Hybrid Porphyrin-Polymeric Materials and their Amazing Applications: A Review
Bibliographic record
Abstract
Background: Bacterial cellulose (BC) is a highly biocompatible biopolymer valued for its unique nanofibrillar structure, excellent mechanical strength, high water retention, and intrinsic non-toxicity, making it particularly suitable for biomedical applications.Methods: A three-dimensional (3D) composite scaffold composed of BC and fish collagen peptides (FCP) was fabricated via a one-step in situ biosynthesis method by incorporating optimized concentrations of FCP (0.1% and 0.5%) into the BC culture medium during bacterial fermentation. The structural integrity and surface morphology of the scaffolds were examined using field emission scanning electron microscopy (FE-SEM). The biological functionality of the scaffolds was further evaluated by culturing HT-22 neuronal cells (mouse hippocampal origin) on both pristine BC and BC-FCP scaffolds. Cell adhesion, morphology, and neurite outgrowth were evaluated using fluorescence microscopy after 2 days of incubation.Results: The BC-FCP composite scaffolds demonstrated superior microstructural integrity and biological performance compared to pristine BC. Field-emission scanning electron microscopy revealed a denser and more uniform nanofibrous architecture in BC-FCP scaffolds, confirming the successful incorporation and uniform distribution of collagen peptides within the cellulose matrix. HT-22 neuronal cells cultured on these scaffolds showed markedly enhanced adhesion, spreading, and neurite outgrowth, particularly at the higher FCP concentrations (0.5%) demonstrating the neuro-supportive capability of the composite system.Conclusion: The BC-FCP composite scaffolds significantly enhance neuronal adhesion and growth, making them promising candidates for neural tissue engineering and regenerative applications.Keywords:Bacterial cellulose, Fish collagen peptides, 3D scaffolds, Biocompatibility, Neuronal outgrowth
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".