Optimization of a near-infrared photothermally controlled nano-retarded release system for loading the anticancer drug adriamycin based on numerical simulation calculations
Bibliographic record
Abstract
The aim of this study is to develop a near-infrared photothermally controlled nano-retarded release system loaded with the anticancer drug Adriamycin optimized based on numerical simulation calculations.Firstly, the instruments, agents and experimental methods for the preparation of selfassembled albumin-loaded nanoparticles were introduced.The cumulative absorption wavelengths of the albumin nanoparticles were investigated by UV and IR spectroscopy, and it was found that the maximal absorption wavelengths of DOX and BDC were distributed at 487 nm and 435 nm, and that the UV maximal absorption wavelength of CUR was 435 nm.In the in vitro slow-release performance, it was found that the cumulative release rate of DOX reached 97.36% when pH 5.0 was used, and that when CUR was used, the cumulative release rate of DOX reached 97.36%.The cumulative release rate of DOX reached 97.36% at pH 5.0, while it was only 59.15% and 30.81% at pH 6.0 and 7.0.The cumulative release rates of CUR at the three pH values were 58.69%, 29.98% and 16.81%, respectively, which were basically the same trend of the retardation curves of the two drugs.The nanoparticles degraded morphology showed the widest and narrowest particle size distribution in PBS buffer solution at pH=5.0 and 7.0, respectively.The loading capacity of the optimized model showed good consistency of effect on measured (11.03%) and predicted (10.87%) values.The photothermal conversion experiments of DOX nanoliposomes were found to have concentration and time dependent photothermal conversion effects.In this paper, from the optical characterization of albumin drugcarrying nanoparticles, it was found that UV light was able to excite PFNSNO for photodynamic therapy as well as NO release through the fluorescence resonance energy transfer process.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".