. As part of the continued development of this vaccine, it is of paramount importance to attain long-term treatment. In this work, we have used hydroxyapatite nanoparticles (a biocompatible and biodegradable material) with an average size of 60 ± 10 nm to induce an anti-EGF immune response. Three candidates referred to as HANp–rhEGF–rP64k, HANp-MC, and HANp-IP were obtained through covalent interactions between proteins and nanoparticles. The total anti-EGF IgG titers induced by the three nanoparticulate systems in mice were between 1:5000 and 1:10,000 during all periods of study (4 immunization doses and 3 extractions, 104 days). No differences in the IgG2/IgG1 ratio were observed in comparison to CIMAvax-EGF, with both being consistent with a Th2 polarization pattern. Histological evaluation of muscle tissues showed that the new nanoparticulate systems do not affect the injection sites in mice. Finally, a study of immune response induction with CIMAvax-EGF and maintenance with HANp–rhEGF–rP64k demonstrated that it is possible to maintain the immune response over the course of treatment (91 days). These results introduce these new hydroxyapatite nanoparticulate systems as effective candidates for the long-term treatment of lung and other cancers of epithelial origin.
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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) | 0.000 | 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".