Bioactivation of Konjac Glucomannan Films by Tannic Acid and Gluconolactone Addition
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Wound healing is a dynamic process that requires an optimal extracellular environment, as well as an accurate synchronization between various cell types. Over the past few years, great efforts have been devoted to developing novel approaches for treating and managing burn injuries, sepsis, and chronic or accidental skin injuries. Multifunctional smart-polymer-based dressings represent a promising approach to support natural healing and address several problems plaguing partially healed injuries, including severe inflammation, scarring, and wound infection. Naturally derived compounds offer unique advantages such as minimal toxicity, cost-effectiveness, and outstanding biocompatibility along with potential anti-inflammatory and antimicrobial activity. Herein, the main driving idea of the work was the design and development of konjac glucomannan d -glucono-1,5-lactone (KG) films bioactivated by tannic acid and d -glucono-1,5-lactone (GL) addition. Our analysis, using attenuated total reflectance-Fourier transform infrared, atomic force microscopy, and surface energy measurements demonstrated that tannic acid (TA) clearly interacted with the KG matrix, acting as its cross-linker, whereas GL was embedded within the polymer structure. All developed films maintained a moist environment, which represents a pivotal property for wound dressing. Hemocompatibility experiments showed that all tested films exhibited no hemolytic impact on human erythrocytes. Moreover, the presence of TA and GL enhanced the metabolic and energetic activity in human dermal fibroblasts, as indicated by the MTT assay, showing results exceeding 150%. Finally, all films demonstrated high antibacterial properties as they significantly reduced the multiplication rate of both Staphylococcus aureus and Escherichia coli in bacterial broth and created the inhibition zones for S. aureus in agar plates. These remarkable outcomes make the KG/TA/GL film promising candidates for wound healing applications.
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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) | 0.000 | 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".