Nanocellulose in targeted drug delivery: A review of modifications and synergistic applications
Bibliographic record
Abstract
Nanocellulose, a versatile biopolymer renowned for its exceptional physicochemical attributes including lightweight, biocompatibility, biodegradability, and higher mechanical strength properties has captured significant attention in biomedical research. This renewable material, extracted from widely abundant biosources including plants, bacteria, and algae, exists in three primary forms: cellulose-based nanocrystals (CNCs), nanofibrils (CNFs), and bacterial nanocellulose (BNC). CNCs are characterized by their highly crystalline, needle-shaped structure, while CNFs possess a blend of amorphous and crystalline regions. BNC stands out as the purest form of nanocellulose. Chemical functionalization enables precise tuning of nanocellulose properties, enhancing its suitability for diverse biomedical applications. In drug delivery systems, nanocellulose's unique structure and surface chemistry offer opportunities for targeted delivery of active molecules. Surface-modified nanocellulose can effectively deliver drugs to specific sites, utilizing its inherent properties to control drug release kinetics and improve therapeutic outcomes. Despite these advantages, challenges such as achieving optimal drug loading capacity and ensuring sustained drug release remain. Future research aims to address these challenges and explore novel applications of nano-structured cellulose in targeted drug delivery, highlighting the continued evolution of this promising biomaterial in biomedicine. Furthermore, the review delves into the impact of chemical, physical, and enzymatic methods for CNC surface modifications, showcasing how these approaches enhance the functionalization of CNCs for targeted delivery of different compounds in biological systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".