Hierarchical chitosan-lignocellulosic duplex system: An in vitro evaluation of controlled release, anti-pathogenic and hemostatic effects
Bibliographic record
Abstract
Critical design challenges that affect novel drug delivery systems concern chemical processing and tissue compatibility of source materials, which highlight the need for sustainable, biocompatible materials and versatile manufacturing methods. This investigation leverages the unique surface chemistry of biomass-derived lignocellulose substrates to form biocomposite frameworks with effective antipathogenic and hemostatic properties via noncovalent synthesis. Complementary electrostatic interactions support a highly porous biocomposite framework, according to spectral (IR, Raman and NMR) and microscopy results. Biocomposite complexes with antipathogenic agents (gentamicin and rifamycin salts) are revealed by kinetic release profiles, as noted by an initial burst and sustained release profile under physiological conditions. In vitro biocompatibility was demonstrated by MTT cell viability assays (ca. 90 % after 48 h). Anti-pathogenic effects are revealed by agar diffusion assays with E. coli (up to 16 mm inhibition zones). In vitro blood sorption, cell adhesion and blood clotting index (BCI) results of biocomposites reveal impressive blood absorption capacity (ca. 10-fold; w/w) with good cell adhesion and efficient hemostatic properties with BCI below 2 %. This study challenges the current limits of specialized biomedical applications of lignocellulose fiber-chitosan biocomposites via in-vitro results at bioactive interfaces for anti-infective targeted drug delivery and trauma management.
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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".