Risedronate-loaded aerogel scaffolds for bone regeneration
Bibliographic record
Abstract
Sugarcane bagasse-derived nanofibrillated cellulose (NFC), a type of cellulose with a fibrous structure, is potentially used in the pharmaceutical field. Regeneration of this cellulose using a green process offers a more accessible and less ordered cellulose II structure (amorphous cellulose; AmC). Furthermore, the preparation of cross-linked cellulose (NFC/AmC) provides a dual advantage by building a structural block that could exhibit distinct mechanical properties. 3D aerogel scaffolds loaded with risedronate were prepared in our study using NFC or cross-linked cellulose (NFC/AmC), then combined with different concentrations of chitosan. Results proved that the aerogel scaffolds composed of NFC and chitosan had significantly improved the mechanical properties and retarded drug release compared to all other fabricated aerogel scaffolds. The aerogel scaffolds containing the highest concentration of chitosan (SC-T3) attained the highest compressive strength and mean release time values (415 ± 41.80 kPa and 2.61 ± 0.23 h, respectively). Scanning electron microscope images proved the uniform highly porous microstructure of SC-T3 with interconnectedness. All the tested medicated as well as unmedicated aerogel scaffolds had the ability to regenerate bone as assessed using the MG-63 cell line, with the former attaining a higher effect than the latter. However, SC-T3 aerogel scaffolds possessed a lower regenerative effect than those composed of NFC only. This study highlights the promising approach of the use of biopolymers derived from agro-wastes for tissue engineering.
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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) | 0.000 | 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".