Effect of UV and Gamma Irradiation Sterilization Processes in the Properties of Different Polymeric Nanoparticles for Biomedical Applications
Bibliographic record
Abstract
The sterilization processes of nanoparticles (NP) by autoclaving and filtration are two of the most utilized methods in the pharmaceutical industry but are not always a viable option. For this reason, the search for alternative options such as UV and gamma radiation is of interest. In this work, we evaluated both types of sterilization on two types of NP in solid state widely employed in the literature for biomedical applications, poly-(ε-caprolactone) and poly(d,l-lactide-co-glycolide) acid NP stabilized with polyvinyl alcohol. Physicochemical properties and cell viability were studied pre- and post-sterilization. The efficiency of irradiation sterilization was performed by a test of sterility using 1 × 108 CFU/mL of Escherichia coli, Staphylococcus aureus, and Candida albicans. Microbiological monitoring revealed that both methods were sufficient for sterilization. After the UV irradiation sterilization (100 µJ/cm2), no substantial changes were observed in the physicochemical properties of the NP or in the interaction or morphology of human glial cells, though 5 and 10 kGy of gamma irradiation showed slight changes of NP size as well as a decrease in cell viability (from 100 µg/mL of NP). At 5 kGy of radiation doses, the presence of trehalose as cryoprotectant reduces the cell damage with high concentrations of NP, but this did not occur at 10 kGy. Therefore, these methods could be highly effective and low-processing-time options for sterilizing NP for medical purposes. However, we suggest validating each NP system because these generally are of different polymer-composition systems.
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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) | 0.001 | 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".