In‐Silico Tuning of Curcumin Loading on PEG Grafted Chitosan: An Atomistic Simulation
Bibliographic record
Abstract
Abstract In this work, trimethyl chitosan grafted polyethylene glycol (PEG) was employed to optimize the curcumin loading, which is a natural bioactive substance with a good anti‐cancerous effect. It is vital to develop a novel carrier to increase the therapeutic effect of curcumin and decrease its hydrophobicity. Biocompatibility and hydrophilicity of the PEG cause it to be one of the most attractive drug carriers. Chitosan is also of great importance, considering its biocompatibility, and is used along with the drug‐carrying polymers. In this work, interaction energies, stability, hydrophilicity, and other molecular properties of the curcumin‐loaded PEG‐chitosan nanohybrid have been investigated. Atomistic analysis showd that the optimum concentration of chitosan is 60 % and optimum concentration of PEG is 40 %. In addition to concentration, the effect of PEG chain length, which is one of the important parameters of this circimin delivery system, has been also studied. The current work gives an atomistic insight into curcumin delivery and suggests a new curcumin delivery system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".