Towards the development of chitosan nanoparticles for plutonium pulmonary decorporation
Bibliographic record
Abstract
Since the 1940s, great amounts of Plutonium (Pu) have been produced for both military and civil purposes. Until now, the standard therapy for decorporation following inhalation has been the intravenous injection of diethylenetriaminepentaacetic acid ligand (Ca-DTPA form). This method offers a strong complexing constant for Pu(iv) but has poor chemical specificity, therefore its efficacy is limited to actinides present in the blood. Consequently, there is no decorporation treatment currently available which efficiently removes the intracellular Pu(iv) trapped in the pulmonary macrophages. Our research shows that a nanoparticle approach could be of particular interest due to large contact area and ability to target the retention compartments of the lungs. In this study, we have focused on the inhalation process involving forms of Pu(iv) with poor solubility. We explored the design of biocompatible nanoparticles able to target the macrophages in the lung alveoli and to chelate the forms of Pu(iv) with poor solubility. Nanoparticle formation was achieved through an ionic cross-linking concept using a polycationic polymer and an anionic chelate linker. We chose N-trimethyl chitosan, for its biocompatibility, as the polycationic polymer base of the nanoparticle and the phosphonic analogue of DTPA, diethylenetriamine-pentamethylenephosphonic acid (DTPMP) as the anionic chelating linker in forming NPs TMC-DTPMP. The synthesis and physico-chemical characterization of these NPs are presented. Secondly, the complexation mechanisms of TMC-DTPMP NPs with Thorium (Th(iv)) are discussed in terms of efficiency and structure. The Extended X-Ray Absorption Fine Structure (EXAFS) of the TMC-DTPMP complex with Th(iv) as well as Pu(iv) are defined and completed with DFT calculations to further delineate the plutonium coordination sphere after complexation. Finally, preliminary cytotoxicity tests onto macrophages were assayed.
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) | 0.001 | 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".