The potential of calcium silicate hydrate as a carrier of ibuprofen
Bibliographic record
Abstract
Calcium silicate nanostructured materials are promising drug carriers owing to their excellent biocompatibility, good bioactivity and high drug-loading capacity. In recent years, studies have been carried out on the synthesis of calcium silicate hydrate (CSH) nanostructured materials with desirable sizes and morphologies and their applications in drug delivery, where very interesting results and important insights have been documented. This editorial is not intended to offer a comprehensive review on the research on CSH nanostructured materials as drug carriers; rather, it presents representative examples: i) mesoporous microspheres; ii) ultrathin nanosheets; iii) iron oxide/CSH core/shell nanocomposites; and iv) CSH/block copolymer nanocomposites, and important results obtained in the study of CSH drug delivery systems for ibuprofen (IBU) as a model drug. These results show that the nanostructured CSH materials with specially designed architectures as IBU carriers have ultrahigh drug-loading capacity and sustainable drug release properties; thus, they are promising drug carriers for IBU. In addition, a new drug release kinetics has been found in the nanostructured CSH drug delivery systems. Most recently, new insight has been gained by tracking the behavior of these drug delivery systems on the molecular level using synchrotron-based X-ray spectroscopy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".