Functionalized bacterial cellulose nanowhiskers as long‐lasting drug nanocarrier for antibiotics and anticancer drugs
Bibliographic record
Abstract
A nano drug carrier based on sustainable and biocompatible nanocellulose was developed for use in prolonged drug releases. The grafting of β‐cyclodextrin (βCD) on bacterial cellulose nanowhiskers (BCNC) using citric acid (CA) as a green linker was performed. This led to the formation of functionalized BCNC‐grafted‐βCD (BCNC‐ g ‐βCD). Broad‐spectrum antibiotic Ciprofloxacin (CIP) and anticancer drugs Doxorubicin (DOX) and Paclitaxel (PTX) were used as model drugs. These model drugs were conjugated to BCNC‐ g ‐βCD to form the drug‐nanocarrier systems (BCNC‐ g ‐βCD‐drug). The change in the nanowhiskers’ surface chemistry, morphology, and crystallinity was characterized by FTIR, solid‐state 13 C NMR, scanning electron microscopy (SEM), atomic force microscopy (AFM), and x‐ray diffraction (XRD). The functionalized nanowhiskers showed a significant increase in the drug payloads, which ranged from 495 ±4–810 ±7 μg/mg, along with a radical improvement in the drug release profiles. For all of the developed drug‐conjugated nanocarriers, the initial burst releases were reduced effectively. The observed drug releases showed a sustained and controlled manner, with cumulative releases of 75–90 % over 5–5.5 days. Nevertheless, an improved drug release performance was observed in the acidic pH of 6.4 that mimicked extracellular tumor cells. In vitro drug release data were fitted zero‐order kinetic model with drug release constants (K 0 ) of 0.68, 0.74, and 0.79 μg drug/h (at pH 6.4 and 37 °C) for BCNC‐ g ‐βCD‐CIP, BCNC‐ g ‐βCD‐DOX, and BCNC‐ g ‐βCD‐PTX nanosystems, respectively. The observed higher payloads along with the slow releases of drugs from the developed nanocarrier suggests its promising potential for reducing the frequent daily dosing and minimizing systemic toxicity of loaded drugs.
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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) | 0.000 | 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) | 0.000 | 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".