Optimization of a two-step desolvation method for preparing gelatin nanoparticles and cell uptake studies in 143B osteosarcoma cancer cells.
Bibliographic record
Abstract
PURPOSE: To establish a matrix of parameters to synthesize nanoparticles of different sizes and to investigate the cellular uptake of these nanoparticles by osteosarcoma cancer cells in order to investigate their potential as therapeutic drugdelivery carriers. METHODS: Gelatin A and B were used to synthesize nanoparticles by a two-step desolvation process. Different parameters were investigated, including temperature, pH, concentration of glutaraldehyde, type of desolvating agent and nature of gelatin. For cell uptake studies, Texas Red labeled nanoparticles were incubated with 143B osteosarcoma cells and then evaluated using confocal laser scanning microscopy (CLSM). RESULTS: The systematic investigation of the synthesis parameters showed that it is possible to prepare gelatin-based nanoparticles with different particle sizes and a narrow size distribution. Temperature and nature of the gelatin were the most important synthesis factors. Bioimaging using CLSM showed uptake of the nanoparticles by 143B osteosarcoma cancer cells. CONCLUSIONS: Osteosarcoma cancer cells take up gelatin nanoparticles. This might improve the clinical effectiveness of anti-cancer treatments if nanoparticles are used as a drug delivery system and has important implications for future cancer treatment strategies.
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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 | 0.001 | 0.001 |
| 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".