Enzymatically degradable, starch-based layer-by-layer films: application to cytocompatible single-cell nanoencapsulation
Bibliographic record
Abstract
The build-up and degradation of cytocompatible nanofilms in a controlled fashion have great potential in biomedical and nanomedicinal fields, including single-cell nanoencapsulation (SCNE). Herein, we report the fabrication of biodegradable films of cationic starch (c-ST) and anionic alginate (ALG) by electrostatically driven layer-by-layer (LbL) assembly technology and its application to the SCNE. The [c-ST/ALG] multilayer nanofilms, assembled either on individual Saccharomyces cerevisiae or on the 2D flat gold surface, degrade on demand, in a cytocompatible fashion, via treatment with α-amylase. Their degradation profiles are investigated, while systematically changing the α-amylase concentration, by several surface characterization techniques, including quartz crystal microbalance with dissipation monitoring (QCM-D) and ellipsometry. DNA incorporation in the LbL nanofilms and its controlled release, upon exposure of the nanofilms to an aqueous α-amylase solution, are demonstrated. The highly cytocompatible nature of the film-forming and -degrading conditions is assessed in the c-ST/ALG-shell formation and degradation of S. cerevisiae. We envisage that the cytocompatible, enzymatic degradation of c-ST-based nanofilms paves the way for developing advanced biomedical devices with programmed dissolution in vivo.
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 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) | 0.000 | 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 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".